System

Abstract

A system is provided.SOLUTION: A system comprising: means for detecting a heart rate and a body temperature in real time by using a wearable device worn by a worker; means for transmitting the detected heart rate and body temperature to a server; means for analyzing the detected AI by using generated information and evaluating a health condition and a tiredness level of the worker in the server; and means for notifying the worker and a manager when an abnormality is detected based on an analysis result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] It is important for workers to work safely while maintaining an appropriate state of health in the workplace. However, at many current workplaces, there are limited means of understanding workers' health status and fatigue levels in real time, making it difficult to detect accidents or health problems early. In particular, there is a demand for systems that can monitor biometric information such as heart rate and body temperature in real time and respond immediately if an abnormality is detected. This will improve safety during work and minimize health risks for workers. [Means for solving the problem]

[0005] The system of the present invention provides a means for detecting heart rate and body temperature in real time using a wearable device worn by a worker. The wearable device transmits the detected heart rate and body temperature data to a server. The server then analyzes the detected data using generative AI to evaluate the worker's health condition and fatigue level. If an abnormality is detected based on the analysis results, a means for notifying the worker and manager is provided. This system enables real-time monitoring of the worker's health condition while working and prompt appropriate action. Furthermore, by transmitting the detected data to the server at regular intervals, continuous and real-time monitoring is achieved. Furthermore, if the analysis results exceed a preset reference value, an abnormality is detected, enabling prompt and appropriate action to be taken.

[0006] A "wearable device" is an electronic device that can be worn by a worker and is used to detect biometric information such as heart rate and body temperature.

[0007] "Heart rate" refers to the number of times the heart beats within a certain period of time, and is an important indicator of health status.

[0008] "Body temperature" is biological information that indicates the internal temperature of a worker's body, and is an index used to evaluate the worker's health condition.

[0009] "Real-time" means that data is processed as soon as it is generated, and refers to a state in which the data is reflected with almost no delay.

[0010] A "server" is a computer system that receives, stores, and analyzes data over a network.

[0011] "Generative AI" is an artificial intelligence system with algorithms that analyze received data and evaluate the worker's health condition and fatigue level.

[0012] "Analysis" is the process of processing collected data and extracting meaningful information.

[0013] "Evaluation" refers to determining the worker's health condition and fatigue level based on the analyzed data.

[0014] "Abnormal" refers to a condition in which the analysis results exceed a predetermined standard value, which may pose a health risk to workers.

[0015] "Notification" refers to the act of conveying warnings and necessary information to workers and managers when an abnormality is detected.

[0016] A "supervisor" is a person who is responsible for overseeing safety at the workplace and managing the health of workers. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention provides a system for improving safety during work by having a worker wear a wearable device and monitoring biological information such as heart rate and body temperature in real time. Specific embodiments are described below.

[0039] System configuration

[0040] 1. Wearable devices

[0041] Workers wear a wearable device that contains sensors that detect heart rate and body temperature in real time.

[0042] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[0043] 2. Dedicated app and device

[0044] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[0045] The app has the function of receiving biometric information sent from the wearable device and sending it to a server.

[0046] 3. Server and Generative AI

[0047] The server receives the biometric information sent through a dedicated app.

[0048] The generated AI inside the server analyzes the received biometric information and evaluates the worker's health condition and fatigue level.

[0049] If an abnormality is detected based on the analysis results, the server sends a notification to the worker and the administrator.

[0050] Explanation of program processing

[0051] 1. Data collection and transmission

[0052] Terminal (wearable device)

[0053] The wearable device detects the worker's heart rate and body temperature in real time.

[0054] The detected data is sent to a dedicated app at regular intervals (e.g., every second).

[0055] 2. Data Receipt and Storage

[0056] server

[0057] The server stores the biometric information received via a dedicated app in a database.

[0058] 3. Data Analysis

[0059] server

[0060] The server's generated AI analyzes the worker's health condition and fatigue level in real time based on the received data.

[0061] The analysis includes comparing patterns of heart rate and body temperature with baseline values.

[0062] 4. Anomaly Detection and Notification

[0063] server

[0064] If the analysis results exceed a preset threshold, the server detects an anomaly.

[0065] If an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the terminals of the worker and manager.

[0066] Specific examples

[0067] Worker C's case

[0068] Data collection

[0069] Worker C puts on the wearable device at the construction site and begins work.

[0070] The device detects the heart rate (e.g., 115 beats per minute) and body temperature (e.g., 38.1°C) and sends the data to a server via a dedicated app.

[0071] Data analysis and notification

[0072] server

[0073] The server analyzes the data received from the dedicated app using a generation AI.

[0074] Based on the analysis results, it is detected whether the heart rate and body temperature exceed the set standard values ​​(e.g., heart rate 60-100 beats / min, body temperature 36.0-37.5°C).

[0075] The server generates an abnormality notification message and sends it to the terminals of worker C and the administrator.

[0076] correspondence

[0077] User (Worker C)

[0078] Worker C receives an abnormality notification via the app, stops working, and monitors his / her health.

[0079] Contact a medical institution if necessary.

[0080] administrator

[0081] The manager checks the abnormality notification in the management system and instructs Worker C to take a break and arranges for medical consultation.

[0082] This system allows for real-time monitoring of workers' health conditions and enables rapid response in the event of an abnormality, thereby reducing the risk of accidents during work and ensuring the health and safety of workers.

[0083] The processing flow will be explained below.

[0084] Step 1:

[0085] The user wears the wearable device and performs initial setup using a device with a dedicated app installed. Specifically, the user logs in to their account, sets emergency contacts, and sets alert thresholds.

[0086] Step 2:

[0087] The terminal (wearable device) detects the worker's heart rate and body temperature in real time. This biometric data is recorded periodically (e.g., every second) in its internal memory. The recorded data is then transmitted via wireless communication such as Bluetooth to a terminal with a dedicated app installed.

[0088] Step 3:

[0089] The terminal (a smartphone or tablet with a dedicated app installed) then transmits the biometric data received from the wearable device to a server via the internet using a secure communication protocol.

[0090] Step 4:

[0091] The server receives the biometric data sent from the device, stores the data in a database, and uses it for subsequent analysis.

[0092] Step 5:

[0093] The server uses generative AI to analyze the incoming biometric data in real time, including evaluating the worker's heart rate and body temperature fluctuation patterns and comparing them with pre-set baseline values.

[0094] Step 6:

[0095] The server evaluates the worker's health condition and fatigue level based on the analysis results, and if an abnormality exceeding the reference value is detected, the server marks it as abnormal.

[0096] Step 7:

[0097] If an anomaly is detected, the server immediately generates an anomaly notification message, which includes the specific details of the anomaly, the biometric data causing the anomaly, and recommended countermeasures.

[0098] Step 8:

[0099] The server sends an abnormality notification message to the devices of the user (worker) and administrator. Notifications are sent in multiple ways, including push notifications and email, to enable a quick response.

[0100] Step 9:

[0101] The user (worker) receives an abnormality notification via their device. The user checks the notification and takes immediate action, such as temporarily suspending work. If necessary, they may drink fluids, take a break, or contact a medical institution.

[0102] Step 10:

[0103] The administrator receives an abnormality notification from the server and checks it on the management screen. The administrator monitors the worker's condition and, if necessary, gives specific instructions to the worker, such as instructing them to take a break or seek medical advice.

[0104] This sequence of steps will realize a system that monitors workers' biological information in real time and takes prompt and appropriate action in the event of an abnormality, thereby significantly improving the safety of the work environment and minimizing health risks to workers.

[0105] Example 1

[0106] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0107] While it is important to monitor the health status of workers in real time at workplaces, conventional systems have had difficulty efficiently and reliably monitoring workers' vital signs, such as heart rate and body temperature, making it difficult to respond quickly when an abnormality occurs. Additionally, on-site managers have had few means to receive information in real time and issue instructions for a prompt response.

[0108] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0109] In this invention, the server includes means for detecting the heart rate and body temperature in real time using a wearable device worn by the worker, means for transmitting the detected heart rate and body temperature data to the server via a terminal, means for analyzing the detected data using a generative AI model in the server and evaluating the worker's health condition and fatigue level, and means for notifying the worker and a manager if an abnormality is detected based on the analysis results. This makes it possible to monitor the worker's health condition in real time and to take prompt action if an abnormality occurs.

[0110] A "wearable device" is an electronic device worn by a worker to detect heart rate and body temperature in real time.

[0111] A "terminal" is an electronic device that has the function of receiving data sent from a wearable device and sending it to a server.

[0112] A "server" is a computer system that receives data sent from a terminal and analyzes it using a generative AI model.

[0113] The "generative AI model" is an algorithm that uses artificial intelligence to analyze collected biometric information and evaluate the worker's health condition and fatigue level.

[0114] "Detecting signs" refers to detecting abnormalities in a worker's heart rate or body temperature based on data analyzed by the generative AI model.

[0115] The "abnormality notification means" is a system and method for issuing an alert to workers and administrators when the server detects an abnormality.

[0116] The "analysis results" are the evaluation results regarding health status and fatigue level output by the generative AI model after processing the input data.

[0117] "Reference values" are standard heart rate and body temperature values ​​that are used for comparison when assessing a worker's health condition.

[0118] The present invention provides a system for improving safety during work by having a worker wear a wearable device and monitoring biological information such as heart rate and body temperature in real time. Specific embodiments are described below.

[0119] System configuration

[0120] 1. Wearable devices

[0121] Workers wear a wearable device equipped with sensors that detect heart rate and body temperature in real time.

[0122] The device uses wireless communication methods such as Bluetooth to communicate data with devices on which a dedicated app is installed.

[0123] 2. Dedicated app and device

[0124] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[0125] The app has the ability to receive biometric information sent from wearable devices, such as Apple's iPhone or Samsung's Galaxy series, and send it to a server.

[0126] 3. Server and Generative AI

[0127] The server receives and stores the biometric information sent via a dedicated app. The server can be hosted on Amazon Web Services (AWS) or Google Cloud Platform (GCP).

[0128] The generative AI inside the server analyzes the received biometric information and evaluates the worker's health condition and fatigue level. TensorFlow and PyTorch can be used for the generative AI model.

[0129] The server has a means for sending a notification to the worker and the administrator if an abnormality is detected based on the analysis results.

[0130] Specific examples

[0131] Data collection

[0132] Worker examples

[0133] Workers put on the wearable device at the construction site and begin work.

[0134] The wearable device detects heart rate (e.g., 115 beats per minute) and body temperature (e.g., 38.1°C), and transmits the data via Bluetooth to a device with a dedicated app installed.

[0135] Data transmission and analysis

[0136] Terminal and server operation

[0137] The terminal receives the detected biometric information and periodically transmits it to the server.

[0138] The server analyzes the transmitted data using a generative AI model to determine whether the heart rate and body temperature exceed the reference values ​​(e.g., heart rate 60-100 beats per minute, body temperature 36.0-37.5°C).

[0139] If an abnormality is detected as a result of the analysis, the server generates an abnormality notification message and immediately sends it to the terminals of the worker and manager.

[0140] Response Procedure

[0141] Worker

[0142] Workers will receive notifications of abnormalities via a dedicated app, stop working in a safe place, and monitor their health.

[0143] Contact a medical institution if necessary.

[0144] administrator

[0145] The manager checks the abnormality notification on the management system and instructs the worker to take a break or arranges for medical consultation.

[0146] Example prompts for generative AI models

[0147] Example 1: How can I send a notification when a worker's heart rate exceeds a threshold?

[0148] Example 2: Walk us through the process of creating an AI model using TensorFlow to monitor heart rate and body temperature in real time.

[0149] Example 3: How can you design a database to monitor the health status of workers?

[0150] By utilizing this system, it is possible to monitor the health status of workers in real time and respond quickly to any abnormalities, thereby reducing the risk of accidents during work and ensuring the health and safety of workers.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] System program processing flow

[0153] Step 1:

[0154] Data collection

[0155] Terminal (wearable device)

[0156] The wearable device detects the worker's heart rate and body temperature in real time.

[0157] Input: Worker's biological information (heart rate, body temperature)

[0158] Data processing: Measuring heart rate and body temperature using sensors

[0159] Output: Measured vital signs (heart rate 115 beats / min, body temperature 38.1°C)

[0160] Step 2:

[0161] Data transmission

[0162] Terminal

[0163] Biometric information obtained from the wearable device is sent to a dedicated app via Bluetooth.

[0164] Input: Measured biological information (heart rate 115 beats / min, body temperature 38.1℃)

[0165] Data processing: Preparation for data transmission via Bluetooth communication

[0166] Output: Biometric information packet for transmission

[0167] Step 3:

[0168] Data reception and temporary storage

[0169] Terminal

[0170] The terminal receives biometric information sent from the wearable device through a dedicated app.

[0171] The app temporarily stores the received data in the device's memory.

[0172] Input: Biometric packet for transmission

[0173] Data processing: Data reception and temporary storage

[0174] Output: Temporarily stored vital signs (heart rate 115 beats / min, body temperature 38.1℃)

[0175] Step 4:

[0176] Data transmission and persistent storage

[0177] Terminal

[0178] The terminal transmits data to the server at regular intervals (e.g., every second).

[0179] Input: Temporarily stored biometric information

[0180] Data processing: creating data packets and preparing them for transmission

[0181] Output: Biometric information sent to the server

[0182] server

[0183] The server receives the biometric information sent via a dedicated app.

[0184] The received data is stored in a database. For example, "Heart rate 115 beats per minute, body temperature 38.1°C" is recorded in a MySQL database.

[0185] Input: Biometric information sent to the server

[0186] Data processing: receiving data and inserting it into the database

[0187] Output: Biometric information stored in a database

[0188] Step 5:

[0189] Data analysis

[0190] server

[0191] The server analyzes the received data using a generative AI model.

[0192] The generative AI model compares heart rate and body temperature with baseline values ​​to assess the worker's health and fatigue level in real time.

[0193] Input: Biometric information stored in the database

[0194] Data processing: Analysis using generative AI models (pattern recognition, comparison with reference values)

[0195] Output: Health status and fatigue evaluation results

[0196] Step 6:

[0197] Anomaly Detection

[0198] server

[0199] If the analysis data exceeds a preset reference value, the server detects an anomaly.

[0200] Input: Health status and fatigue assessment results

[0201] Data processing: Abnormality detection by comparing with reference values

[0202] Output: Anomaly detection result (abnormal / no abnormality)

[0203] Step 7:

[0204] Abnormality notification and response

[0205] server

[0206] When an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the worker and the administrator.

[0207] Input: Anomaly detection results

[0208] Data processing: Generation of abnormality notification messages

[0209] Output: Sending an error notification message

[0210] User (operator, administrator)

[0211] Workers will receive notifications of abnormalities via a dedicated app, stop working in a safe place, and monitor their health.

[0212] The manager checks the abnormality notification on the management system and instructs the worker to take a break or arranges for medical consultation.

[0213] (Application example 1)

[0214] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0215] Currently, many factories are using robots, but if the operator's health condition suddenly changes while the robot is operating, there is a risk that the robot will continue working without noticing. This situation not only impairs the operator's health, but can also have a negative impact on work efficiency and safety, so there is a need for a system that can monitor the operator's health condition in real time and respond immediately if an abnormality occurs.

[0216] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0217] In this invention, the server includes a means for transmitting detected heart rate and body temperature data to the server, a means for analyzing the detected data using a generative AI and evaluating the worker's health condition and fatigue level, a means for notifying the worker and manager if an abnormality is detected based on the analysis results, and a means for temporarily suspending machine operation when an abnormality is detected. This makes it possible to understand the operator's health condition in real time and respond immediately when an abnormality occurs, thereby improving safety and efficiency during work.

[0218] A "wearable device" is a device worn by a worker that detects biometric information such as heart rate and body temperature in real time.

[0219] The "heart rate" is a type of biological information that indicates the number of times the heart beats per unit time.

[0220] "Body temperature" is biological information that indicates the internal body temperature of the worker.

[0221] A "server" is a central device that receives and analyzes biometric information sent from a wearable device.

[0222] "Generative AI" is an artificial intelligence model that analyzes received biometric information and evaluates the worker's health condition and fatigue level.

[0223] "Abnormal" refers to a state in which the analysis results exceed a preset reference value.

[0224] An "abnormality notification" is an alarm or message sent to a worker or manager when an abnormality is detected based on the analysis results.

[0225] A "means for temporarily suspending robot operation" is a mechanism or system for temporarily halting machine operation when an abnormality is detected.

[0226] "Worker" refers to a person who wears a wearable device while performing their work.

[0227] The "manager" is the person in charge of monitoring the health status of workers and taking action if any abnormalities occur.

[0228] The present invention improves safety during work by using a system including a wearable device worn by a worker, a transmission means, an analysis means, a notification means, and a means for temporarily suspending machine operation. Detailed embodiments for implementing the present invention will be described below.

[0229] System Configuration

[0230] 1. Wearable devices

[0231] Workers wear a wearable device that contains sensors to detect heart rate and body temperature in real time.

[0232] Wearable devices communicate with devices such as smartphones and tablets using wireless communication technologies such as Bluetooth.

[0233] 2. Terminals and Edge Devices

[0234] Users use a smartphone or tablet with a dedicated app installed.

[0235] The dedicated app has the function of receiving biometric information sent from the wearable device and sending it to a server.

[0236] 3. Server and Generative AI Model

[0237] The server receives the biometric information sent from the wearable device and stores it in a database.

[0238] The generative AI model analyzes the data sent to the server and evaluates the worker's health condition and fatigue level.

[0239] The analysis includes comparing patterns of heart rate and body temperature with baseline values.

[0240] 4. Anomaly Detection and Notification

[0241] Based on the analysis results, if an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the worker and administrator.

[0242] Upon receiving the notification, the manager will instruct appropriate action to ensure the safety of the workers.

[0243] 5. Robot stops working

[0244] When an abnormality is detected, the server sends an instruction to temporarily suspend the robot's operation.

[0245] This function immediately stops machine operation if an abnormality occurs in the worker's health condition, reducing the risk of accidents.

[0246] Program processing explanation

[0247] In implementing the present invention, the following hardware and software are used.

[0248] Hardware:

[0249] Wearable devices: Typical smartwatches (e.g., Fitbit, Apple Watch).

[0250] Server: A cloud-based server (e.g., AWS Lambda, Google Cloud).

[0251] Robot: An automated device used in factories.

[0252] software:

[0253] Programming language: Python

[0254] AI model: A generative AI model using TensorFlow and PyTorch that uses machine learning techniques to analyze biometric data.

[0255] Database: Manage biometric data using MySQL or MongoDB.

[0256] Communication protocol: Data communication between devices is performed using REST API.

[0257] Specific examples

[0258] As an example, let's take Operator A, who works in a factory. Operator A wears a wearable device while working. If the device detects a heart rate of 120 beats per minute and a body temperature of 38.5°C, the server receives this data and analyzes it using a generative AI model. As a result, it is determined that these values ​​exceed the standard values, and an abnormality notification is issued. The server immediately pauses the robot's operation and notifies Operator A and the administrator of the abnormality. This allows Operator A to quickly check his health condition and, if necessary, contact a medical institution.

[0259] Example of an input prompt for a generative AI model:

[0260] Design a program to analyze heart rate and body temperature data collected in real time and generate an abnormality notification if the data exceeds the threshold. Also, add a method to pause the operation of the factory robot based on the abnormality notification. The threshold for heart rate is 60-100 beats per minute, and the threshold for body temperature is 36.0-37.5°C.

[0261]

[0262] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0263] Step 1:

[0264] Data collection

[0265] Before starting work, the user puts on the wearable device, which detects the user's heart rate and body temperature in real time. These values ​​are measured by sensors and temporarily stored inside the wearable device.

[0266] Input: Heart rate and temperature biometric data

[0267] Output: Temporary data in the wearable device

[0268] How it works: Heart rate is detected using an optical sensor, and body temperature is measured using a built-in body temperature sensor.

[0269] Step 2:

[0270] Data transmission

[0271] The wearable device periodically collects biometric data from sensors and transmits it to the user's smartphone or tablet via wireless communication such as Bluetooth. A dedicated app receives the data and transfers it to a server.

[0272] Input: Temporary data in the wearable device

[0273] Output: Data displayed on the dedicated app and sent to the server

[0274] What it does: The device activates its Bluetooth module, pairs with the smartphone, and transmits data. A dedicated app receives this data in real time and forwards it to the server via an HTTP request.

[0275] Step 3:

[0276] analysis

[0277] The server stores the received data in a database, then analyzes the biometric data using a generative AI model. Specifically, it compares heart rate and body temperature values ​​with baseline values ​​to determine whether there are any abnormalities.

[0278] Input: Biometric data received by the server

[0279] Output: Analysis result (normal / abnormal)

[0280] Specific operations: Connect to a database and save data to storage. A generative AI model is used to analyze the saved data. This model is built using TensorFlow and PyTorch and performs calculations to detect outliers.

[0281] Step 4:

[0282] Anomaly Detection and Notification

[0283] If an abnormality is detected, the server generates an abnormality notification message based on the analysis results of the AI. This message is sent to workers and managers via a dedicated app.

[0284] Input: Analysis results of the generative AI model

[0285] Output: Error notification message

[0286] Specific operation: If an abnormal value is detected, the server generates a notification message and sends a push notification to the worker's smartphone or the manager's device.

[0287] Step 5:

[0288] Robot stops working

[0289] If the server detects an abnormality, it sends an instruction to the robots in the factory to stop operating. This instruction is transmitted to the robots via the control system.

[0290] Input: Analysis results of the generative AI model

[0291] Output: Robot stops moving

[0292] Specific operation: The server sends a stop command to the robot control system via an HTTP request, etc. The control system receives this command and stops the robot's operation.

[0293] Example prompt sentence:

[0294] Design a program to analyze heart rate and body temperature data collected in real time and generate an abnormality notification if the data exceeds the threshold. Also, add a method to pause the operation of the factory robot based on the abnormality notification. The threshold for heart rate is 60-100 beats per minute, and the threshold for body temperature is 36.0-37.5°C.

[0295] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0296] The present invention is a system that uses a wearable device worn by a worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work. Specific embodiments are described below.

[0297] System configuration

[0298] 1. Wearable devices

[0299] Workers wear a wearable device that includes sensors to detect heart rate and body temperature in real time, as well as a microphone and camera for emotion recognition.

[0300] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[0301] 2. Dedicated app and device

[0302] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[0303] The app has the function of receiving biometric information and emotional data sent from the wearable device and sending it to a server.

[0304] 3. Server, Generative AI and Emotion Engine

[0305] The server receives the biometric information and emotional data sent through a dedicated app.

[0306] The generated AI inside the server analyzes the received biometric data and evaluates the worker's health condition and fatigue level.

[0307] The emotion engine analyzes the emotion data received from the wearable device and assesses the worker's emotional state.

[0308] If an abnormality is detected based on the analysis results, the server sends a notification to the worker and the administrator.

[0309] Explanation of program processing

[0310] 1. Data collection and transmission

[0311] Terminal (wearable device)

[0312] The wearable device detects the worker's heart rate and body temperature in real time, and also uses a microphone and camera to collect voice and facial expression data.

[0313] The detected biometric and emotional data is sent to a dedicated app at regular intervals (e.g., every second).

[0314] 2. Data Receipt and Storage

[0315] server

[0316] The server stores the biometric and emotional data received via a dedicated app in a database.

[0317] 3. Data Analysis

[0318] server

[0319] The server's generated AI analyzes the worker's health condition and fatigue level in real time based on the received biometric data.

[0320] The emotion engine analyzes the received emotion data and evaluates the worker's emotional state, which includes changes in tone of voice and facial expressions.

[0321] 4. Overall evaluation and anomaly detection

[0322] server

[0323] The server makes a comprehensive judgment based on the results of biometric data analysis by the generation AI and the emotional state evaluation results by the emotion engine.

[0324] Based on the overall evaluation, if an abnormality exceeding the reference value is detected, the server marks it as abnormal.

[0325] 5. Abnormality notification and response suggestions

[0326] server

[0327] If an anomaly is detected, the server immediately generates an anomaly notification message, which includes the specific details of the anomaly, the data causing the anomaly, and recommended actions to take.

[0328] The server sends an abnormality notification message to the terminals of the user (worker) and the administrator.

[0329] Specific examples

[0330] Worker D's case

[0331] Data collection

[0332] Worker D puts on the wearable device at the construction site and begins work.

[0333] The device detects elevated heart rate (e.g., 120 beats per minute), body temperature (e.g., 37.8°C), and voice tone, and transmits this data to a server via a dedicated app.

[0334] Data analysis and abnormality notification

[0335] server

[0336] The server uses AI to analyze and check the data received from the dedicated app, detecting whether the heart rate and body temperature exceed the standard values ​​(heart rate 60-100 beats per minute, body temperature 36.0-37.5°C).

[0337] The emotion engine analyzes the tone of voice as being different from normal and assesses that the worker is feeling stressed.

[0338] The server integrates these analysis results and determines that there is something wrong with worker D's health condition.

[0339] The server generates an abnormality notification message and sends it to the terminals of worker D and the administrator.

[0340] correspondence

[0341] User (Worker D)

[0342] Worker D receives an abnormality notification via the app, stops working, takes a break, and contacts a medical institution if necessary.

[0343] administrator

[0344] The administrator checks the abnormality notification from the server on the management screen, reviews the status of worker D, and, if necessary, issues instructions for a break or arranges for him to be examined at a medical institution.

[0345] In this way, a system can be built that monitors workers' biometric information and emotional state in real time and responds quickly in the event of an abnormality, significantly improving the safety of the work environment and minimizing health risks for workers.

[0346] The processing flow will be explained below.

[0347] Step 1:

[0348] The user wears the wearable device and performs initial setup using a device with a dedicated app installed, including logging in to their account, setting emergency contacts, and setting alert thresholds.

[0349] Step 2:

[0350] The terminal (wearable device) detects the worker's heart rate and body temperature in real time, and also uses the microphone and camera built into the wearable device to collect emotional data such as voice and facial expressions in real time.

[0351] Step 3:

[0352] The terminal (wearable device) transmits the collected heart rate, body temperature, and emotional data to a dedicated app at regular intervals (e.g., every second).

[0353] Step 4:

[0354] The device (a smartphone or tablet with a dedicated app installed) then transmits the received biometric and emotional data to a server via the Internet.

[0355] Step 5:

[0356] The server receives the biometric data and emotion data sent from the terminal and stores them in a database.

[0357] Step 6:

[0358] The server's generation AI analyzes the received biometric data and evaluates the worker's health condition and fatigue level in real time, while the emotion engine analyzes the received emotional data and evaluates the worker's emotional state.

[0359] Step 7:

[0360] Based on the analysis results, the server makes a comprehensive judgment of the heart rate, body temperature, and emotional data, and if it detects any abnormalities that exceed the standard values, it marks them as abnormal.

[0361] Step 8:

[0362] If the server detects an anomaly that exceeds the threshold, it immediately generates an anomaly notification message that includes the nature of the anomaly, the data that caused it, and a recommended course of action.

[0363] Step 9:

[0364] The server sends an abnormality notification message to the devices of the user (worker) and administrator. Notifications are sent in multiple ways, including push notifications and email, to enable a quick response.

[0365] Step 10:

[0366] The user (worker) receives an abnormality notification via their terminal. After checking the notification, the user temporarily stops their work and takes necessary measures, such as drinking water, taking a break, or contacting a medical institution.

[0367] Step 11:

[0368] The administrator checks the abnormality notification from the server on the management screen and monitors the worker's condition. If necessary, the administrator can instruct workers to take a break, seek medical advice, or take other measures.

[0369] In this way, the system of the present invention monitors the worker's biometric information and emotional state in real time, and takes prompt and appropriate action in the event of an abnormality, thereby ensuring the safety and health of the worker.

[0370] Example 2

[0371] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0372] Conventional technologies exist that monitor only a worker's biometric information, such as heart rate and body temperature, in real time. However, these systems are unable to adequately assess the worker's psychological stress and fatigue due to emotional changes, making it difficult to fully grasp the worker's overall health status. As a result, there are limitations to improving safety and health management during work. Furthermore, the difficulty of responding quickly when an abnormality occurs can have a negative impact on work efficiency and safety.

[0373] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting emotion data using a wearable device that detects heart rate and body temperature in real time, means for evaluating health status and fatigue level using a generation AI, means for evaluating the emotional state using an emotion engine, and means for notifying the worker and manager when an abnormality is detected. This allows for a comprehensive evaluation of the worker's biometric information and emotional state, and enables rapid response when an abnormality is detected in real time.

[0374] A "worker" is a person who works at a work site while wearing a wearable device.

[0375] A "wearable device" is a device that can be worn by a worker and is equipped with sensors and input devices for detecting biometric and emotional data such as heart rate, body temperature, voice, and facial expressions in real time.

[0376] "Heart rate" refers to data indicating the number of times the worker's heart beats per unit time.

[0377] "Body temperature" refers to data indicating the worker's body temperature.

[0378] "Emotion data" is data obtained from voice and facial expressions, and includes information for evaluating the emotional state of the worker.

[0379] "Server" is a general term for a computer system that receives, analyzes, and stores biometric and emotional data sent through a dedicated app.

[0380] "Generative AI" is an artificial intelligence technology that analyzes received biometric data and evaluates the health status and fatigue level of workers.

[0381] An "emotion engine" is an algorithm or software that analyzes emotional data such as voice and facial expressions to evaluate the emotional state of a worker.

[0382] "Analysis results" refer to the evaluation results of the worker's health and emotional state obtained by the generative AI and emotion engine.

[0383] The "notification means" refers to a function or device that notifies the worker or manager of an abnormality when it is detected based on the analysis results.

[0384] "Supervisor" refers to the person in charge of managing the safety and health of workers at the work site.

[0385] This invention is a system that uses a wearable device worn by the worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work.

[0386] System configuration

[0387] 1. Wearable devices

[0388] Workers wear a wearable device that includes sensors to detect heart rate and body temperature in real time, as well as a microphone and camera for emotion recognition. The wearable device communicates with a terminal on which a dedicated app is installed using wireless communication methods such as Bluetooth.

[0389] 2. Dedicated app and device

[0390] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed. The app has the function of receiving biometric and emotional data sent from the wearable device and sending it to a server.

[0391] 3. Server, Generative AI and Emotion Engine

[0392] The server receives biometric information and emotional data sent through a dedicated app. The generation AI inside the server analyzes the received biometric data and evaluates the worker's health condition and fatigue level. The emotion engine analyzes the emotional data received from the wearable device and evaluates the worker's emotional state. If the server detects an abnormality based on the analysis results, it sends a notification to the worker and manager.

[0393] Specific actions

[0394] Example: Worker D's case

[0395] Data collection

[0396] Worker D puts on the wearable device at the construction site and begins work. The device detects rising heart rate (e.g., 120 beats per minute), body temperature (e.g., 37.8°C), and voice tone. This data is sent to the server via a dedicated app.

[0397] Data analysis and abnormality notification

[0398] The server uses the generation AI to analyze the heart rate and body temperature. It confirms that they exceed the standard values ​​(a heart rate of 60-100 beats per minute and a body temperature of 36.0-37.5°C are standard values). The emotion engine analyzes the tone of the voice data received and determines that the worker is in a state of stress. The server combines these results and determines that worker D's health and emotional state are abnormal. It creates an abnormality notification message and immediately sends it to the devices of worker D and the manager, including specific details of the abnormality, the cause, and countermeasures.

[0399] correspondence

[0400] Worker D receives the abnormality notification via the app, stops working, and takes a break. If necessary, he or she contacts a medical institution. The administrator receives the abnormality notification from the server and checks the status of Worker D. The administrator instructs the appropriate countermeasures and ensures the safety of Worker D.

[0401] An example of a prompt for this system is:

[0402] This system monitors workers' health and emotional state in real time using heart rate, body temperature, voice tone, and facial expression data obtained from wearable devices worn by workers. If an abnormality is detected, such as a worker feeling stressed or their body temperature exceeding the normal range, a notification is sent immediately. In this way, the system improves the safety of the work environment and minimizes health risks to workers.

[0403] In this way, a system can be built that monitors workers' biometric information and emotional state in real time and responds quickly in the event of an abnormality, significantly improving the safety of the work environment and minimizing health risks for workers.

[0404] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0405] Step 1: Data collection and transmission

[0406] Terminal (wearable device)

[0407] The wearable device measures the worker's heart rate and body temperature in real time. It also uses a built-in microphone and camera to capture the worker's voice and facial expressions. The collected biometric and emotional data is sent via Bluetooth to a device (smartphone or tablet) with a dedicated app installed at regular intervals (e.g., every second).

[0408] Input: Heart rate, body temperature, voice data, facial expression data

[0409] Data processing / calculation: Measurement by sensor, recording by microphone and camera

[0410] Output: Biometric and emotional data sent to a dedicated app

[0411] Step 2: Receiving and storing data

[0412] server

[0413] The server receives biometric and emotional data sent from the dedicated app in real time. The received data is structured and stored in a database. Each piece of data is assigned the worker's ID and a timestamp.

[0414] Input: Biometric data and emotional data sent from a dedicated app

[0415] Data processing / calculation: Receiving data, assigning ID and time stamp

[0416] Output: Biometric and emotional data stored in a database

[0417] Step 3: Data analysis

[0418] server

[0419] The generative AI analyzes received biometric data such as heart rate and body temperature to assess the worker's health and fatigue level. The emotion engine analyzes collected voice tone and facial expression data to assess the worker's emotional state. Each analysis result is stored in a database.

[0420] Input: Heart rate, body temperature, voice tone, and facial expression data obtained from the database

[0421] Data processing / calculation: Analysis of health status and fatigue level using generative AI, analysis of emotional state using emotion engine

[0422] Output: Analysis results (health status, fatigue level, emotional state)

[0423] Step 4: Overall evaluation and anomaly detection

[0424] server

[0425] The server combines the results of the biometric data analysis by the generative AI with the results of the emotional state evaluation by the emotion engine to make a comprehensive evaluation. If the analysis results exceed a preset threshold, the system will mark the person as abnormal.

[0426] Input: Analysis results of generative AI and emotion engine

[0427] Data processing / calculation: Integration of analysis results, comparison with reference values

[0428] Output: Overall evaluation result marked as abnormal (exceeding the standard value)

[0429] Step 5: Notification of abnormalities and suggested actions

[0430] server

[0431] When an abnormality is detected in the overall evaluation, the server generates an abnormality notification message. This message contains the specific details of the abnormality, the data that caused it, and recommended countermeasures. The abnormality notification message is sent to the terminals of the worker and the manager.

[0432] Input: Overall evaluation result marked as abnormal

[0433] Data processing / calculation: Generation of error notification messages

[0434] Output: Anomaly notification message sent to the terminals of the worker and manager

[0435] In this way, specific data processing and calculations are performed at each step, making it possible to monitor the worker's health and emotional state in real time and respond quickly in the event of an abnormality.

[0436] (Application example 2)

[0437] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0438] In conventional work environments, systems exist that manage the health status of workers by continuously monitoring biometric information such as heart rate and body temperature, but these systems often overlook health risks and declines in work efficiency due to changes in emotional state. Furthermore, there is a lack of real-time responses and suggestions for appropriate countermeasures when abnormalities are detected, which has resulted in insufficient improvements in worker safety and productivity. Therefore, the present invention aims to provide a system that comprehensively monitors a worker's biometric information and emotional state and suggests appropriate countermeasures when an abnormality is detected.

[0439] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0440] In this invention, the server includes means for detecting the heart rate and body temperature in real time using a wearable device worn by the worker, means for transmitting the detected heart rate and body temperature data to the server, means for analyzing the detected data using a generative AI and evaluating the worker's health condition and fatigue level, means for notifying the worker and manager when an abnormality is detected, means for collecting and analyzing voice and facial expression data to evaluate the worker's emotional state using an emotion engine, and means for the robot to notify the worker of the abnormality and suggest measures to take, such as taking a break or seeing a doctor. This makes it possible to comprehensively monitor and analyze the worker's health and emotional state, and to take prompt and appropriate action when an abnormality occurs.

[0441] A "wearable device" is a device worn by a worker that detects biometric information such as heart rate and body temperature in real time.

[0442] "Heart rate" is the number of times the heart beats within a certain period of time (usually one minute), and is an important indicator of the health of a living organism.

[0443] "Body temperature" refers to the internal temperature of an organism, usually measured in degrees Celsius (°C).

[0444] A "server" is a computing device that receives biometric information and emotional data sent from a wearable device and analyzes it in real time.

[0445] "Generative AI" is software that uses machine learning and artificial intelligence techniques to analyze collected biometric information and evaluate a worker's health condition and fatigue level.

[0446] The "emotion engine" is a computer program that analyzes voice and facial expression data to evaluate the emotional state of a worker.

[0447] A "robot" is an automated mechanical device that notifies workers in real time and suggests solutions when an abnormality occurs.

[0448] "Abnormal" refers to a situation in which the analysis results by the server exceed a preset standard value, which may affect the health and safety of workers.

[0449] A "notification" is a warning message sent from the server to an operator or administrator when an abnormality is detected.

[0450] "Countermeasures" are appropriate measures that the robot suggests when an abnormality is detected, such as recommending a break or seeking medical attention.

[0451] The present invention is a system that uses a wearable device worn by a worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work. Specific embodiments are described below.

[0452] System configuration

[0453] 1. Wearable devices

[0454] Workers wear wearable devices equipped with sensors that detect heart rate and body temperature in real time, as well as a microphone and camera to collect voice and facial expression data.

[0455] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[0456] 2. Device and dedicated app

[0457] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[0458] The dedicated app is responsible for receiving biometric information and emotional data sent from the wearable device and sending it to the server.

[0459] 3. Server

[0460] The server receives the biometric information and emotional data sent through a dedicated app.

[0461] The received data is analyzed using a generative AI model to evaluate the worker's health condition and fatigue level.

[0462] The emotion engine analyzes the emotion data received from the wearable device and assesses the worker's emotional state.

[0463] If an abnormality is detected based on the analysis results, the server will notify the worker or administrator of the abnormality and suggest appropriate countermeasures.

[0464] Specific examples

[0465] For example, let us consider a case where Worker A working in a factory has a heart rate of 130 beats per minute and a body temperature of 37.6°C, and the emotion engine recognizes that he is in a "stressed" state.

[0466] 1. Data Collection

[0467] The wearable device detects heart rate and body temperature, and collects voice and facial expression data, which is then sent via Bluetooth to a dedicated app.

[0468] 2. Data Analysis

[0469] The dedicated app transmits biometric information and emotional data to a server.

[0470] The server's generated AI analyzes the received biometric data in real time and evaluates the user's health and fatigue level.

[0471] The emotion engine analyzes the received emotion data and evaluates the emotional state.

[0472] 3. Anomaly Detection and Notification

[0473] If the analysis result exceeds the threshold, the server marks it as abnormal and generates an anomaly notification.

[0474] The server notifies the worker A of the abnormality and also notifies the worker A to take a break, and notifies the manager of the same.

[0475] The robot will suggest specific measures to be taken for Worker A and encourage actions such as recommending a break or seeking medical attention.

[0476] Prompt Sentence Examples

[0477] python

[0478] from health_monitoring import wearables

[0479] from emotion_engine import EmotionRecognizer

[0480] Prompt: Is the worker's heart rate above the reference range (60-100 beats per minute)?

[0481] heart_rate = 130

[0482] thresholds = {

[0483] 'heart_rate': (60, 100)

[0484] }

[0485] is_abnormal = not (thresholds['heart_rate'][0] <= heart_rate <= thresholds['heart_rate'][1])

[0486] print("Abnormal:", is_abnormal) Output: Abnormal: True

[0487] This system enables comprehensive monitoring of workers' biometric information and emotional state, and enables prompt and appropriate response if an abnormality is detected, thereby significantly improving the safety of the work environment and worker health management.

[0488] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0489] Step 1:

[0490] Data collection

[0491] The wearable device detects the worker's heart rate and body temperature in real time. The sensors (heart rate sensor, body temperature sensor) on the wearable device collect data every second, as well as voice and facial expression data. This data is sent to a dedicated app via Bluetooth.

[0492] Input: Biometric data (heart rate, body temperature) and emotional data (voice, facial expression) from wearable devices

[0493] Output: Send data to a dedicated app

[0494] Step 2:

[0495] Data reception and transmission

[0496] The dedicated app receives biometric and emotional data from the wearable device, and then transmits the data to a server.

[0497] Input: Biometric and emotional data from wearable devices

[0498] Output: Send data to the server

[0499] Step 3:

[0500] Biological data analysis

[0501] The server analyzes the biometric data sent from the dedicated app using a generative AI model. Based on heart rate and body temperature data, the server evaluates the worker's health condition and fatigue level. For example, if the heart rate exceeds the normal range (60 to 100 beats per minute), it marks it as abnormal.

[0502] Input: Biometric data (heart rate, body temperature)

[0503] Output: Health status and fatigue evaluation results

[0504] Step 4:

[0505] Emotional Data Analysis

[0506] The server uses an emotion engine to analyze the voice and facial expression data sent from the wearable device and evaluate the worker's emotional state. For example, it can recognize a "stressed" state from changes in voice tone and facial expression.

[0507] Input: Emotion data (voice, facial expression)

[0508] Output: Emotional state evaluation result

[0509] Step 5:

[0510] Overall evaluation and anomaly detection

[0511] The server integrates the health status assessment results from the generative AI model and the emotional status assessment results from the emotion engine to make a comprehensive judgment. If an abnormality is detected, it is marked as an abnormality.

[0512] Input: Health status assessment results, emotional status assessment results

[0513] Output: Overall evaluation result and abnormality mark

[0514] Step 6:

[0515] Abnormality notification and solution suggestions

[0516] If an abnormality is detected, the server generates an abnormality notification message and sends it to the worker and administrator. This message includes information about the worker's health and emotional state, as well as recommended actions to take. In addition, the robot notifies the worker in real time and suggests specific actions to take, such as taking a break or seeking medical attention.

[0517] Input: Overall evaluation result and abnormal mark

[0518] Output: Error notification message and suggested solutions

[0519] In this way, a system has been created that collects and analyzes data at each step, enabling prompt and appropriate action when an abnormality is detected.

[0520] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0521] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0522] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0523] [Second embodiment]

[0524] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0525] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0526] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0527] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0528] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0529] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0530] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0531] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0532] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0533] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0534] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0535] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0536] The present invention provides a system for improving safety during work by having a worker wear a wearable device and monitoring biological information such as heart rate and body temperature in real time. Specific embodiments are described below.

[0537] System configuration

[0538] 1. Wearable devices

[0539] Workers wear a wearable device that contains sensors that detect heart rate and body temperature in real time.

[0540] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[0541] 2. Dedicated app and device

[0542] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[0543] The app has the function of receiving biometric information sent from the wearable device and sending it to a server.

[0544] 3. Server and Generative AI

[0545] The server receives the biometric information sent through a dedicated app.

[0546] The generated AI inside the server analyzes the received biometric information and evaluates the worker's health condition and fatigue level.

[0547] If an abnormality is detected based on the analysis results, the server sends a notification to the worker and the administrator.

[0548] Explanation of program processing

[0549] 1. Data collection and transmission

[0550] Terminal (wearable device)

[0551] The wearable device detects the worker's heart rate and body temperature in real time.

[0552] The detected data is sent to a dedicated app at regular intervals (e.g., every second).

[0553] 2. Data Receipt and Storage

[0554] server

[0555] The server stores the biometric information received via a dedicated app in a database.

[0556] 3. Data Analysis

[0557] server

[0558] The server's generated AI analyzes the worker's health condition and fatigue level in real time based on the received data.

[0559] The analysis includes comparing patterns of heart rate and body temperature with baseline values.

[0560] 4. Anomaly Detection and Notification

[0561] server

[0562] If the analysis results exceed a preset threshold, the server detects an anomaly.

[0563] If an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the terminals of the worker and manager.

[0564] Specific examples

[0565] Worker C's case

[0566] Data collection

[0567] Worker C puts on the wearable device at the construction site and begins work.

[0568] The device detects the heart rate (e.g., 115 beats per minute) and body temperature (e.g., 38.1°C) and sends the data to a server via a dedicated app.

[0569] Data analysis and notification

[0570] server

[0571] The server analyzes the data received from the dedicated app using a generation AI.

[0572] Based on the analysis results, it is detected whether the heart rate and body temperature exceed the set standard values ​​(e.g., heart rate 60-100 beats / min, body temperature 36.0-37.5°C).

[0573] The server generates an abnormality notification message and sends it to the terminals of worker C and the administrator.

[0574] correspondence

[0575] User (Worker C)

[0576] Worker C receives an abnormality notification via the app, stops working, and monitors his / her health.

[0577] Contact a medical institution if necessary.

[0578] administrator

[0579] The manager checks the abnormality notification in the management system and instructs Worker C to take a break and arranges for medical consultation.

[0580] This system allows for real-time monitoring of workers' health conditions and enables rapid response in the event of an abnormality, thereby reducing the risk of accidents during work and ensuring the health and safety of workers.

[0581] The processing flow will be explained below.

[0582] Step 1:

[0583] The user wears the wearable device and performs initial setup using a device with a dedicated app installed. Specifically, the user logs in to their account, sets emergency contacts, and sets alert thresholds.

[0584] Step 2:

[0585] The terminal (wearable device) detects the worker's heart rate and body temperature in real time. This biometric data is recorded periodically (e.g., every second) in its internal memory. The recorded data is then transmitted via wireless communication such as Bluetooth to a terminal with a dedicated app installed.

[0586] Step 3:

[0587] The terminal (a smartphone or tablet with a dedicated app installed) then transmits the biometric data received from the wearable device to a server via the internet using a secure communication protocol.

[0588] Step 4:

[0589] The server receives the biometric data sent from the device, stores the data in a database, and uses it for subsequent analysis.

[0590] Step 5:

[0591] The server uses generative AI to analyze the incoming biometric data in real time, including evaluating the worker's heart rate and body temperature fluctuation patterns and comparing them with pre-set baseline values.

[0592] Step 6:

[0593] The server evaluates the worker's health condition and fatigue level based on the analysis results, and if an abnormality exceeding the reference value is detected, the server marks it as abnormal.

[0594] Step 7:

[0595] If an anomaly is detected, the server immediately generates an anomaly notification message, which includes the specific details of the anomaly, the biometric data causing the anomaly, and recommended countermeasures.

[0596] Step 8:

[0597] The server sends an abnormality notification message to the devices of the user (worker) and administrator. Notifications are sent in multiple ways, including push notifications and email, to enable a quick response.

[0598] Step 9:

[0599] The user (worker) receives an abnormality notification via their device. The user checks the notification and takes immediate action, such as temporarily suspending work. If necessary, they may drink fluids, take a break, or contact a medical institution.

[0600] Step 10:

[0601] The administrator receives an abnormality notification from the server and checks it on the management screen. The administrator monitors the worker's condition and, if necessary, gives specific instructions to the worker, such as instructing them to take a break or seek medical advice.

[0602] This sequence of steps will realize a system that monitors workers' biological information in real time and takes prompt and appropriate action in the event of an abnormality, thereby significantly improving the safety of the work environment and minimizing health risks to workers.

[0603] Example 1

[0604] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0605] While it is important to monitor the health status of workers in real time at workplaces, conventional systems have had difficulty efficiently and reliably monitoring workers' vital signs, such as heart rate and body temperature, making it difficult to respond quickly when an abnormality occurs. Additionally, on-site managers have had few means to receive information in real time and issue instructions for a prompt response.

[0606] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0607] In this invention, the server includes means for detecting the heart rate and body temperature in real time using a wearable device worn by the worker, means for transmitting the detected heart rate and body temperature data to the server via a terminal, means for analyzing the detected data using a generative AI model in the server and evaluating the worker's health condition and fatigue level, and means for notifying the worker and a manager if an abnormality is detected based on the analysis results. This makes it possible to monitor the worker's health condition in real time and to take prompt action if an abnormality occurs.

[0608] A "wearable device" is an electronic device worn by a worker to detect heart rate and body temperature in real time.

[0609] A "terminal" is an electronic device that has the function of receiving data sent from a wearable device and sending it to a server.

[0610] A "server" is a computer system that receives data sent from a terminal and analyzes it using a generative AI model.

[0611] The "generative AI model" is an algorithm that uses artificial intelligence to analyze collected biometric information and evaluate the worker's health condition and fatigue level.

[0612] "Detecting signs" refers to detecting abnormalities in a worker's heart rate or body temperature based on data analyzed by the generative AI model.

[0613] The "abnormality notification means" is a system and method for issuing an alert to workers and administrators when the server detects an abnormality.

[0614] The "analysis results" are the evaluation results regarding health status and fatigue level output by the generative AI model after processing the input data.

[0615] "Reference values" are standard heart rate and body temperature values ​​that are used for comparison when assessing a worker's health condition.

[0616] The present invention provides a system for improving safety during work by having a worker wear a wearable device and monitoring biological information such as heart rate and body temperature in real time. Specific embodiments are described below.

[0617] System configuration

[0618] 1. Wearable devices

[0619] Workers wear a wearable device equipped with sensors that detect heart rate and body temperature in real time.

[0620] The device uses wireless communication methods such as Bluetooth to communicate data with devices on which a dedicated app is installed.

[0621] 2. Dedicated app and device

[0622] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[0623] The app has the ability to receive biometric information sent from wearable devices, such as Apple's iPhone or Samsung's Galaxy series, and send it to a server.

[0624] 3. Server and Generative AI

[0625] The server receives and stores the biometric information sent via a dedicated app. The server can be hosted on Amazon Web Services (AWS) or Google Cloud Platform (GCP).

[0626] The generative AI inside the server analyzes the received biometric information and evaluates the worker's health condition and fatigue level. TensorFlow and PyTorch can be used for the generative AI model.

[0627] The server has a means for sending a notification to the worker and the administrator if an abnormality is detected based on the analysis results.

[0628] Specific examples

[0629] Data collection

[0630] Worker examples

[0631] Workers put on the wearable device at the construction site and begin work.

[0632] The wearable device detects heart rate (e.g., 115 beats per minute) and body temperature (e.g., 38.1°C), and transmits the data via Bluetooth to a device with a dedicated app installed.

[0633] Data transmission and analysis

[0634] Terminal and server operation

[0635] The terminal receives the detected biometric information and periodically transmits it to the server.

[0636] The server analyzes the transmitted data using a generative AI model to determine whether the heart rate and body temperature exceed the reference values ​​(e.g., heart rate 60-100 beats per minute, body temperature 36.0-37.5°C).

[0637] If an abnormality is detected as a result of the analysis, the server generates an abnormality notification message and immediately sends it to the terminals of the worker and manager.

[0638] Response Procedure

[0639] Worker

[0640] Workers will receive notifications of abnormalities via a dedicated app, stop working in a safe place, and monitor their health.

[0641] Contact a medical institution if necessary.

[0642] administrator

[0643] The manager checks the abnormality notification on the management system and instructs the worker to take a break or arranges for medical consultation.

[0644] Example prompts for generative AI models

[0645] Example 1: How can I send a notification when a worker's heart rate exceeds a threshold?

[0646] Example 2: Walk us through the process of creating an AI model using TensorFlow to monitor heart rate and body temperature in real time.

[0647] Example 3: How can you design a database to monitor the health status of workers?

[0648] By utilizing this system, it is possible to monitor the health status of workers in real time and respond quickly to any abnormalities, thereby reducing the risk of accidents during work and ensuring the health and safety of workers.

[0649] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0650] System program processing flow

[0651] Step 1:

[0652] Data collection

[0653] Terminal (wearable device)

[0654] The wearable device detects the worker's heart rate and body temperature in real time.

[0655] Input: Worker's biological information (heart rate, body temperature)

[0656] Data processing: Measuring heart rate and body temperature using sensors

[0657] Output: Measured vital signs (heart rate 115 beats / min, body temperature 38.1°C)

[0658] Step 2:

[0659] Data transmission

[0660] Terminal

[0661] Biometric information obtained from the wearable device is sent to a dedicated app via Bluetooth.

[0662] Input: Measured biological information (heart rate 115 beats / min, body temperature 38.1℃)

[0663] Data processing: Preparation for data transmission via Bluetooth communication

[0664] Output: Biometric information packet for transmission

[0665] Step 3:

[0666] Data reception and temporary storage

[0667] Terminal

[0668] The terminal receives biometric information sent from the wearable device through a dedicated app.

[0669] The app temporarily stores the received data in the device's memory.

[0670] Input: Biometric packet for transmission

[0671] Data processing: Data reception and temporary storage

[0672] Output: Temporarily stored vital signs (heart rate 115 beats / min, body temperature 38.1℃)

[0673] Step 4:

[0674] Data transmission and persistent storage

[0675] Terminal

[0676] The terminal transmits data to the server at regular intervals (e.g., every second).

[0677] Input: Temporarily stored biometric information

[0678] Data processing: creating data packets and preparing them for transmission

[0679] Output: Biometric information sent to the server

[0680] server

[0681] The server receives the biometric information sent via a dedicated app.

[0682] The received data is stored in a database. For example, "Heart rate 115 beats per minute, body temperature 38.1°C" is recorded in a MySQL database.

[0683] Input: Biometric information sent to the server

[0684] Data processing: receiving data and inserting it into the database

[0685] Output: Biometric information stored in a database

[0686] Step 5:

[0687] Data analysis

[0688] server

[0689] The server analyzes the received data using a generative AI model.

[0690] The generative AI model compares heart rate and body temperature with baseline values ​​to assess the worker's health and fatigue level in real time.

[0691] Input: Biometric information stored in the database

[0692] Data processing: Analysis using generative AI models (pattern recognition, comparison with reference values)

[0693] Output: Health status and fatigue evaluation results

[0694] Step 6:

[0695] Anomaly Detection

[0696] server

[0697] If the analysis data exceeds a preset reference value, the server detects an anomaly.

[0698] Input: Health status and fatigue assessment results

[0699] Data processing: Abnormality detection by comparing with reference values

[0700] Output: Anomaly detection result (abnormal / no abnormality)

[0701] Step 7:

[0702] Abnormality notification and response

[0703] server

[0704] When an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the worker and the administrator.

[0705] Input: Anomaly detection results

[0706] Data processing: Generation of abnormality notification messages

[0707] Output: Sending an error notification message

[0708] User (operator, administrator)

[0709] Workers will receive notifications of abnormalities via a dedicated app, stop working in a safe place, and monitor their health.

[0710] The manager checks the abnormality notification on the management system and instructs the worker to take a break or arranges for medical consultation.

[0711] (Application example 1)

[0712] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0713] Currently, many factories are using robots, but if the operator's health condition suddenly changes while the robot is operating, there is a risk that the robot will continue working without noticing. This situation not only impairs the operator's health, but can also have a negative impact on work efficiency and safety, so there is a need for a system that can monitor the operator's health condition in real time and respond immediately if an abnormality occurs.

[0714] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0715] In this invention, the server includes a means for transmitting detected heart rate and body temperature data to the server, a means for analyzing the detected data using a generative AI and evaluating the worker's health condition and fatigue level, a means for notifying the worker and manager if an abnormality is detected based on the analysis results, and a means for temporarily suspending machine operation when an abnormality is detected. This makes it possible to understand the operator's health condition in real time and respond immediately when an abnormality occurs, thereby improving safety and efficiency during work.

[0716] A "wearable device" is a device worn by a worker that detects biometric information such as heart rate and body temperature in real time.

[0717] The "heart rate" is a type of biological information that indicates the number of times the heart beats per unit time.

[0718] "Body temperature" is biological information that indicates the internal body temperature of the worker.

[0719] A "server" is a central device that receives and analyzes biometric information sent from a wearable device.

[0720] "Generative AI" is an artificial intelligence model that analyzes received biometric information and evaluates the worker's health condition and fatigue level.

[0721] "Abnormal" refers to a state in which the analysis results exceed a preset reference value.

[0722] An "abnormality notification" is an alarm or message sent to a worker or manager when an abnormality is detected based on the analysis results.

[0723] A "means for temporarily suspending robot operation" is a mechanism or system for temporarily halting machine operation when an abnormality is detected.

[0724] "Worker" refers to a person who wears a wearable device while performing their work.

[0725] The "manager" is the person in charge of monitoring the health status of workers and taking action if any abnormalities occur.

[0726] The present invention improves safety during work by using a system including a wearable device worn by a worker, a transmission means, an analysis means, a notification means, and a means for temporarily suspending machine operation. Detailed embodiments for implementing the present invention will be described below.

[0727] System Configuration

[0728] 1. Wearable devices

[0729] Workers wear a wearable device that contains sensors to detect heart rate and body temperature in real time.

[0730] Wearable devices communicate with devices such as smartphones and tablets using wireless communication technologies such as Bluetooth.

[0731] 2. Terminals and Edge Devices

[0732] Users use a smartphone or tablet with a dedicated app installed.

[0733] The dedicated app has the function of receiving biometric information sent from the wearable device and sending it to a server.

[0734] 3. Server and Generative AI Model

[0735] The server receives the biometric information sent from the wearable device and stores it in a database.

[0736] The generative AI model analyzes the data sent to the server and evaluates the worker's health condition and fatigue level.

[0737] The analysis includes comparing patterns of heart rate and body temperature with baseline values.

[0738] 4. Anomaly Detection and Notification

[0739] Based on the analysis results, if an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the worker and administrator.

[0740] Upon receiving the notification, the manager will instruct appropriate action to ensure the safety of the workers.

[0741] 5. Robot stops working

[0742] When an abnormality is detected, the server sends an instruction to temporarily suspend the robot's operation.

[0743] This function immediately stops machine operation if an abnormality occurs in the worker's health condition, reducing the risk of accidents.

[0744] Program processing explanation

[0745] In implementing the present invention, the following hardware and software are used.

[0746] Hardware:

[0747] Wearable devices: Typical smartwatches (e.g., Fitbit, Apple Watch).

[0748] Server: A cloud-based server (e.g., AWS Lambda, Google Cloud).

[0749] Robot: An automated device used in factories.

[0750] software:

[0751] Programming language: Python

[0752] AI model: A generative AI model using TensorFlow and PyTorch that uses machine learning techniques to analyze biometric data.

[0753] Database: Manage biometric data using MySQL or MongoDB.

[0754] Communication protocol: Data communication between devices is performed using REST API.

[0755] Specific examples

[0756] As an example, let's take Operator A, who works in a factory. Operator A wears a wearable device while working. If the device detects a heart rate of 120 beats per minute and a body temperature of 38.5°C, the server receives this data and analyzes it using a generative AI model. As a result, it is determined that these values ​​exceed the standard values, and an abnormality notification is issued. The server immediately pauses the robot's operation and notifies Operator A and the administrator of the abnormality. This allows Operator A to quickly check his health condition and, if necessary, contact a medical institution.

[0757] Example of an input prompt for a generative AI model:

[0758] Design a program to analyze heart rate and body temperature data collected in real time and generate an abnormality notification if the data exceeds the threshold. Also, add a method to pause the operation of the factory robot based on the abnormality notification. The threshold for heart rate is 60-100 beats per minute, and the threshold for body temperature is 36.0-37.5°C.

[0759]

[0760] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0761] Step 1:

[0762] Data collection

[0763] Before starting work, the user puts on the wearable device, which detects the user's heart rate and body temperature in real time. These values ​​are measured by sensors and temporarily stored inside the wearable device.

[0764] Input: Heart rate and temperature biometric data

[0765] Output: Temporary data in the wearable device

[0766] How it works: Heart rate is detected using an optical sensor, and body temperature is measured using a built-in body temperature sensor.

[0767] Step 2:

[0768] Data transmission

[0769] The wearable device periodically collects biometric data from sensors and transmits it to the user's smartphone or tablet via wireless communication such as Bluetooth. A dedicated app receives the data and transfers it to a server.

[0770] Input: Temporary data in the wearable device

[0771] Output: Data displayed on the dedicated app and sent to the server

[0772] What it does: The device activates its Bluetooth module, pairs with the smartphone, and transmits data. A dedicated app receives this data in real time and forwards it to the server via an HTTP request.

[0773] Step 3:

[0774] analysis

[0775] The server stores the received data in a database, then analyzes the biometric data using a generative AI model. Specifically, it compares heart rate and body temperature values ​​with baseline values ​​to determine whether there are any abnormalities.

[0776] Input: Biometric data received by the server

[0777] Output: Analysis result (normal / abnormal)

[0778] Specific operations: Connect to a database and save data to storage. A generative AI model is used to analyze the saved data. This model is built using TensorFlow and PyTorch and performs calculations to detect outliers.

[0779] Step 4:

[0780] Anomaly Detection and Notification

[0781] If an abnormality is detected, the server generates an abnormality notification message based on the analysis results of the AI. This message is sent to workers and managers via a dedicated app.

[0782] Input: Analysis results of the generative AI model

[0783] Output: Error notification message

[0784] Specific operation: If an abnormal value is detected, the server generates a notification message and sends a push notification to the worker's smartphone or the manager's device.

[0785] Step 5:

[0786] Robot stops working

[0787] If the server detects an abnormality, it sends an instruction to the robots in the factory to stop operating. This instruction is transmitted to the robots via the control system.

[0788] Input: Analysis results of the generative AI model

[0789] Output: Robot stops moving

[0790] Specific operation: The server sends a stop command to the robot control system via an HTTP request, etc. The control system receives this command and stops the robot's operation.

[0791] Example prompt sentence:

[0792] Design a program to analyze heart rate and body temperature data collected in real time and generate an abnormality notification if the data exceeds the threshold. Also, add a method to pause the operation of the factory robot based on the abnormality notification. The threshold for heart rate is 60-100 beats per minute, and the threshold for body temperature is 36.0-37.5°C.

[0793] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0794] The present invention is a system that uses a wearable device worn by a worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work. Specific embodiments are described below.

[0795] System configuration

[0796] 1. Wearable devices

[0797] Workers wear a wearable device that includes sensors to detect heart rate and body temperature in real time, as well as a microphone and camera for emotion recognition.

[0798] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[0799] 2. Dedicated app and device

[0800] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[0801] The app has the function of receiving biometric information and emotional data sent from the wearable device and sending it to a server.

[0802] 3. Server, Generative AI and Emotion Engine

[0803] The server receives the biometric information and emotional data sent through a dedicated app.

[0804] The generated AI inside the server analyzes the received biometric data and evaluates the worker's health condition and fatigue level.

[0805] The emotion engine analyzes the emotion data received from the wearable device and assesses the worker's emotional state.

[0806] If an abnormality is detected based on the analysis results, the server sends a notification to the worker and the administrator.

[0807] Explanation of program processing

[0808] 1. Data collection and transmission

[0809] Terminal (wearable device)

[0810] The wearable device detects the worker's heart rate and body temperature in real time, and also uses a microphone and camera to collect voice and facial expression data.

[0811] The detected biometric and emotional data is sent to a dedicated app at regular intervals (e.g., every second).

[0812] 2. Data Receipt and Storage

[0813] server

[0814] The server stores the biometric and emotional data received via a dedicated app in a database.

[0815] 3. Data Analysis

[0816] server

[0817] The server's generated AI analyzes the worker's health condition and fatigue level in real time based on the received biometric data.

[0818] The emotion engine analyzes the received emotion data and evaluates the worker's emotional state, which includes changes in tone of voice and facial expressions.

[0819] 4. Overall evaluation and anomaly detection

[0820] server

[0821] The server makes a comprehensive judgment based on the results of biometric data analysis by the generation AI and the emotional state evaluation results by the emotion engine.

[0822] Based on the overall evaluation, if an abnormality exceeding the reference value is detected, the server marks it as abnormal.

[0823] 5. Abnormality notification and response suggestions

[0824] server

[0825] If an anomaly is detected, the server immediately generates an anomaly notification message, which includes the specific details of the anomaly, the data causing the anomaly, and recommended actions to take.

[0826] The server sends an abnormality notification message to the terminals of the user (worker) and the administrator.

[0827] Specific examples

[0828] Worker D's case

[0829] Data collection

[0830] Worker D puts on the wearable device at the construction site and begins work.

[0831] The device detects elevated heart rate (e.g., 120 beats per minute), body temperature (e.g., 37.8°C), and voice tone, and transmits this data to a server via a dedicated app.

[0832] Data analysis and abnormality notification

[0833] server

[0834] The server uses AI to analyze and check the data received from the dedicated app, detecting whether the heart rate and body temperature exceed the standard values ​​(heart rate 60-100 beats per minute, body temperature 36.0-37.5°C).

[0835] The emotion engine analyzes the tone of voice as being different from normal and assesses that the worker is feeling stressed.

[0836] The server integrates these analysis results and determines that there is something wrong with worker D's health condition.

[0837] The server generates an abnormality notification message and sends it to the terminals of worker D and the administrator.

[0838] correspondence

[0839] User (Worker D)

[0840] Worker D receives an abnormality notification via the app, stops working, takes a break, and contacts a medical institution if necessary.

[0841] administrator

[0842] The administrator checks the abnormality notification from the server on the management screen, reviews the status of worker D, and, if necessary, issues instructions for a break or arranges for him to be examined at a medical institution.

[0843] In this way, a system can be built that monitors workers' biometric information and emotional state in real time and responds quickly in the event of an abnormality, significantly improving the safety of the work environment and minimizing health risks for workers.

[0844] The processing flow will be explained below.

[0845] Step 1:

[0846] The user wears the wearable device and performs initial setup using a device with a dedicated app installed, including logging in to their account, setting emergency contacts, and setting alert thresholds.

[0847] Step 2:

[0848] The terminal (wearable device) detects the worker's heart rate and body temperature in real time, and also uses the microphone and camera built into the wearable device to collect emotional data such as voice and facial expressions in real time.

[0849] Step 3:

[0850] The terminal (wearable device) transmits the collected heart rate, body temperature, and emotional data to a dedicated app at regular intervals (e.g., every second).

[0851] Step 4:

[0852] The device (a smartphone or tablet with a dedicated app installed) then transmits the received biometric and emotional data to a server via the Internet.

[0853] Step 5:

[0854] The server receives the biometric data and emotion data sent from the terminal and stores them in a database.

[0855] Step 6:

[0856] The server's generation AI analyzes the received biometric data and evaluates the worker's health condition and fatigue level in real time, while the emotion engine analyzes the received emotional data and evaluates the worker's emotional state.

[0857] Step 7:

[0858] Based on the analysis results, the server makes a comprehensive judgment of the heart rate, body temperature, and emotional data, and if it detects any abnormalities that exceed the standard values, it marks them as abnormal.

[0859] Step 8:

[0860] If the server detects an anomaly that exceeds the threshold, it immediately generates an anomaly notification message that includes the nature of the anomaly, the data that caused it, and a recommended course of action.

[0861] Step 9:

[0862] The server sends an abnormality notification message to the devices of the user (worker) and administrator. Notifications are sent in multiple ways, including push notifications and email, to enable a quick response.

[0863] Step 10:

[0864] The user (worker) receives an abnormality notification via their terminal. After checking the notification, the user temporarily stops their work and takes necessary measures, such as drinking water, taking a break, or contacting a medical institution.

[0865] Step 11:

[0866] The administrator checks the abnormality notification from the server on the management screen and monitors the worker's condition. If necessary, the administrator can instruct workers to take a break, seek medical advice, or take other measures.

[0867] In this way, the system of the present invention monitors the worker's biometric information and emotional state in real time, and takes prompt and appropriate action in the event of an abnormality, thereby ensuring the safety and health of the worker.

[0868] Example 2

[0869] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0870] Conventional technologies exist that monitor only a worker's biometric information, such as heart rate and body temperature, in real time. However, these systems are unable to adequately assess the worker's psychological stress and fatigue due to emotional changes, making it difficult to fully grasp the worker's overall health status. As a result, there are limitations to improving safety and health management during work. Furthermore, the difficulty of responding quickly when an abnormality occurs can have a negative impact on work efficiency and safety.

[0871] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting emotion data using a wearable device that detects heart rate and body temperature in real time, means for evaluating health status and fatigue level using a generation AI, means for evaluating the emotional state using an emotion engine, and means for notifying the worker and manager when an abnormality is detected. This allows for a comprehensive evaluation of the worker's biometric information and emotional state, and enables rapid response when an abnormality is detected in real time.

[0872] A "worker" is a person who works at a work site while wearing a wearable device.

[0873] A "wearable device" is a device that can be worn by a worker and is equipped with sensors and input devices for detecting biometric and emotional data such as heart rate, body temperature, voice, and facial expressions in real time.

[0874] "Heart rate" refers to data indicating the number of times the worker's heart beats per unit time.

[0875] "Body temperature" refers to data indicating the worker's body temperature.

[0876] "Emotion data" is data obtained from voice and facial expressions, and includes information for evaluating the emotional state of the worker.

[0877] "Server" is a general term for a computer system that receives, analyzes, and stores biometric and emotional data sent through a dedicated app.

[0878] "Generative AI" is an artificial intelligence technology that analyzes received biometric data and evaluates the health status and fatigue level of workers.

[0879] An "emotion engine" is an algorithm or software that analyzes emotional data such as voice and facial expressions to evaluate the emotional state of a worker.

[0880] "Analysis results" refer to the evaluation results of the worker's health and emotional state obtained by the generative AI and emotion engine.

[0881] The "notification means" refers to a function or device that notifies the worker or manager of an abnormality when it is detected based on the analysis results.

[0882] "Supervisor" refers to the person in charge of managing the safety and health of workers at the work site.

[0883] This invention is a system that uses a wearable device worn by the worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work.

[0884] System configuration

[0885] 1. Wearable devices

[0886] Workers wear a wearable device that includes sensors to detect heart rate and body temperature in real time, as well as a microphone and camera for emotion recognition. The wearable device communicates with a terminal on which a dedicated app is installed using wireless communication methods such as Bluetooth.

[0887] 2. Dedicated app and device

[0888] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed. The app has the function of receiving biometric and emotional data sent from the wearable device and sending it to a server.

[0889] 3. Server, Generative AI and Emotion Engine

[0890] The server receives biometric information and emotional data sent through a dedicated app. The generation AI inside the server analyzes the received biometric data and evaluates the worker's health condition and fatigue level. The emotion engine analyzes the emotional data received from the wearable device and evaluates the worker's emotional state. If the server detects an abnormality based on the analysis results, it sends a notification to the worker and manager.

[0891] Specific actions

[0892] Example: Worker D's case

[0893] Data collection

[0894] Worker D puts on the wearable device at the construction site and begins work. The device detects rising heart rate (e.g., 120 beats per minute), body temperature (e.g., 37.8°C), and voice tone. This data is sent to the server via a dedicated app.

[0895] Data analysis and abnormality notification

[0896] The server uses the generation AI to analyze the heart rate and body temperature. It confirms that they exceed the standard values ​​(a heart rate of 60-100 beats per minute and a body temperature of 36.0-37.5°C are standard values). The emotion engine analyzes the tone of the voice data received and determines that the worker is in a state of stress. The server combines these results and determines that worker D's health and emotional state are abnormal. It creates an abnormality notification message and immediately sends it to the devices of worker D and the manager, including specific details of the abnormality, the cause, and countermeasures.

[0897] correspondence

[0898] Worker D receives the abnormality notification via the app, stops working, and takes a break. If necessary, he or she contacts a medical institution. The administrator receives the abnormality notification from the server and checks the status of Worker D. The administrator instructs the appropriate countermeasures and ensures the safety of Worker D.

[0899] An example of a prompt for this system is:

[0900] This system monitors workers' health and emotional state in real time using heart rate, body temperature, voice tone, and facial expression data obtained from wearable devices worn by workers. If an abnormality is detected, such as a worker feeling stressed or their body temperature exceeding the normal range, a notification is sent immediately. In this way, the system improves the safety of the work environment and minimizes health risks to workers.

[0901] In this way, a system can be built that monitors workers' biometric information and emotional state in real time and responds quickly in the event of an abnormality, significantly improving the safety of the work environment and minimizing health risks for workers.

[0902] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0903] Step 1: Data collection and transmission

[0904] Terminal (wearable device)

[0905] The wearable device measures the worker's heart rate and body temperature in real time. It also uses a built-in microphone and camera to capture the worker's voice and facial expressions. The collected biometric and emotional data is sent via Bluetooth to a device (smartphone or tablet) with a dedicated app installed at regular intervals (e.g., every second).

[0906] Input: Heart rate, body temperature, voice data, facial expression data

[0907] Data processing / calculation: Measurement by sensor, recording by microphone and camera

[0908] Output: Biometric and emotional data sent to a dedicated app

[0909] Step 2: Receiving and storing data

[0910] server

[0911] The server receives biometric and emotional data sent from the dedicated app in real time. The received data is structured and stored in a database. Each piece of data is assigned the worker's ID and a timestamp.

[0912] Input: Biometric data and emotional data sent from a dedicated app

[0913] Data processing / calculation: Receiving data, assigning ID and time stamp

[0914] Output: Biometric and emotional data stored in a database

[0915] Step 3: Data analysis

[0916] server

[0917] The generative AI analyzes received biometric data such as heart rate and body temperature to assess the worker's health and fatigue level. The emotion engine analyzes collected voice tone and facial expression data to assess the worker's emotional state. Each analysis result is stored in a database.

[0918] Input: Heart rate, body temperature, voice tone, and facial expression data obtained from the database

[0919] Data processing / calculation: Analysis of health status and fatigue level using generative AI, analysis of emotional state using emotion engine

[0920] Output: Analysis results (health status, fatigue level, emotional state)

[0921] Step 4: Overall evaluation and anomaly detection

[0922] server

[0923] The server combines the results of the biometric data analysis by the generative AI with the results of the emotional state evaluation by the emotion engine to make a comprehensive evaluation. If the analysis results exceed a preset threshold, the system will mark the person as abnormal.

[0924] Input: Analysis results of generative AI and emotion engine

[0925] Data processing / calculation: Integration of analysis results, comparison with reference values

[0926] Output: Overall evaluation result marked as abnormal (exceeding the standard value)

[0927] Step 5: Notification of abnormalities and suggested actions

[0928] server

[0929] When an abnormality is detected in the overall evaluation, the server generates an abnormality notification message. This message contains the specific details of the abnormality, the data that caused it, and recommended countermeasures. The abnormality notification message is sent to the terminals of the worker and the manager.

[0930] Input: Overall evaluation result marked as abnormal

[0931] Data processing / calculation: Generation of error notification messages

[0932] Output: Anomaly notification message sent to the terminals of the worker and manager

[0933] In this way, specific data processing and calculations are performed at each step, making it possible to monitor the worker's health and emotional state in real time and respond quickly in the event of an abnormality.

[0934] (Application example 2)

[0935] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0936] In conventional work environments, systems exist that manage the health status of workers by continuously monitoring biometric information such as heart rate and body temperature, but these systems often overlook health risks and declines in work efficiency due to changes in emotional state. Furthermore, there is a lack of real-time responses and suggestions for appropriate countermeasures when abnormalities are detected, which has resulted in insufficient improvements in worker safety and productivity. Therefore, the present invention aims to provide a system that comprehensively monitors a worker's biometric information and emotional state and suggests appropriate countermeasures when an abnormality is detected.

[0937] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0938] In this invention, the server includes means for detecting the heart rate and body temperature in real time using a wearable device worn by the worker, means for transmitting the detected heart rate and body temperature data to the server, means for analyzing the detected data using a generative AI and evaluating the worker's health condition and fatigue level, means for notifying the worker and manager when an abnormality is detected, means for collecting and analyzing voice and facial expression data to evaluate the worker's emotional state using an emotion engine, and means for the robot to notify the worker of the abnormality and suggest measures to take, such as taking a break or seeing a doctor. This makes it possible to comprehensively monitor and analyze the worker's health and emotional state, and to take prompt and appropriate action when an abnormality occurs.

[0939] A "wearable device" is a device worn by a worker that detects biometric information such as heart rate and body temperature in real time.

[0940] "Heart rate" is the number of times the heart beats within a certain period of time (usually one minute), and is an important indicator of the health of a living organism.

[0941] "Body temperature" refers to the internal temperature of an organism, usually measured in degrees Celsius (°C).

[0942] A "server" is a computing device that receives biometric information and emotional data sent from a wearable device and analyzes it in real time.

[0943] "Generative AI" is software that uses machine learning and artificial intelligence techniques to analyze collected biometric information and evaluate a worker's health condition and fatigue level.

[0944] The "emotion engine" is a computer program that analyzes voice and facial expression data to evaluate the emotional state of a worker.

[0945] A "robot" is an automated mechanical device that notifies workers in real time and suggests solutions when an abnormality occurs.

[0946] "Abnormal" refers to a situation in which the analysis results by the server exceed a preset standard value, which may affect the health and safety of workers.

[0947] A "notification" is a warning message sent from the server to an operator or administrator when an abnormality is detected.

[0948] "Countermeasures" are appropriate measures that the robot suggests when an abnormality is detected, such as recommending a break or seeking medical attention.

[0949] The present invention is a system that uses a wearable device worn by a worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work. Specific embodiments are described below.

[0950] System configuration

[0951] 1. Wearable devices

[0952] Workers wear wearable devices equipped with sensors that detect heart rate and body temperature in real time, as well as a microphone and camera to collect voice and facial expression data.

[0953] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[0954] 2. Device and dedicated app

[0955] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[0956] The dedicated app is responsible for receiving biometric information and emotional data sent from the wearable device and sending it to the server.

[0957] 3. Server

[0958] The server receives the biometric information and emotional data sent through a dedicated app.

[0959] The received data is analyzed using a generative AI model to evaluate the worker's health condition and fatigue level.

[0960] The emotion engine analyzes the emotion data received from the wearable device and assesses the worker's emotional state.

[0961] If an abnormality is detected based on the analysis results, the server will notify the worker or administrator of the abnormality and suggest appropriate countermeasures.

[0962] Specific examples

[0963] For example, let us consider a case where Worker A working in a factory has a heart rate of 130 beats per minute and a body temperature of 37.6°C, and the emotion engine recognizes that he is in a "stressed" state.

[0964] 1. Data Collection

[0965] The wearable device detects heart rate and body temperature, and collects voice and facial expression data, which is then sent via Bluetooth to a dedicated app.

[0966] 2. Data Analysis

[0967] The dedicated app transmits biometric information and emotional data to a server.

[0968] The server's generated AI analyzes the received biometric data in real time and evaluates the user's health and fatigue level.

[0969] The emotion engine analyzes the received emotion data and evaluates the emotional state.

[0970] 3. Anomaly Detection and Notification

[0971] If the analysis result exceeds the threshold, the server marks it as abnormal and generates an anomaly notification.

[0972] The server notifies the worker A of the abnormality and also notifies the worker A to take a break, and notifies the manager of the same.

[0973] The robot will suggest specific measures to be taken for Worker A and encourage actions such as recommending a break or seeking medical attention.

[0974] Prompt Sentence Examples

[0975] python

[0976] from health_monitoring import wearables

[0977] from emotion_engine import EmotionRecognizer

[0978] Prompt: Is the worker's heart rate above the reference range (60-100 beats per minute)?

[0979] heart_rate = 130

[0980] thresholds = {

[0981] 'heart_rate': (60, 100)

[0982] }

[0983] is_abnormal = not (thresholds['heart_rate'][0] <= heart_rate <= thresholds['heart_rate'][1])

[0984] print("Abnormal:", is_abnormal) Output: Abnormal: True

[0985] This system enables comprehensive monitoring of workers' biometric information and emotional state, and enables prompt and appropriate response if an abnormality is detected, thereby significantly improving the safety of the work environment and worker health management.

[0986] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0987] Step 1:

[0988] Data collection

[0989] The wearable device detects the worker's heart rate and body temperature in real time. The sensors (heart rate sensor, body temperature sensor) on the wearable device collect data every second, as well as voice and facial expression data. This data is sent to a dedicated app via Bluetooth.

[0990] Input: Biometric data (heart rate, body temperature) and emotional data (voice, facial expression) from wearable devices

[0991] Output: Send data to a dedicated app

[0992] Step 2:

[0993] Data reception and transmission

[0994] The dedicated app receives biometric and emotional data from the wearable device, and then transmits the data to a server.

[0995] Input: Biometric and emotional data from wearable devices

[0996] Output: Send data to the server

[0997] Step 3:

[0998] Biological data analysis

[0999] The server analyzes the biometric data sent from the dedicated app using a generative AI model. Based on heart rate and body temperature data, the server evaluates the worker's health condition and fatigue level. For example, if the heart rate exceeds the normal range (60 to 100 beats per minute), it marks it as abnormal.

[1000] Input: Biometric data (heart rate, body temperature)

[1001] Output: Health status and fatigue evaluation results

[1002] Step 4:

[1003] Emotional Data Analysis

[1004] The server uses an emotion engine to analyze the voice and facial expression data sent from the wearable device and evaluate the worker's emotional state. For example, it can recognize a "stressed" state from changes in voice tone and facial expression.

[1005] Input: Emotion data (voice, facial expression)

[1006] Output: Emotional state evaluation result

[1007] Step 5:

[1008] Overall evaluation and anomaly detection

[1009] The server integrates the health status assessment results from the generative AI model and the emotional status assessment results from the emotion engine to make a comprehensive judgment. If an abnormality is detected, it is marked as an abnormality.

[1010] Input: Health status assessment results, emotional status assessment results

[1011] Output: Overall evaluation result and abnormality mark

[1012] Step 6:

[1013] Abnormality notification and solution suggestions

[1014] If an abnormality is detected, the server generates an abnormality notification message and sends it to the worker and administrator. This message includes information about the worker's health and emotional state, as well as recommended actions to take. In addition, the robot notifies the worker in real time and suggests specific actions to take, such as taking a break or seeking medical attention.

[1015] Input: Overall evaluation result and abnormal mark

[1016] Output: Error notification message and suggested solutions

[1017] In this way, a system has been created that collects and analyzes data at each step, enabling prompt and appropriate action when an abnormality is detected.

[1018] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1019] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1020] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1021] [Third embodiment]

[1022] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1023] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1025] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1026] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1027] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1029] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1030] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1032] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1033] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1034] The present invention provides a system for improving safety during work by having a worker wear a wearable device and monitoring biological information such as heart rate and body temperature in real time. Specific embodiments are described below.

[1035] System configuration

[1036] 1. Wearable devices

[1037] Workers wear a wearable device that contains sensors that detect heart rate and body temperature in real time.

[1038] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[1039] 2. Dedicated app and device

[1040] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[1041] The app has the function of receiving biometric information sent from the wearable device and sending it to a server.

[1042] 3. Server and Generative AI

[1043] The server receives the biometric information sent through a dedicated app.

[1044] The generated AI inside the server analyzes the received biometric information and evaluates the worker's health condition and fatigue level.

[1045] If an abnormality is detected based on the analysis results, the server sends a notification to the worker and the administrator.

[1046] Explanation of program processing

[1047] 1. Data collection and transmission

[1048] Terminal (wearable device)

[1049] The wearable device detects the worker's heart rate and body temperature in real time.

[1050] The detected data is sent to a dedicated app at regular intervals (e.g., every second).

[1051] 2. Data Receipt and Storage

[1052] server

[1053] The server stores the biometric information received via a dedicated app in a database.

[1054] 3. Data Analysis

[1055] server

[1056] The server's generated AI analyzes the worker's health condition and fatigue level in real time based on the received data.

[1057] The analysis includes comparing patterns of heart rate and body temperature with baseline values.

[1058] 4. Anomaly Detection and Notification

[1059] server

[1060] If the analysis results exceed a preset threshold, the server detects an anomaly.

[1061] If an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the terminals of the worker and manager.

[1062] Specific examples

[1063] Worker C's case

[1064] Data collection

[1065] Worker C puts on the wearable device at the construction site and begins work.

[1066] The device detects the heart rate (e.g., 115 beats per minute) and body temperature (e.g., 38.1°C) and sends the data to a server via a dedicated app.

[1067] Data analysis and notification

[1068] server

[1069] The server analyzes the data received from the dedicated app using a generation AI.

[1070] Based on the analysis results, it is detected whether the heart rate and body temperature exceed the set standard values ​​(e.g., heart rate 60-100 beats / min, body temperature 36.0-37.5°C).

[1071] The server generates an abnormality notification message and sends it to the terminals of worker C and the administrator.

[1072] correspondence

[1073] User (Worker C)

[1074] Worker C receives an abnormality notification via the app, stops working, and monitors his / her health.

[1075] Contact a medical institution if necessary.

[1076] administrator

[1077] The manager checks the abnormality notification in the management system and instructs Worker C to take a break and arranges for medical consultation.

[1078] This system allows for real-time monitoring of workers' health conditions and enables rapid response in the event of an abnormality, thereby reducing the risk of accidents during work and ensuring the health and safety of workers.

[1079] The processing flow will be explained below.

[1080] Step 1:

[1081] The user wears the wearable device and performs initial setup using a device with a dedicated app installed. Specifically, the user logs in to their account, sets emergency contacts, and sets alert thresholds.

[1082] Step 2:

[1083] The terminal (wearable device) detects the worker's heart rate and body temperature in real time. This biometric data is recorded periodically (e.g., every second) in its internal memory. The recorded data is then transmitted via wireless communication such as Bluetooth to a terminal with a dedicated app installed.

[1084] Step 3:

[1085] The terminal (a smartphone or tablet with a dedicated app installed) then transmits the biometric data received from the wearable device to a server via the internet using a secure communication protocol.

[1086] Step 4:

[1087] The server receives the biometric data sent from the device, stores the data in a database, and uses it for subsequent analysis.

[1088] Step 5:

[1089] The server uses generative AI to analyze the incoming biometric data in real time, including evaluating the worker's heart rate and body temperature fluctuation patterns and comparing them with pre-set baseline values.

[1090] Step 6:

[1091] The server evaluates the worker's health condition and fatigue level based on the analysis results, and if an abnormality exceeding the reference value is detected, the server marks it as abnormal.

[1092] Step 7:

[1093] If an anomaly is detected, the server immediately generates an anomaly notification message, which includes the specific details of the anomaly, the biometric data causing the anomaly, and recommended countermeasures.

[1094] Step 8:

[1095] The server sends an abnormality notification message to the devices of the user (worker) and administrator. Notifications are sent in multiple ways, including push notifications and email, to enable a quick response.

[1096] Step 9:

[1097] The user (worker) receives an abnormality notification via their device. The user checks the notification and takes immediate action, such as temporarily suspending work. If necessary, they may drink fluids, take a break, or contact a medical institution.

[1098] Step 10:

[1099] The administrator receives an abnormality notification from the server and checks it on the management screen. The administrator monitors the worker's condition and, if necessary, gives specific instructions to the worker, such as instructing them to take a break or seek medical advice.

[1100] This sequence of steps will realize a system that monitors workers' biological information in real time and takes prompt and appropriate action in the event of an abnormality, thereby significantly improving the safety of the work environment and minimizing health risks to workers.

[1101] Example 1

[1102] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1103] While it is important to monitor the health status of workers in real time at workplaces, conventional systems have had difficulty efficiently and reliably monitoring workers' vital signs, such as heart rate and body temperature, making it difficult to respond quickly when an abnormality occurs. Additionally, on-site managers have had few means to receive information in real time and issue instructions for a prompt response.

[1104] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1105] In this invention, the server includes means for detecting the heart rate and body temperature in real time using a wearable device worn by the worker, means for transmitting the detected heart rate and body temperature data to the server via a terminal, means for analyzing the detected data using a generative AI model in the server and evaluating the worker's health condition and fatigue level, and means for notifying the worker and a manager if an abnormality is detected based on the analysis results. This makes it possible to monitor the worker's health condition in real time and to take prompt action if an abnormality occurs.

[1106] A "wearable device" is an electronic device worn by a worker to detect heart rate and body temperature in real time.

[1107] A "terminal" is an electronic device that has the function of receiving data sent from a wearable device and sending it to a server.

[1108] A "server" is a computer system that receives data sent from a terminal and analyzes it using a generative AI model.

[1109] The "generative AI model" is an algorithm that uses artificial intelligence to analyze collected biometric information and evaluate the worker's health condition and fatigue level.

[1110] "Detecting signs" refers to detecting abnormalities in a worker's heart rate or body temperature based on data analyzed by the generative AI model.

[1111] The "abnormality notification means" is a system and method for issuing an alert to workers and administrators when the server detects an abnormality.

[1112] The "analysis results" are the evaluation results regarding health status and fatigue level output by the generative AI model after processing the input data.

[1113] "Reference values" are standard heart rate and body temperature values ​​that are used for comparison when assessing a worker's health condition.

[1114] The present invention provides a system for improving safety during work by having a worker wear a wearable device and monitoring biological information such as heart rate and body temperature in real time. Specific embodiments are described below.

[1115] System configuration

[1116] 1. Wearable devices

[1117] Workers wear a wearable device equipped with sensors that detect heart rate and body temperature in real time.

[1118] The device uses wireless communication methods such as Bluetooth to communicate data with devices on which a dedicated app is installed.

[1119] 2. Dedicated app and device

[1120] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[1121] The app has the ability to receive biometric information sent from wearable devices, such as Apple's iPhone or Samsung's Galaxy series, and send it to a server.

[1122] 3. Server and Generative AI

[1123] The server receives and stores the biometric information sent via a dedicated app. The server can be hosted on Amazon Web Services (AWS) or Google Cloud Platform (GCP).

[1124] The generative AI inside the server analyzes the received biometric information and evaluates the worker's health condition and fatigue level. TensorFlow and PyTorch can be used for the generative AI model.

[1125] The server has a means for sending a notification to the worker and the administrator if an abnormality is detected based on the analysis results.

[1126] Specific examples

[1127] Data collection

[1128] Worker examples

[1129] Workers put on the wearable device at the construction site and begin work.

[1130] The wearable device detects heart rate (e.g., 115 beats per minute) and body temperature (e.g., 38.1°C), and transmits the data via Bluetooth to a device with a dedicated app installed.

[1131] Data transmission and analysis

[1132] Terminal and server operation

[1133] The terminal receives the detected biometric information and periodically transmits it to the server.

[1134] The server analyzes the transmitted data using a generative AI model to determine whether the heart rate and body temperature exceed the reference values ​​(e.g., heart rate 60-100 beats per minute, body temperature 36.0-37.5°C).

[1135] If an abnormality is detected as a result of the analysis, the server generates an abnormality notification message and immediately sends it to the terminals of the worker and manager.

[1136] Response Procedure

[1137] Worker

[1138] Workers will receive notifications of abnormalities via a dedicated app, stop working in a safe place, and monitor their health.

[1139] Contact a medical institution if necessary.

[1140] administrator

[1141] The manager checks the abnormality notification on the management system and instructs the worker to take a break or arranges for medical consultation.

[1142] Example prompts for generative AI models

[1143] Example 1: How can I send a notification when a worker's heart rate exceeds a threshold?

[1144] Example 2: Walk us through the process of creating an AI model using TensorFlow to monitor heart rate and body temperature in real time.

[1145] Example 3: How can you design a database to monitor the health status of workers?

[1146] By utilizing this system, it is possible to monitor the health status of workers in real time and respond quickly to any abnormalities, thereby reducing the risk of accidents during work and ensuring the health and safety of workers.

[1147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1148] System program processing flow

[1149] Step 1:

[1150] Data collection

[1151] Terminal (wearable device)

[1152] The wearable device detects the worker's heart rate and body temperature in real time.

[1153] Input: Worker's biological information (heart rate, body temperature)

[1154] Data processing: Measuring heart rate and body temperature using sensors

[1155] Output: Measured vital signs (heart rate 115 beats / min, body temperature 38.1°C)

[1156] Step 2:

[1157] Data transmission

[1158] Terminal

[1159] Biometric information obtained from the wearable device is sent to a dedicated app via Bluetooth.

[1160] Input: Measured biological information (heart rate 115 beats / min, body temperature 38.1℃)

[1161] Data processing: Preparation for data transmission via Bluetooth communication

[1162] Output: Biometric information packet for transmission

[1163] Step 3:

[1164] Data reception and temporary storage

[1165] Terminal

[1166] The terminal receives biometric information sent from the wearable device through a dedicated app.

[1167] The app temporarily stores the received data in the device's memory.

[1168] Input: Biometric packet for transmission

[1169] Data processing: Data reception and temporary storage

[1170] Output: Temporarily stored vital signs (heart rate 115 beats / min, body temperature 38.1℃)

[1171] Step 4:

[1172] Data transmission and persistent storage

[1173] Terminal

[1174] The terminal transmits data to the server at regular intervals (e.g., every second).

[1175] Input: Temporarily stored biometric information

[1176] Data processing: creating data packets and preparing them for transmission

[1177] Output: Biometric information sent to the server

[1178] server

[1179] The server receives the biometric information sent via a dedicated app.

[1180] The received data is stored in a database. For example, "Heart rate 115 beats per minute, body temperature 38.1°C" is recorded in a MySQL database.

[1181] Input: Biometric information sent to the server

[1182] Data processing: receiving data and inserting it into the database

[1183] Output: Biometric information stored in a database

[1184] Step 5:

[1185] Data analysis

[1186] server

[1187] The server analyzes the received data using a generative AI model.

[1188] The generative AI model compares heart rate and body temperature with baseline values ​​to assess the worker's health and fatigue level in real time.

[1189] Input: Biometric information stored in the database

[1190] Data processing: Analysis using generative AI models (pattern recognition, comparison with reference values)

[1191] Output: Health status and fatigue evaluation results

[1192] Step 6:

[1193] Anomaly Detection

[1194] server

[1195] If the analysis data exceeds a preset reference value, the server detects an anomaly.

[1196] Input: Health status and fatigue assessment results

[1197] Data processing: Abnormality detection by comparing with reference values

[1198] Output: Anomaly detection result (abnormal / no abnormality)

[1199] Step 7:

[1200] Abnormality notification and response

[1201] server

[1202] When an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the worker and the administrator.

[1203] Input: Anomaly detection results

[1204] Data processing: Generation of abnormality notification messages

[1205] Output: Sending an error notification message

[1206] User (operator, administrator)

[1207] Workers will receive notifications of abnormalities via a dedicated app, stop working in a safe place, and monitor their health.

[1208] The manager checks the abnormality notification on the management system and instructs the worker to take a break or arranges for medical consultation.

[1209] (Application example 1)

[1210] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1211] Currently, many factories are using robots, but if the operator's health condition suddenly changes while the robot is operating, there is a risk that the robot will continue working without noticing. This situation not only impairs the operator's health, but can also have a negative impact on work efficiency and safety, so there is a need for a system that can monitor the operator's health condition in real time and respond immediately if an abnormality occurs.

[1212] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1213] In this invention, the server includes a means for transmitting detected heart rate and body temperature data to the server, a means for analyzing the detected data using a generative AI and evaluating the worker's health condition and fatigue level, a means for notifying the worker and manager if an abnormality is detected based on the analysis results, and a means for temporarily suspending machine operation when an abnormality is detected. This makes it possible to understand the operator's health condition in real time and respond immediately when an abnormality occurs, thereby improving safety and efficiency during work.

[1214] A "wearable device" is a device worn by a worker that detects biometric information such as heart rate and body temperature in real time.

[1215] The "heart rate" is a type of biological information that indicates the number of times the heart beats per unit time.

[1216] "Body temperature" is biological information that indicates the internal body temperature of the worker.

[1217] A "server" is a central device that receives and analyzes biometric information sent from a wearable device.

[1218] "Generative AI" is an artificial intelligence model that analyzes received biometric information and evaluates the worker's health condition and fatigue level.

[1219] "Abnormal" refers to a state in which the analysis results exceed a preset reference value.

[1220] An "abnormality notification" is an alarm or message sent to a worker or manager when an abnormality is detected based on the analysis results.

[1221] A "means for temporarily suspending robot operation" is a mechanism or system for temporarily halting machine operation when an abnormality is detected.

[1222] "Worker" refers to a person who wears a wearable device while performing their work.

[1223] The "manager" is the person in charge of monitoring the health status of workers and taking action if any abnormalities occur.

[1224] The present invention improves safety during work by using a system including a wearable device worn by a worker, a transmission means, an analysis means, a notification means, and a means for temporarily suspending machine operation. Detailed embodiments for implementing the present invention will be described below.

[1225] System Configuration

[1226] 1. Wearable devices

[1227] Workers wear a wearable device that contains sensors to detect heart rate and body temperature in real time.

[1228] Wearable devices communicate with devices such as smartphones and tablets using wireless communication technologies such as Bluetooth.

[1229] 2. Terminals and Edge Devices

[1230] Users use a smartphone or tablet with a dedicated app installed.

[1231] The dedicated app has the function of receiving biometric information sent from the wearable device and sending it to a server.

[1232] 3. Server and Generative AI Model

[1233] The server receives the biometric information sent from the wearable device and stores it in a database.

[1234] The generative AI model analyzes the data sent to the server and evaluates the worker's health condition and fatigue level.

[1235] The analysis includes comparing patterns of heart rate and body temperature with baseline values.

[1236] 4. Anomaly Detection and Notification

[1237] Based on the analysis results, if an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the worker and administrator.

[1238] Upon receiving the notification, the manager will instruct appropriate action to ensure the safety of the workers.

[1239] 5. Robot stops working

[1240] When an abnormality is detected, the server sends an instruction to temporarily suspend the robot's operation.

[1241] This function immediately stops machine operation if an abnormality occurs in the worker's health condition, reducing the risk of accidents.

[1242] Program processing explanation

[1243] In implementing the present invention, the following hardware and software are used.

[1244] Hardware:

[1245] Wearable devices: Typical smartwatches (e.g., Fitbit, Apple Watch).

[1246] Server: A cloud-based server (e.g., AWS Lambda, Google Cloud).

[1247] Robot: An automated device used in factories.

[1248] software:

[1249] Programming language: Python

[1250] AI model: A generative AI model using TensorFlow and PyTorch that uses machine learning techniques to analyze biometric data.

[1251] Database: Manage biometric data using MySQL or MongoDB.

[1252] Communication protocol: Data communication between devices is performed using REST API.

[1253] Specific examples

[1254] As an example, let's take Operator A, who works in a factory. Operator A wears a wearable device while working. If the device detects a heart rate of 120 beats per minute and a body temperature of 38.5°C, the server receives this data and analyzes it using a generative AI model. As a result, it is determined that these values ​​exceed the standard values, and an abnormality notification is issued. The server immediately pauses the robot's operation and notifies Operator A and the administrator of the abnormality. This allows Operator A to quickly check his health condition and, if necessary, contact a medical institution.

[1255] Example of an input prompt for a generative AI model:

[1256] Design a program to analyze heart rate and body temperature data collected in real time and generate an abnormality notification if the data exceeds the threshold. Also, add a method to pause the operation of the factory robot based on the abnormality notification. The threshold for heart rate is 60-100 beats per minute, and the threshold for body temperature is 36.0-37.5°C.

[1257]

[1258] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1259] Step 1:

[1260] Data collection

[1261] Before starting work, the user puts on the wearable device, which detects the user's heart rate and body temperature in real time. These values ​​are measured by sensors and temporarily stored inside the wearable device.

[1262] Input: Heart rate and temperature biometric data

[1263] Output: Temporary data in the wearable device

[1264] How it works: Heart rate is detected using an optical sensor, and body temperature is measured using a built-in body temperature sensor.

[1265] Step 2:

[1266] Data transmission

[1267] The wearable device periodically collects biometric data from sensors and transmits it to the user's smartphone or tablet via wireless communication such as Bluetooth. A dedicated app receives the data and transfers it to a server.

[1268] Input: Temporary data in the wearable device

[1269] Output: Data displayed on the dedicated app and sent to the server

[1270] What it does: The device activates its Bluetooth module, pairs with the smartphone, and transmits data. A dedicated app receives this data in real time and forwards it to the server via an HTTP request.

[1271] Step 3:

[1272] analysis

[1273] The server stores the received data in a database, then analyzes the biometric data using a generative AI model. Specifically, it compares heart rate and body temperature values ​​with baseline values ​​to determine whether there are any abnormalities.

[1274] Input: Biometric data received by the server

[1275] Output: Analysis result (normal / abnormal)

[1276] Specific operations: Connect to a database and save data to storage. A generative AI model is used to analyze the saved data. This model is built using TensorFlow and PyTorch and performs calculations to detect outliers.

[1277] Step 4:

[1278] Anomaly Detection and Notification

[1279] If an abnormality is detected, the server generates an abnormality notification message based on the analysis results of the AI. This message is sent to workers and managers via a dedicated app.

[1280] Input: Analysis results of the generative AI model

[1281] Output: Error notification message

[1282] Specific operation: If an abnormal value is detected, the server generates a notification message and sends a push notification to the worker's smartphone or the manager's device.

[1283] Step 5:

[1284] Robot stops working

[1285] If the server detects an abnormality, it sends an instruction to the robots in the factory to stop operating. This instruction is transmitted to the robots via the control system.

[1286] Input: Analysis results of the generative AI model

[1287] Output: Robot stops moving

[1288] Specific operation: The server sends a stop command to the robot control system via an HTTP request, etc. The control system receives this command and stops the robot's operation.

[1289] Example prompt sentence:

[1290] Design a program to analyze heart rate and body temperature data collected in real time and generate an abnormality notification if the data exceeds the threshold. Also, add a method to pause the operation of the factory robot based on the abnormality notification. The threshold for heart rate is 60-100 beats per minute, and the threshold for body temperature is 36.0-37.5°C.

[1291] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1292] The present invention is a system that uses a wearable device worn by a worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work. Specific embodiments are described below.

[1293] System configuration

[1294] 1. Wearable devices

[1295] Workers wear a wearable device that includes sensors to detect heart rate and body temperature in real time, as well as a microphone and camera for emotion recognition.

[1296] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[1297] 2. Dedicated app and device

[1298] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[1299] The app has the function of receiving biometric information and emotional data sent from the wearable device and sending it to a server.

[1300] 3. Server, Generative AI and Emotion Engine

[1301] The server receives the biometric information and emotional data sent through a dedicated app.

[1302] The generated AI inside the server analyzes the received biometric data and evaluates the worker's health condition and fatigue level.

[1303] The emotion engine analyzes the emotion data received from the wearable device and assesses the worker's emotional state.

[1304] If an abnormality is detected based on the analysis results, the server sends a notification to the worker and the administrator.

[1305] Explanation of program processing

[1306] 1. Data collection and transmission

[1307] Terminal (wearable device)

[1308] The wearable device detects the worker's heart rate and body temperature in real time, and also uses a microphone and camera to collect voice and facial expression data.

[1309] The detected biometric and emotional data is sent to a dedicated app at regular intervals (e.g., every second).

[1310] 2. Data Receipt and Storage

[1311] server

[1312] The server stores the biometric and emotional data received via a dedicated app in a database.

[1313] 3. Data Analysis

[1314] server

[1315] The server's generated AI analyzes the worker's health condition and fatigue level in real time based on the received biometric data.

[1316] The emotion engine analyzes the received emotion data and evaluates the worker's emotional state, which includes changes in tone of voice and facial expressions.

[1317] 4. Overall evaluation and anomaly detection

[1318] server

[1319] The server makes a comprehensive judgment based on the results of biometric data analysis by the generation AI and the emotional state evaluation results by the emotion engine.

[1320] Based on the overall evaluation, if an abnormality exceeding the reference value is detected, the server marks it as abnormal.

[1321] 5. Abnormality notification and response suggestions

[1322] server

[1323] If an anomaly is detected, the server immediately generates an anomaly notification message, which includes the specific details of the anomaly, the data causing the anomaly, and recommended actions to take.

[1324] The server sends an abnormality notification message to the terminals of the user (worker) and the administrator.

[1325] Specific examples

[1326] Worker D's case

[1327] Data collection

[1328] Worker D puts on the wearable device at the construction site and begins work.

[1329] The device detects elevated heart rate (e.g., 120 beats per minute), body temperature (e.g., 37.8°C), and voice tone, and transmits this data to a server via a dedicated app.

[1330] Data analysis and abnormality notification

[1331] server

[1332] The server uses AI to analyze and check the data received from the dedicated app, detecting whether the heart rate and body temperature exceed the standard values ​​(heart rate 60-100 beats per minute, body temperature 36.0-37.5°C).

[1333] The emotion engine analyzes the tone of voice as being different from normal and assesses that the worker is feeling stressed.

[1334] The server integrates these analysis results and determines that there is something wrong with worker D's health condition.

[1335] The server generates an abnormality notification message and sends it to the terminals of worker D and the administrator.

[1336] correspondence

[1337] User (Worker D)

[1338] Worker D receives an abnormality notification via the app, stops working, takes a break, and contacts a medical institution if necessary.

[1339] administrator

[1340] The administrator checks the abnormality notification from the server on the management screen, reviews the status of worker D, and, if necessary, issues instructions for a break or arranges for him to be examined at a medical institution.

[1341] In this way, a system can be built that monitors workers' biometric information and emotional state in real time and responds quickly in the event of an abnormality, significantly improving the safety of the work environment and minimizing health risks for workers.

[1342] The processing flow will be explained below.

[1343] Step 1:

[1344] The user wears the wearable device and performs initial setup using a device with a dedicated app installed, including logging in to their account, setting emergency contacts, and setting alert thresholds.

[1345] Step 2:

[1346] The terminal (wearable device) detects the worker's heart rate and body temperature in real time, and also uses the microphone and camera built into the wearable device to collect emotional data such as voice and facial expressions in real time.

[1347] Step 3:

[1348] The terminal (wearable device) transmits the collected heart rate, body temperature, and emotional data to a dedicated app at regular intervals (e.g., every second).

[1349] Step 4:

[1350] The device (a smartphone or tablet with a dedicated app installed) then transmits the received biometric and emotional data to a server via the Internet.

[1351] Step 5:

[1352] The server receives the biometric data and emotion data sent from the terminal and stores them in a database.

[1353] Step 6:

[1354] The server's generation AI analyzes the received biometric data and evaluates the worker's health condition and fatigue level in real time, while the emotion engine analyzes the received emotional data and evaluates the worker's emotional state.

[1355] Step 7:

[1356] Based on the analysis results, the server makes a comprehensive judgment of the heart rate, body temperature, and emotional data, and if it detects any abnormalities that exceed the standard values, it marks them as abnormal.

[1357] Step 8:

[1358] If the server detects an anomaly that exceeds the threshold, it immediately generates an anomaly notification message that includes the nature of the anomaly, the data that caused it, and a recommended course of action.

[1359] Step 9:

[1360] The server sends an abnormality notification message to the devices of the user (worker) and administrator. Notifications are sent in multiple ways, including push notifications and email, to enable a quick response.

[1361] Step 10:

[1362] The user (worker) receives an abnormality notification via their terminal. After checking the notification, the user temporarily stops their work and takes necessary measures, such as drinking water, taking a break, or contacting a medical institution.

[1363] Step 11:

[1364] The administrator checks the abnormality notification from the server on the management screen and monitors the worker's condition. If necessary, the administrator can instruct workers to take a break, seek medical advice, or take other measures.

[1365] In this way, the system of the present invention monitors the worker's biometric information and emotional state in real time, and takes prompt and appropriate action in the event of an abnormality, thereby ensuring the safety and health of the worker.

[1366] Example 2

[1367] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1368] Conventional technologies exist that monitor only a worker's biometric information, such as heart rate and body temperature, in real time. However, these systems are unable to adequately assess the worker's psychological stress and fatigue due to emotional changes, making it difficult to fully grasp the worker's overall health status. As a result, there are limitations to improving safety and health management during work. Furthermore, the difficulty of responding quickly when an abnormality occurs can have a negative impact on work efficiency and safety.

[1369] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting emotion data using a wearable device that detects heart rate and body temperature in real time, means for evaluating health status and fatigue level using a generation AI, means for evaluating the emotional state using an emotion engine, and means for notifying the worker and manager when an abnormality is detected. This allows for a comprehensive evaluation of the worker's biometric information and emotional state, and enables rapid response when an abnormality is detected in real time.

[1370] A "worker" is a person who works at a work site while wearing a wearable device.

[1371] A "wearable device" is a device that can be worn by a worker and is equipped with sensors and input devices for detecting biometric and emotional data such as heart rate, body temperature, voice, and facial expressions in real time.

[1372] "Heart rate" refers to data indicating the number of times the worker's heart beats per unit time.

[1373] "Body temperature" refers to data indicating the worker's body temperature.

[1374] "Emotion data" is data obtained from voice and facial expressions, and includes information for evaluating the emotional state of the worker.

[1375] "Server" is a general term for a computer system that receives, analyzes, and stores biometric and emotional data sent through a dedicated app.

[1376] "Generative AI" is an artificial intelligence technology that analyzes received biometric data and evaluates the health status and fatigue level of workers.

[1377] An "emotion engine" is an algorithm or software that analyzes emotional data such as voice and facial expressions to evaluate the emotional state of a worker.

[1378] "Analysis results" refer to the evaluation results of the worker's health and emotional state obtained by the generative AI and emotion engine.

[1379] The "notification means" refers to a function or device that notifies the worker or manager of an abnormality when it is detected based on the analysis results.

[1380] "Supervisor" refers to the person in charge of managing the safety and health of workers at the work site.

[1381] This invention is a system that uses a wearable device worn by the worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work.

[1382] System configuration

[1383] 1. Wearable devices

[1384] Workers wear a wearable device that includes sensors to detect heart rate and body temperature in real time, as well as a microphone and camera for emotion recognition. The wearable device communicates with a terminal on which a dedicated app is installed using wireless communication methods such as Bluetooth.

[1385] 2. Dedicated app and device

[1386] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed. The app has the function of receiving biometric and emotional data sent from the wearable device and sending it to a server.

[1387] 3. Server, Generative AI and Emotion Engine

[1388] The server receives biometric information and emotional data sent through a dedicated app. The generation AI inside the server analyzes the received biometric data and evaluates the worker's health condition and fatigue level. The emotion engine analyzes the emotional data received from the wearable device and evaluates the worker's emotional state. If the server detects an abnormality based on the analysis results, it sends a notification to the worker and manager.

[1389] Specific actions

[1390] Example: Worker D's case

[1391] Data collection

[1392] Worker D puts on the wearable device at the construction site and begins work. The device detects rising heart rate (e.g., 120 beats per minute), body temperature (e.g., 37.8°C), and voice tone. This data is sent to the server via a dedicated app.

[1393] Data analysis and abnormality notification

[1394] The server uses the generation AI to analyze the heart rate and body temperature. It confirms that they exceed the standard values ​​(a heart rate of 60-100 beats per minute and a body temperature of 36.0-37.5°C are standard values). The emotion engine analyzes the tone of the voice data received and determines that the worker is in a state of stress. The server combines these results and determines that worker D's health and emotional state are abnormal. It creates an abnormality notification message and immediately sends it to the devices of worker D and the manager, including specific details of the abnormality, the cause, and countermeasures.

[1395] correspondence

[1396] Worker D receives the abnormality notification via the app, stops working, and takes a break. If necessary, he or she contacts a medical institution. The administrator receives the abnormality notification from the server and checks the status of Worker D. The administrator instructs the appropriate countermeasures and ensures the safety of Worker D.

[1397] An example of a prompt for this system is:

[1398] This system monitors workers' health and emotional state in real time using heart rate, body temperature, voice tone, and facial expression data obtained from wearable devices worn by workers. If an abnormality is detected, such as a worker feeling stressed or their body temperature exceeding the normal range, a notification is sent immediately. In this way, the system improves the safety of the work environment and minimizes health risks to workers.

[1399] In this way, a system can be built that monitors workers' biometric information and emotional state in real time and responds quickly in the event of an abnormality, significantly improving the safety of the work environment and minimizing health risks for workers.

[1400] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1401] Step 1: Data collection and transmission

[1402] Terminal (wearable device)

[1403] The wearable device measures the worker's heart rate and body temperature in real time. It also uses a built-in microphone and camera to capture the worker's voice and facial expressions. The collected biometric and emotional data is sent via Bluetooth to a device (smartphone or tablet) with a dedicated app installed at regular intervals (e.g., every second).

[1404] Input: Heart rate, body temperature, voice data, facial expression data

[1405] Data processing / calculation: Measurement by sensor, recording by microphone and camera

[1406] Output: Biometric and emotional data sent to a dedicated app

[1407] Step 2: Receiving and storing data

[1408] server

[1409] The server receives biometric and emotional data sent from the dedicated app in real time. The received data is structured and stored in a database. Each piece of data is assigned the worker's ID and a timestamp.

[1410] Input: Biometric data and emotional data sent from a dedicated app

[1411] Data processing / calculation: Receiving data, assigning ID and time stamp

[1412] Output: Biometric and emotional data stored in a database

[1413] Step 3: Data analysis

[1414] server

[1415] The generative AI analyzes received biometric data such as heart rate and body temperature to assess the worker's health and fatigue level. The emotion engine analyzes collected voice tone and facial expression data to assess the worker's emotional state. Each analysis result is stored in a database.

[1416] Input: Heart rate, body temperature, voice tone, and facial expression data obtained from the database

[1417] Data processing / calculation: Analysis of health status and fatigue level using generative AI, analysis of emotional state using emotion engine

[1418] Output: Analysis results (health status, fatigue level, emotional state)

[1419] Step 4: Overall evaluation and anomaly detection

[1420] server

[1421] The server combines the results of the biometric data analysis by the generative AI with the results of the emotional state evaluation by the emotion engine to make a comprehensive evaluation. If the analysis results exceed a preset threshold, the system will mark the person as abnormal.

[1422] Input: Analysis results of generative AI and emotion engine

[1423] Data processing / calculation: Integration of analysis results, comparison with reference values

[1424] Output: Overall evaluation result marked as abnormal (exceeding the standard value)

[1425] Step 5: Notification of abnormalities and suggested actions

[1426] server

[1427] When an abnormality is detected in the overall evaluation, the server generates an abnormality notification message. This message contains the specific details of the abnormality, the data that caused it, and recommended countermeasures. The abnormality notification message is sent to the terminals of the worker and the manager.

[1428] Input: Overall evaluation result marked as abnormal

[1429] Data processing / calculation: Generation of error notification messages

[1430] Output: Anomaly notification message sent to the terminals of the worker and manager

[1431] In this way, specific data processing and calculations are performed at each step, making it possible to monitor the worker's health and emotional state in real time and respond quickly in the event of an abnormality.

[1432] (Application example 2)

[1433] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1434] In conventional work environments, systems exist that manage the health status of workers by continuously monitoring biometric information such as heart rate and body temperature, but these systems often overlook health risks and declines in work efficiency due to changes in emotional state. Furthermore, there is a lack of real-time responses and suggestions for appropriate countermeasures when abnormalities are detected, which has resulted in insufficient improvements in worker safety and productivity. Therefore, the present invention aims to provide a system that comprehensively monitors a worker's biometric information and emotional state and suggests appropriate countermeasures when an abnormality is detected.

[1435] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1436] In this invention, the server includes means for detecting the heart rate and body temperature in real time using a wearable device worn by the worker, means for transmitting the detected heart rate and body temperature data to the server, means for analyzing the detected data using a generative AI and evaluating the worker's health condition and fatigue level, means for notifying the worker and manager when an abnormality is detected, means for collecting and analyzing voice and facial expression data to evaluate the worker's emotional state using an emotion engine, and means for the robot to notify the worker of the abnormality and suggest measures to take, such as taking a break or seeing a doctor. This makes it possible to comprehensively monitor and analyze the worker's health and emotional state, and to take prompt and appropriate action when an abnormality occurs.

[1437] A "wearable device" is a device worn by a worker that detects biometric information such as heart rate and body temperature in real time.

[1438] "Heart rate" is the number of times the heart beats within a certain period of time (usually one minute), and is an important indicator of the health of a living organism.

[1439] "Body temperature" refers to the internal temperature of an organism, usually measured in degrees Celsius (°C).

[1440] A "server" is a computing device that receives biometric information and emotional data sent from a wearable device and analyzes it in real time.

[1441] "Generative AI" is software that uses machine learning and artificial intelligence techniques to analyze collected biometric information and evaluate a worker's health condition and fatigue level.

[1442] The "emotion engine" is a computer program that analyzes voice and facial expression data to evaluate the emotional state of a worker.

[1443] A "robot" is an automated mechanical device that notifies workers in real time and suggests solutions when an abnormality occurs.

[1444] "Abnormal" refers to a situation in which the analysis results by the server exceed a preset standard value, which may affect the health and safety of workers.

[1445] A "notification" is a warning message sent from the server to an operator or administrator when an abnormality is detected.

[1446] "Countermeasures" are appropriate measures that the robot suggests when an abnormality is detected, such as recommending a break or seeking medical attention.

[1447] The present invention is a system that uses a wearable device worn by a worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work. Specific embodiments are described below.

[1448] System configuration

[1449] 1. Wearable devices

[1450] Workers wear wearable devices equipped with sensors that detect heart rate and body temperature in real time, as well as a microphone and camera to collect voice and facial expression data.

[1451] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[1452] 2. Device and dedicated app

[1453] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[1454] The dedicated app is responsible for receiving biometric information and emotional data sent from the wearable device and sending it to the server.

[1455] 3. Server

[1456] The server receives the biometric information and emotional data sent through a dedicated app.

[1457] The received data is analyzed using a generative AI model to evaluate the worker's health condition and fatigue level.

[1458] The emotion engine analyzes the emotion data received from the wearable device and assesses the worker's emotional state.

[1459] If an abnormality is detected based on the analysis results, the server will notify the worker or administrator of the abnormality and suggest appropriate countermeasures.

[1460] Specific examples

[1461] For example, let us consider a case where Worker A working in a factory has a heart rate of 130 beats per minute and a body temperature of 37.6°C, and the emotion engine recognizes that he is in a "stressed" state.

[1462] 1. Data Collection

[1463] The wearable device detects heart rate and body temperature, and collects voice and facial expression data, which is then sent via Bluetooth to a dedicated app.

[1464] 2. Data Analysis

[1465] The dedicated app transmits biometric information and emotional data to a server.

[1466] The server's generated AI analyzes the received biometric data in real time and evaluates the user's health and fatigue level.

[1467] The emotion engine analyzes the received emotion data and evaluates the emotional state.

[1468] 3. Anomaly Detection and Notification

[1469] If the analysis result exceeds the threshold, the server marks it as abnormal and generates an anomaly notification.

[1470] The server notifies the worker A of the abnormality and also notifies the worker A to take a break, and notifies the manager of the same.

[1471] The robot will suggest specific measures to be taken for Worker A and encourage actions such as recommending a break or seeking medical attention.

[1472] Prompt Sentence Examples

[1473] python

[1474] from health_monitoring import wearables

[1475] from emotion_engine import EmotionRecognizer

[1476] Prompt: Is the worker's heart rate above the reference range (60-100 beats per minute)?

[1477] heart_rate = 130

[1478] thresholds = {

[1479] 'heart_rate': (60, 100)

[1480] }

[1481] is_abnormal = not (thresholds['heart_rate'][0] <= heart_rate <= thresholds['heart_rate'][1])

[1482] print("Abnormal:", is_abnormal) Output: Abnormal: True

[1483] This system enables comprehensive monitoring of workers' biometric information and emotional state, and enables prompt and appropriate response if an abnormality is detected, thereby significantly improving the safety of the work environment and worker health management.

[1484] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1485] Step 1:

[1486] Data collection

[1487] The wearable device detects the worker's heart rate and body temperature in real time. The sensors (heart rate sensor, body temperature sensor) on the wearable device collect data every second, as well as voice and facial expression data. This data is sent to a dedicated app via Bluetooth.

[1488] Input: Biometric data (heart rate, body temperature) and emotional data (voice, facial expression) from wearable devices

[1489] Output: Send data to a dedicated app

[1490] Step 2:

[1491] Data reception and transmission

[1492] The dedicated app receives biometric and emotional data from the wearable device, and then transmits the data to a server.

[1493] Input: Biometric and emotional data from wearable devices

[1494] Output: Send data to the server

[1495] Step 3:

[1496] Biological data analysis

[1497] The server analyzes the biometric data sent from the dedicated app using a generative AI model. Based on heart rate and body temperature data, the server evaluates the worker's health condition and fatigue level. For example, if the heart rate exceeds the normal range (60 to 100 beats per minute), it marks it as abnormal.

[1498] Input: Biometric data (heart rate, body temperature)

[1499] Output: Health status and fatigue evaluation results

[1500] Step 4:

[1501] Emotional Data Analysis

[1502] The server uses an emotion engine to analyze the voice and facial expression data sent from the wearable device and evaluate the worker's emotional state. For example, it can recognize a "stressed" state from changes in voice tone and facial expression.

[1503] Input: Emotion data (voice, facial expression)

[1504] Output: Emotional state evaluation result

[1505] Step 5:

[1506] Overall evaluation and anomaly detection

[1507] The server integrates the health status assessment results from the generative AI model and the emotional status assessment results from the emotion engine to make a comprehensive judgment. If an abnormality is detected, it is marked as an abnormality.

[1508] Input: Health status assessment results, emotional status assessment results

[1509] Output: Overall evaluation result and abnormality mark

[1510] Step 6:

[1511] Abnormality notification and solution suggestions

[1512] If an abnormality is detected, the server generates an abnormality notification message and sends it to the worker and administrator. This message includes information about the worker's health and emotional state, as well as recommended actions to take. In addition, the robot notifies the worker in real time and suggests specific actions to take, such as taking a break or seeking medical attention.

[1513] Input: Overall evaluation result and abnormal mark

[1514] Output: Error notification message and suggested solutions

[1515] In this way, a system has been created that collects and analyzes data at each step, enabling prompt and appropriate action when an abnormality is detected.

[1516] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1517] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1518] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1519] [Fourth embodiment]

[1520] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1521] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1522] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1523] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1524] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1525] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1526] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1527] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1528] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1529] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1530] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1531] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1532] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1533] The present invention provides a system for improving safety during work by having a worker wear a wearable device and monitoring biological information such as heart rate and body temperature in real time. Specific embodiments are described below.

[1534] System configuration

[1535] 1. Wearable devices

[1536] Workers wear a wearable device that contains sensors that detect heart rate and body temperature in real time.

[1537] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[1538] 2. Dedicated app and device

[1539] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[1540] The app has the function of receiving biometric information sent from the wearable device and sending it to a server.

[1541] 3. Server and Generative AI

[1542] The server receives the biometric information sent through a dedicated app.

[1543] The generated AI inside the server analyzes the received biometric information and evaluates the worker's health condition and fatigue level.

[1544] If an abnormality is detected based on the analysis results, the server sends a notification to the worker and the administrator.

[1545] Explanation of program processing

[1546] 1. Data collection and transmission

[1547] Terminal (wearable device)

[1548] The wearable device detects the worker's heart rate and body temperature in real time.

[1549] The detected data is sent to a dedicated app at regular intervals (e.g., every second).

[1550] 2. Data Receipt and Storage

[1551] server

[1552] The server stores the biometric information received via a dedicated app in a database.

[1553] 3. Data Analysis

[1554] server

[1555] The server's generated AI analyzes the worker's health condition and fatigue level in real time based on the received data.

[1556] The analysis includes comparing patterns of heart rate and body temperature with baseline values.

[1557] 4. Anomaly Detection and Notification

[1558] server

[1559] If the analysis results exceed a preset threshold, the server detects an anomaly.

[1560] If an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the terminals of the worker and manager.

[1561] Specific examples

[1562] Worker C's case

[1563] Data collection

[1564] Worker C puts on the wearable device at the construction site and begins work.

[1565] The device detects the heart rate (e.g., 115 beats per minute) and body temperature (e.g., 38.1°C) and sends the data to a server via a dedicated app.

[1566] Data analysis and notification

[1567] server

[1568] The server analyzes the data received from the dedicated app using a generation AI.

[1569] Based on the analysis results, it is detected whether the heart rate and body temperature exceed the set standard values ​​(e.g., heart rate 60-100 beats / min, body temperature 36.0-37.5°C).

[1570] The server generates an abnormality notification message and sends it to the terminals of worker C and the administrator.

[1571] correspondence

[1572] User (Worker C)

[1573] Worker C receives an abnormality notification via the app, stops working, and monitors his / her health.

[1574] Contact a medical institution if necessary.

[1575] administrator

[1576] The manager checks the abnormality notification in the management system and instructs Worker C to take a break and arranges for medical consultation.

[1577] This system allows for real-time monitoring of workers' health conditions and enables rapid response in the event of an abnormality, thereby reducing the risk of accidents during work and ensuring the health and safety of workers.

[1578] The processing flow will be explained below.

[1579] Step 1:

[1580] The user wears the wearable device and performs initial setup using a device with a dedicated app installed. Specifically, the user logs in to their account, sets emergency contacts, and sets alert thresholds.

[1581] Step 2:

[1582] The terminal (wearable device) detects the worker's heart rate and body temperature in real time. This biometric data is recorded periodically (e.g., every second) in its internal memory. The recorded data is then transmitted via wireless communication such as Bluetooth to a terminal with a dedicated app installed.

[1583] Step 3:

[1584] The terminal (a smartphone or tablet with a dedicated app installed) then transmits the biometric data received from the wearable device to a server via the internet using a secure communication protocol.

[1585] Step 4:

[1586] The server receives the biometric data sent from the device, stores the data in a database, and uses it for subsequent analysis.

[1587] Step 5:

[1588] The server uses generative AI to analyze the incoming biometric data in real time, including evaluating the worker's heart rate and body temperature fluctuation patterns and comparing them with pre-set baseline values.

[1589] Step 6:

[1590] The server evaluates the worker's health condition and fatigue level based on the analysis results, and if an abnormality exceeding the reference value is detected, the server marks it as abnormal.

[1591] Step 7:

[1592] If an anomaly is detected, the server immediately generates an anomaly notification message, which includes the specific details of the anomaly, the biometric data causing the anomaly, and recommended countermeasures.

[1593] Step 8:

[1594] The server sends an abnormality notification message to the devices of the user (worker) and administrator. Notifications are sent in multiple ways, including push notifications and email, to enable a quick response.

[1595] Step 9:

[1596] The user (worker) receives an abnormality notification via their device. The user checks the notification and takes immediate action, such as temporarily suspending work. If necessary, they may drink fluids, take a break, or contact a medical institution.

[1597] Step 10:

[1598] The administrator receives an abnormality notification from the server and checks it on the management screen. The administrator monitors the worker's condition and, if necessary, gives specific instructions to the worker, such as instructing them to take a break or seek medical advice.

[1599] This sequence of steps will realize a system that monitors workers' biological information in real time and takes prompt and appropriate action in the event of an abnormality, thereby significantly improving the safety of the work environment and minimizing health risks to workers.

[1600] Example 1

[1601] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1602] While it is important to monitor the health status of workers in real time at workplaces, conventional systems have had difficulty efficiently and reliably monitoring workers' vital signs, such as heart rate and body temperature, making it difficult to respond quickly when an abnormality occurs. Additionally, on-site managers have had few means to receive information in real time and issue instructions for a prompt response.

[1603] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1604] In this invention, the server includes means for detecting the heart rate and body temperature in real time using a wearable device worn by the worker, means for transmitting the detected heart rate and body temperature data to the server via a terminal, means for analyzing the detected data using a generative AI model in the server and evaluating the worker's health condition and fatigue level, and means for notifying the worker and a manager if an abnormality is detected based on the analysis results. This makes it possible to monitor the worker's health condition in real time and to take prompt action if an abnormality occurs.

[1605] A "wearable device" is an electronic device worn by a worker to detect heart rate and body temperature in real time.

[1606] A "terminal" is an electronic device that has the function of receiving data sent from a wearable device and sending it to a server.

[1607] A "server" is a computer system that receives data sent from a terminal and analyzes it using a generative AI model.

[1608] The "generative AI model" is an algorithm that uses artificial intelligence to analyze collected biometric information and evaluate the worker's health condition and fatigue level.

[1609] "Detecting signs" refers to detecting abnormalities in a worker's heart rate or body temperature based on data analyzed by the generative AI model.

[1610] The "abnormality notification means" is a system and method for issuing an alert to workers and administrators when the server detects an abnormality.

[1611] The "analysis results" are the evaluation results regarding health status and fatigue level output by the generative AI model after processing the input data.

[1612] "Reference values" are standard heart rate and body temperature values ​​that are used for comparison when assessing a worker's health condition.

[1613] The present invention provides a system for improving safety during work by having a worker wear a wearable device and monitoring biological information such as heart rate and body temperature in real time. Specific embodiments are described below.

[1614] System configuration

[1615] 1. Wearable devices

[1616] Workers wear a wearable device equipped with sensors that detect heart rate and body temperature in real time.

[1617] The device uses wireless communication methods such as Bluetooth to communicate data with devices on which a dedicated app is installed.

[1618] 2. Dedicated app and device

[1619] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[1620] The app has the ability to receive biometric information sent from wearable devices, such as Apple's iPhone or Samsung's Galaxy series, and send it to a server.

[1621] 3. Server and Generative AI

[1622] The server receives and stores the biometric information sent via a dedicated app. The server can be hosted on Amazon Web Services (AWS) or Google Cloud Platform (GCP).

[1623] The generative AI inside the server analyzes the received biometric information and evaluates the worker's health condition and fatigue level. TensorFlow and PyTorch can be used for the generative AI model.

[1624] The server has a means for sending a notification to the worker and the administrator if an abnormality is detected based on the analysis results.

[1625] Specific examples

[1626] Data collection

[1627] Worker examples

[1628] Workers put on the wearable device at the construction site and begin work.

[1629] The wearable device detects heart rate (e.g., 115 beats per minute) and body temperature (e.g., 38.1°C), and transmits the data via Bluetooth to a device with a dedicated app installed.

[1630] Data transmission and analysis

[1631] Terminal and server operation

[1632] The terminal receives the detected biometric information and periodically transmits it to the server.

[1633] The server analyzes the transmitted data using a generative AI model to determine whether the heart rate and body temperature exceed the reference values ​​(e.g., heart rate 60-100 beats per minute, body temperature 36.0-37.5°C).

[1634] If an abnormality is detected as a result of the analysis, the server generates an abnormality notification message and immediately sends it to the terminals of the worker and manager.

[1635] Response Procedure

[1636] Worker

[1637] Workers will receive notifications of abnormalities via a dedicated app, stop working in a safe place, and monitor their health.

[1638] Contact a medical institution if necessary.

[1639] administrator

[1640] The manager checks the abnormality notification on the management system and instructs the worker to take a break or arranges for medical consultation.

[1641] Example prompts for generative AI models

[1642] Example 1: How can I send a notification when a worker's heart rate exceeds a threshold?

[1643] Example 2: Walk us through the process of creating an AI model using TensorFlow to monitor heart rate and body temperature in real time.

[1644] Example 3: How can you design a database to monitor the health status of workers?

[1645] By utilizing this system, it is possible to monitor the health status of workers in real time and respond quickly to any abnormalities, thereby reducing the risk of accidents during work and ensuring the health and safety of workers.

[1646] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1647] System program processing flow

[1648] Step 1:

[1649] Data collection

[1650] Terminal (wearable device)

[1651] The wearable device detects the worker's heart rate and body temperature in real time.

[1652] Input: Worker's biological information (heart rate, body temperature)

[1653] Data processing: Measuring heart rate and body temperature using sensors

[1654] Output: Measured vital signs (heart rate 115 beats / min, body temperature 38.1°C)

[1655] Step 2:

[1656] Data transmission

[1657] Terminal

[1658] Biometric information obtained from the wearable device is sent to a dedicated app via Bluetooth.

[1659] Input: Measured biological information (heart rate 115 beats / min, body temperature 38.1℃)

[1660] Data processing: Preparation for data transmission via Bluetooth communication

[1661] Output: Biometric information packet for transmission

[1662] Step 3:

[1663] Data reception and temporary storage

[1664] Terminal

[1665] The terminal receives biometric information sent from the wearable device through a dedicated app.

[1666] The app temporarily stores the received data in the device's memory.

[1667] Input: Biometric packet for transmission

[1668] Data processing: Data reception and temporary storage

[1669] Output: Temporarily stored vital signs (heart rate 115 beats / min, body temperature 38.1℃)

[1670] Step 4:

[1671] Data transmission and persistent storage

[1672] Terminal

[1673] The terminal transmits data to the server at regular intervals (e.g., every second).

[1674] Input: Temporarily stored biometric information

[1675] Data processing: creating data packets and preparing them for transmission

[1676] Output: Biometric information sent to the server

[1677] server

[1678] The server receives the biometric information sent via a dedicated app.

[1679] The received data is stored in a database. For example, "Heart rate 115 beats per minute, body temperature 38.1°C" is recorded in a MySQL database.

[1680] Input: Biometric information sent to the server

[1681] Data processing: receiving data and inserting it into the database

[1682] Output: Biometric information stored in a database

[1683] Step 5:

[1684] Data analysis

[1685] server

[1686] The server analyzes the received data using a generative AI model.

[1687] The generative AI model compares heart rate and body temperature with baseline values ​​to assess the worker's health and fatigue level in real time.

[1688] Input: Biometric information stored in the database

[1689] Data processing: Analysis using generative AI models (pattern recognition, comparison with reference values)

[1690] Output: Health status and fatigue evaluation results

[1691] Step 6:

[1692] Anomaly Detection

[1693] server

[1694] If the analysis data exceeds a preset reference value, the server detects an anomaly.

[1695] Input: Health status and fatigue assessment results

[1696] Data processing: Abnormality detection by comparing with reference values

[1697] Output: Anomaly detection result (abnormal / no abnormality)

[1698] Step 7:

[1699] Abnormality notification and response

[1700] server

[1701] When an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the worker and the administrator.

[1702] Input: Anomaly detection results

[1703] Data processing: Generation of abnormality notification messages

[1704] Output: Sending an error notification message

[1705] User (operator, administrator)

[1706] Workers will receive notifications of abnormalities via a dedicated app, stop working in a safe place, and monitor their health.

[1707] The manager checks the abnormality notification on the management system and instructs the worker to take a break or arranges for medical consultation.

[1708] (Application example 1)

[1709] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1710] Currently, many factories are using robots, but if the operator's health condition suddenly changes while the robot is operating, there is a risk that the robot will continue working without noticing. This situation not only impairs the operator's health, but can also have a negative impact on work efficiency and safety, so there is a need for a system that can monitor the operator's health condition in real time and respond immediately if an abnormality occurs.

[1711] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1712] In this invention, the server includes a means for transmitting detected heart rate and body temperature data to the server, a means for analyzing the detected data using a generative AI and evaluating the worker's health condition and fatigue level, a means for notifying the worker and manager if an abnormality is detected based on the analysis results, and a means for temporarily suspending machine operation when an abnormality is detected. This makes it possible to understand the operator's health condition in real time and respond immediately when an abnormality occurs, thereby improving safety and efficiency during work.

[1713] A "wearable device" is a device worn by a worker that detects biometric information such as heart rate and body temperature in real time.

[1714] The "heart rate" is a type of biological information that indicates the number of times the heart beats per unit time.

[1715] "Body temperature" is biological information that indicates the internal body temperature of the worker.

[1716] A "server" is a central device that receives and analyzes biometric information sent from a wearable device.

[1717] "Generative AI" is an artificial intelligence model that analyzes received biometric information and evaluates the worker's health condition and fatigue level.

[1718] "Abnormal" refers to a state in which the analysis results exceed a preset reference value.

[1719] An "abnormality notification" is an alarm or message sent to a worker or manager when an abnormality is detected based on the analysis results.

[1720] A "means for temporarily suspending robot operation" is a mechanism or system for temporarily halting machine operation when an abnormality is detected.

[1721] "Worker" refers to a person who wears a wearable device while performing their work.

[1722] The "manager" is the person in charge of monitoring the health status of workers and taking action if any abnormalities occur.

[1723] The present invention improves safety during work by using a system including a wearable device worn by a worker, a transmission means, an analysis means, a notification means, and a means for temporarily suspending machine operation. Detailed embodiments for implementing the present invention will be described below.

[1724] System Configuration

[1725] 1. Wearable devices

[1726] Workers wear a wearable device that contains sensors to detect heart rate and body temperature in real time.

[1727] Wearable devices communicate with devices such as smartphones and tablets using wireless communication technologies such as Bluetooth.

[1728] 2. Terminals and Edge Devices

[1729] Users use a smartphone or tablet with a dedicated app installed.

[1730] The dedicated app has the function of receiving biometric information sent from the wearable device and sending it to a server.

[1731] 3. Server and Generative AI Model

[1732] The server receives the biometric information sent from the wearable device and stores it in a database.

[1733] The generative AI model analyzes the data sent to the server and evaluates the worker's health condition and fatigue level.

[1734] The analysis includes comparing patterns of heart rate and body temperature with baseline values.

[1735] 4. Anomaly Detection and Notification

[1736] Based on the analysis results, if an abnormality is detected, the server immediately generates an abnormality notification message and sends it to the worker and administrator.

[1737] Upon receiving the notification, the manager will instruct appropriate action to ensure the safety of the workers.

[1738] 5. Robot stops working

[1739] When an abnormality is detected, the server sends an instruction to temporarily suspend the robot's operation.

[1740] This function immediately stops machine operation if an abnormality occurs in the worker's health condition, reducing the risk of accidents.

[1741] Program processing explanation

[1742] In implementing the present invention, the following hardware and software are used.

[1743] Hardware:

[1744] Wearable devices: Typical smartwatches (e.g., Fitbit, Apple Watch).

[1745] Server: A cloud-based server (e.g., AWS Lambda, Google Cloud).

[1746] Robot: An automated device used in factories.

[1747] software:

[1748] Programming language: Python

[1749] AI model: A generative AI model using TensorFlow and PyTorch that uses machine learning techniques to analyze biometric data.

[1750] Database: Manage biometric data using MySQL or MongoDB.

[1751] Communication protocol: Data communication between devices is performed using REST API.

[1752] Specific examples

[1753] As an example, let's take Operator A, who works in a factory. Operator A wears a wearable device while working. If the device detects a heart rate of 120 beats per minute and a body temperature of 38.5°C, the server receives this data and analyzes it using a generative AI model. As a result, it is determined that these values ​​exceed the standard values, and an abnormality notification is issued. The server immediately pauses the robot's operation and notifies Operator A and the administrator of the abnormality. This allows Operator A to quickly check his health condition and, if necessary, contact a medical institution.

[1754] Example of an input prompt for a generative AI model:

[1755] Design a program to analyze heart rate and body temperature data collected in real time and generate an abnormality notification if the data exceeds the threshold. Also, add a method to pause the operation of the factory robot based on the abnormality notification. The threshold for heart rate is 60-100 beats per minute, and the threshold for body temperature is 36.0-37.5°C.

[1756]

[1757] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1758] Step 1:

[1759] Data collection

[1760] Before starting work, the user puts on the wearable device, which detects the user's heart rate and body temperature in real time. These values ​​are measured by sensors and temporarily stored inside the wearable device.

[1761] Input: Heart rate and temperature biometric data

[1762] Output: Temporary data in the wearable device

[1763] How it works: Heart rate is detected using an optical sensor, and body temperature is measured using a built-in body temperature sensor.

[1764] Step 2:

[1765] Data transmission

[1766] The wearable device periodically collects biometric data from sensors and transmits it to the user's smartphone or tablet via wireless communication such as Bluetooth. A dedicated app receives the data and transfers it to a server.

[1767] Input: Temporary data in the wearable device

[1768] Output: Data displayed on the dedicated app and sent to the server

[1769] What it does: The device activates its Bluetooth module, pairs with the smartphone, and transmits data. A dedicated app receives this data in real time and forwards it to the server via an HTTP request.

[1770] Step 3:

[1771] analysis

[1772] The server stores the received data in a database, then analyzes the biometric data using a generative AI model. Specifically, it compares heart rate and body temperature values ​​with baseline values ​​to determine whether there are any abnormalities.

[1773] Input: Biometric data received by the server

[1774] Output: Analysis result (normal / abnormal)

[1775] Specific operations: Connect to a database and save data to storage. A generative AI model is used to analyze the saved data. This model is built using TensorFlow and PyTorch and performs calculations to detect outliers.

[1776] Step 4:

[1777] Anomaly Detection and Notification

[1778] If an abnormality is detected, the server generates an abnormality notification message based on the analysis results of the AI. This message is sent to workers and managers via a dedicated app.

[1779] Input: Analysis results of the generative AI model

[1780] Output: Error notification message

[1781] Specific operation: If an abnormal value is detected, the server generates a notification message and sends a push notification to the worker's smartphone or the manager's device.

[1782] Step 5:

[1783] Robot stops working

[1784] If the server detects an abnormality, it sends an instruction to the robots in the factory to stop operating. This instruction is transmitted to the robots via the control system.

[1785] Input: Analysis results of the generative AI model

[1786] Output: Robot stops moving

[1787] Specific operation: The server sends a stop command to the robot control system via an HTTP request, etc. The control system receives this command and stops the robot's operation.

[1788] Example prompt sentence:

[1789] Design a program to analyze heart rate and body temperature data collected in real time and generate an abnormality notification if the data exceeds the threshold. Also, add a method to pause the operation of the factory robot based on the abnormality notification. The threshold for heart rate is 60-100 beats per minute, and the threshold for body temperature is 36.0-37.5°C.

[1790] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1791] The present invention is a system that uses a wearable device worn by a worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work. Specific embodiments are described below.

[1792] System configuration

[1793] 1. Wearable devices

[1794] Workers wear a wearable device that includes sensors to detect heart rate and body temperature in real time, as well as a microphone and camera for emotion recognition.

[1795] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[1796] 2. Dedicated app and device

[1797] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[1798] The app has the function of receiving biometric information and emotional data sent from the wearable device and sending it to a server.

[1799] 3. Server, Generative AI and Emotion Engine

[1800] The server receives the biometric information and emotional data sent through a dedicated app.

[1801] The generated AI inside the server analyzes the received biometric data and evaluates the worker's health condition and fatigue level.

[1802] The emotion engine analyzes the emotion data received from the wearable device and assesses the worker's emotional state.

[1803] If an abnormality is detected based on the analysis results, the server sends a notification to the worker and the administrator.

[1804] Explanation of program processing

[1805] 1. Data collection and transmission

[1806] Terminal (wearable device)

[1807] The wearable device detects the worker's heart rate and body temperature in real time, and also uses a microphone and camera to collect voice and facial expression data.

[1808] The detected biometric and emotional data is sent to a dedicated app at regular intervals (e.g., every second).

[1809] 2. Data Receipt and Storage

[1810] server

[1811] The server stores the biometric and emotional data received via a dedicated app in a database.

[1812] 3. Data Analysis

[1813] server

[1814] The server's generated AI analyzes the worker's health condition and fatigue level in real time based on the received biometric data.

[1815] The emotion engine analyzes the received emotion data and evaluates the worker's emotional state, which includes changes in tone of voice and facial expressions.

[1816] 4. Overall evaluation and anomaly detection

[1817] server

[1818] The server makes a comprehensive judgment based on the results of biometric data analysis by the generation AI and the emotional state evaluation results by the emotion engine.

[1819] Based on the overall evaluation, if an abnormality exceeding the reference value is detected, the server marks it as abnormal.

[1820] 5. Abnormality notification and response suggestions

[1821] server

[1822] If an anomaly is detected, the server immediately generates an anomaly notification message, which includes the specific details of the anomaly, the data causing the anomaly, and recommended actions to take.

[1823] The server sends an abnormality notification message to the terminals of the user (worker) and the administrator.

[1824] Specific examples

[1825] Worker D's case

[1826] Data collection

[1827] Worker D puts on the wearable device at the construction site and begins work.

[1828] The device detects elevated heart rate (e.g., 120 beats per minute), body temperature (e.g., 37.8°C), and voice tone, and transmits this data to a server via a dedicated app.

[1829] Data analysis and abnormality notification

[1830] server

[1831] The server uses AI to analyze and check the data received from the dedicated app, detecting whether the heart rate and body temperature exceed the standard values ​​(heart rate 60-100 beats per minute, body temperature 36.0-37.5°C).

[1832] The emotion engine analyzes the tone of voice as being different from normal and assesses that the worker is feeling stressed.

[1833] The server integrates these analysis results and determines that there is something wrong with worker D's health condition.

[1834] The server generates an abnormality notification message and sends it to the terminals of worker D and the administrator.

[1835] correspondence

[1836] User (Worker D)

[1837] Worker D receives an abnormality notification via the app, stops working, takes a break, and contacts a medical institution if necessary.

[1838] administrator

[1839] The administrator checks the abnormality notification from the server on the management screen, reviews the status of worker D, and, if necessary, issues instructions for a break or arranges for him to be examined at a medical institution.

[1840] In this way, a system can be built that monitors workers' biometric information and emotional state in real time and responds quickly in the event of an abnormality, significantly improving the safety of the work environment and minimizing health risks for workers.

[1841] The processing flow will be explained below.

[1842] Step 1:

[1843] The user wears the wearable device and performs initial setup using a device with a dedicated app installed, including logging in to their account, setting emergency contacts, and setting alert thresholds.

[1844] Step 2:

[1845] The terminal (wearable device) detects the worker's heart rate and body temperature in real time, and also uses the microphone and camera built into the wearable device to collect emotional data such as voice and facial expressions in real time.

[1846] Step 3:

[1847] The terminal (wearable device) transmits the collected heart rate, body temperature, and emotional data to a dedicated app at regular intervals (e.g., every second).

[1848] Step 4:

[1849] The device (a smartphone or tablet with a dedicated app installed) then transmits the received biometric and emotional data to a server via the Internet.

[1850] Step 5:

[1851] The server receives the biometric data and emotion data sent from the terminal and stores them in a database.

[1852] Step 6:

[1853] The server's generation AI analyzes the received biometric data and evaluates the worker's health condition and fatigue level in real time, while the emotion engine analyzes the received emotional data and evaluates the worker's emotional state.

[1854] Step 7:

[1855] Based on the analysis results, the server makes a comprehensive judgment of the heart rate, body temperature, and emotional data, and if it detects any abnormalities that exceed the standard values, it marks them as abnormal.

[1856] Step 8:

[1857] If the server detects an anomaly that exceeds the threshold, it immediately generates an anomaly notification message that includes the nature of the anomaly, the data that caused it, and a recommended course of action.

[1858] Step 9:

[1859] The server sends an abnormality notification message to the devices of the user (worker) and administrator. Notifications are sent in multiple ways, including push notifications and email, to enable a quick response.

[1860] Step 10:

[1861] The user (worker) receives an abnormality notification via their terminal. After checking the notification, the user temporarily stops their work and takes necessary measures, such as drinking water, taking a break, or contacting a medical institution.

[1862] Step 11:

[1863] The administrator checks the abnormality notification from the server on the management screen and monitors the worker's condition. If necessary, the administrator can instruct workers to take a break, seek medical advice, or take other measures.

[1864] In this way, the system of the present invention monitors the worker's biometric information and emotional state in real time, and takes prompt and appropriate action in the event of an abnormality, thereby ensuring the safety and health of the worker.

[1865] Example 2

[1866] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1867] Conventional technologies exist that monitor only a worker's biometric information, such as heart rate and body temperature, in real time. However, these systems are unable to adequately assess the worker's psychological stress and fatigue due to emotional changes, making it difficult to fully grasp the worker's overall health status. As a result, there are limitations to improving safety and health management during work. Furthermore, the difficulty of responding quickly when an abnormality occurs can have a negative impact on work efficiency and safety.

[1868] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting emotion data using a wearable device that detects heart rate and body temperature in real time, means for evaluating health status and fatigue level using a generation AI, means for evaluating the emotional state using an emotion engine, and means for notifying the worker and manager when an abnormality is detected. This allows for a comprehensive evaluation of the worker's biometric information and emotional state, and enables rapid response when an abnormality is detected in real time.

[1869] A "worker" is a person who works at a work site while wearing a wearable device.

[1870] A "wearable device" is a device that can be worn by a worker and is equipped with sensors and input devices for detecting biometric and emotional data such as heart rate, body temperature, voice, and facial expressions in real time.

[1871] "Heart rate" refers to data indicating the number of times the worker's heart beats per unit time.

[1872] "Body temperature" refers to data indicating the worker's body temperature.

[1873] "Emotion data" is data obtained from voice and facial expressions, and includes information for evaluating the emotional state of the worker.

[1874] "Server" is a general term for a computer system that receives, analyzes, and stores biometric and emotional data sent through a dedicated app.

[1875] "Generative AI" is an artificial intelligence technology that analyzes received biometric data and evaluates the health status and fatigue level of workers.

[1876] An "emotion engine" is an algorithm or software that analyzes emotional data such as voice and facial expressions to evaluate the emotional state of a worker.

[1877] "Analysis results" refer to the evaluation results of the worker's health and emotional state obtained by the generative AI and emotion engine.

[1878] The "notification means" refers to a function or device that notifies the worker or manager of an abnormality when it is detected based on the analysis results.

[1879] "Supervisor" refers to the person in charge of managing the safety and health of workers at the work site.

[1880] This invention is a system that uses a wearable device worn by the worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work.

[1881] System configuration

[1882] 1. Wearable devices

[1883] Workers wear a wearable device that includes sensors to detect heart rate and body temperature in real time, as well as a microphone and camera for emotion recognition. The wearable device communicates with a terminal on which a dedicated app is installed using wireless communication methods such as Bluetooth.

[1884] 2. Dedicated app and device

[1885] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed. The app has the function of receiving biometric and emotional data sent from the wearable device and sending it to a server.

[1886] 3. Server, Generative AI and Emotion Engine

[1887] The server receives biometric information and emotional data sent through a dedicated app. The generation AI inside the server analyzes the received biometric data and evaluates the worker's health condition and fatigue level. The emotion engine analyzes the emotional data received from the wearable device and evaluates the worker's emotional state. If the server detects an abnormality based on the analysis results, it sends a notification to the worker and manager.

[1888] Specific actions

[1889] Example: Worker D's case

[1890] Data collection

[1891] Worker D puts on the wearable device at the construction site and begins work. The device detects rising heart rate (e.g., 120 beats per minute), body temperature (e.g., 37.8°C), and voice tone. This data is sent to the server via a dedicated app.

[1892] Data analysis and abnormality notification

[1893] The server uses the generation AI to analyze the heart rate and body temperature. It confirms that they exceed the standard values ​​(a heart rate of 60-100 beats per minute and a body temperature of 36.0-37.5°C are standard values). The emotion engine analyzes the tone of the voice data received and determines that the worker is in a state of stress. The server combines these results and determines that worker D's health and emotional state are abnormal. It creates an abnormality notification message and immediately sends it to the devices of worker D and the manager, including specific details of the abnormality, the cause, and countermeasures.

[1894] correspondence

[1895] Worker D receives the abnormality notification via the app, stops working, and takes a break. If necessary, he or she contacts a medical institution. The administrator receives the abnormality notification from the server and checks the status of Worker D. The administrator instructs the appropriate countermeasures and ensures the safety of Worker D.

[1896] An example of a prompt for this system is:

[1897] This system monitors workers' health and emotional state in real time using heart rate, body temperature, voice tone, and facial expression data obtained from wearable devices worn by workers. If an abnormality is detected, such as a worker feeling stressed or their body temperature exceeding the normal range, a notification is sent immediately. In this way, the system improves the safety of the work environment and minimizes health risks to workers.

[1898] In this way, a system can be built that monitors workers' biometric information and emotional state in real time and responds quickly in the event of an abnormality, significantly improving the safety of the work environment and minimizing health risks for workers.

[1899] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1900] Step 1: Data collection and transmission

[1901] Terminal (wearable device)

[1902] The wearable device measures the worker's heart rate and body temperature in real time. It also uses a built-in microphone and camera to capture the worker's voice and facial expressions. The collected biometric and emotional data is sent via Bluetooth to a device (smartphone or tablet) with a dedicated app installed at regular intervals (e.g., every second).

[1903] Input: Heart rate, body temperature, voice data, facial expression data

[1904] Data processing / calculation: Measurement by sensor, recording by microphone and camera

[1905] Output: Biometric and emotional data sent to a dedicated app

[1906] Step 2: Receiving and storing data

[1907] server

[1908] The server receives biometric and emotional data sent from the dedicated app in real time. The received data is structured and stored in a database. Each piece of data is assigned the worker's ID and a timestamp.

[1909] Input: Biometric data and emotional data sent from a dedicated app

[1910] Data processing / calculation: Receiving data, assigning ID and time stamp

[1911] Output: Biometric and emotional data stored in a database

[1912] Step 3: Data analysis

[1913] server

[1914] The generative AI analyzes received biometric data such as heart rate and body temperature to assess the worker's health and fatigue level. The emotion engine analyzes collected voice tone and facial expression data to assess the worker's emotional state. Each analysis result is stored in a database.

[1915] Input: Heart rate, body temperature, voice tone, and facial expression data obtained from the database

[1916] Data processing / calculation: Analysis of health status and fatigue level using generative AI, analysis of emotional state using emotion engine

[1917] Output: Analysis results (health status, fatigue level, emotional state)

[1918] Step 4: Overall evaluation and anomaly detection

[1919] server

[1920] The server combines the results of the biometric data analysis by the generative AI with the results of the emotional state evaluation by the emotion engine to make a comprehensive evaluation. If the analysis results exceed a preset threshold, the system will mark the person as abnormal.

[1921] Input: Analysis results of generative AI and emotion engine

[1922] Data processing / calculation: Integration of analysis results, comparison with reference values

[1923] Output: Overall evaluation result marked as abnormal (exceeding the standard value)

[1924] Step 5: Notification of abnormalities and suggested actions

[1925] server

[1926] When an abnormality is detected in the overall evaluation, the server generates an abnormality notification message. This message contains the specific details of the abnormality, the data that caused it, and recommended countermeasures. The abnormality notification message is sent to the terminals of the worker and the manager.

[1927] Input: Overall evaluation result marked as abnormal

[1928] Data processing / calculation: Generation of error notification messages

[1929] Output: Anomaly notification message sent to the terminals of the worker and manager

[1930] In this way, specific data processing and calculations are performed at each step, making it possible to monitor the worker's health and emotional state in real time and respond quickly in the event of an abnormality.

[1931] (Application example 2)

[1932] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1933] In conventional work environments, systems exist that manage the health status of workers by continuously monitoring biometric information such as heart rate and body temperature, but these systems often overlook health risks and declines in work efficiency due to changes in emotional state. Furthermore, there is a lack of real-time responses and suggestions for appropriate countermeasures when abnormalities are detected, which has resulted in insufficient improvements in worker safety and productivity. Therefore, the present invention aims to provide a system that comprehensively monitors a worker's biometric information and emotional state and suggests appropriate countermeasures when an abnormality is detected.

[1934] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1935] In this invention, the server includes means for detecting the heart rate and body temperature in real time using a wearable device worn by the worker, means for transmitting the detected heart rate and body temperature data to the server, means for analyzing the detected data using a generative AI and evaluating the worker's health condition and fatigue level, means for notifying the worker and manager when an abnormality is detected, means for collecting and analyzing voice and facial expression data to evaluate the worker's emotional state using an emotion engine, and means for the robot to notify the worker of the abnormality and suggest measures to take, such as taking a break or seeing a doctor. This makes it possible to comprehensively monitor and analyze the worker's health and emotional state, and to take prompt and appropriate action when an abnormality occurs.

[1936] A "wearable device" is a device worn by a worker that detects biometric information such as heart rate and body temperature in real time.

[1937] "Heart rate" is the number of times the heart beats within a certain period of time (usually one minute), and is an important indicator of the health of a living organism.

[1938] "Body temperature" refers to the internal temperature of an organism, usually measured in degrees Celsius (°C).

[1939] A "server" is a computing device that receives biometric information and emotional data sent from a wearable device and analyzes it in real time.

[1940] "Generative AI" is software that uses machine learning and artificial intelligence techniques to analyze collected biometric information and evaluate a worker's health condition and fatigue level.

[1941] The "emotion engine" is a computer program that analyzes voice and facial expression data to evaluate the emotional state of a worker.

[1942] A "robot" is an automated mechanical device that notifies workers in real time and suggests solutions when an abnormality occurs.

[1943] "Abnormal" refers to a situation in which the analysis results by the server exceed a preset standard value, which may affect the health and safety of workers.

[1944] A "notification" is a warning message sent from the server to an operator or administrator when an abnormality is detected.

[1945] "Countermeasures" are appropriate measures that the robot suggests when an abnormality is detected, such as recommending a break or seeking medical attention.

[1946] The present invention is a system that uses a wearable device worn by a worker to monitor biometric information such as heart rate and body temperature in real time, and further combines it with an emotion engine to recognize the user's emotions, thereby improving safety and health management during work. Specific embodiments are described below.

[1947] System configuration

[1948] 1. Wearable devices

[1949] Workers wear wearable devices equipped with sensors that detect heart rate and body temperature in real time, as well as a microphone and camera to collect voice and facial expression data.

[1950] Wearable devices communicate with devices that have a dedicated app installed using wireless communication methods such as Bluetooth.

[1951] 2. Device and dedicated app

[1952] Users connect to the wearable device using a smartphone or tablet with a dedicated app installed.

[1953] The dedicated app is responsible for receiving biometric information and emotional data sent from the wearable device and sending it to the server.

[1954] 3. Server

[1955] The server receives the biometric information and emotional data sent through a dedicated app.

[1956] The received data is analyzed using a generative AI model to evaluate the worker's health condition and fatigue level.

[1957] The emotion engine analyzes the emotion data received from the wearable device and assesses the worker's emotional state.

[1958] If an abnormality is detected based on the analysis results, the server will notify the worker or administrator of the abnormality and suggest appropriate countermeasures.

[1959] Specific examples

[1960] For example, let us consider a case where Worker A working in a factory has a heart rate of 130 beats per minute and a body temperature of 37.6°C, and the emotion engine recognizes that he is in a "stressed" state.

[1961] 1. Data Collection

[1962] The wearable device detects heart rate and body temperature, and collects voice and facial expression data, which is then sent via Bluetooth to a dedicated app.

[1963] 2. Data Analysis

[1964] The dedicated app transmits biometric information and emotional data to a server.

[1965] The server's generated AI analyzes the received biometric data in real time and evaluates the user's health and fatigue level.

[1966] The emotion engine analyzes the received emotion data and evaluates the emotional state.

[1967] 3. Anomaly Detection and Notification

[1968] If the analysis result exceeds the threshold, the server marks it as abnormal and generates an anomaly notification.

[1969] The server notifies the worker A of the abnormality and also notifies the worker A to take a break, and notifies the manager of the same.

[1970] The robot will suggest specific measures to be taken for Worker A and encourage actions such as recommending a break or seeking medical attention.

[1971] Prompt Sentence Examples

[1972] python

[1973] from health_monitoring import wearables

[1974] from emotion_engine import EmotionRecognizer

[1975] Prompt: Is the worker's heart rate above the reference range (60-100 beats per minute)?

[1976] heart_rate = 130

[1977] thresholds = {

[1978] 'heart_rate': (60, 100)

[1979] }

[1980] is_abnormal = not (thresholds['heart_rate'][0] <= heart_rate <= thresholds['heart_rate'][1])

[1981] print("Abnormal:", is_abnormal) Output: Abnormal: True

[1982] This system enables comprehensive monitoring of workers' biometric information and emotional state, and enables prompt and appropriate response if an abnormality is detected, thereby significantly improving the safety of the work environment and worker health management.

[1983] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1984] Step 1:

[1985] Data collection

[1986] The wearable device detects the worker's heart rate and body temperature in real time. The sensors (heart rate sensor, body temperature sensor) on the wearable device collect data every second, as well as voice and facial expression data. This data is sent to a dedicated app via Bluetooth.

[1987] Input: Biometric data (heart rate, body temperature) and emotional data (voice, facial expression) from wearable devices

[1988] Output: Send data to a dedicated app

[1989] Step 2:

[1990] Data reception and transmission

[1991] The dedicated app receives biometric and emotional data from the wearable device, and then transmits the data to a server.

[1992] Input: Biometric and emotional data from wearable devices

[1993] Output: Send data to the server

[1994] Step 3:

[1995] Biological data analysis

[1996] The server analyzes the biometric data sent from the dedicated app using a generative AI model. Based on heart rate and body temperature data, the server evaluates the worker's health condition and fatigue level. For example, if the heart rate exceeds the normal range (60 to 100 beats per minute), it marks it as abnormal.

[1997] Input: Biometric data (heart rate, body temperature)

[1998] Output: Health status and fatigue evaluation results

[1999] Step 4:

[2000] Emotional Data Analysis

[2001] The server uses an emotion engine to analyze the voice and facial expression data sent from the wearable device and evaluate the worker's emotional state. For example, it can recognize a "stressed" state from changes in voice tone and facial expression.

[2002] Input: Emotion data (voice, facial expression)

[2003] Output: Emotional state evaluation result

[2004] Step 5:

[2005] Overall evaluation and anomaly detection

[2006] The server integrates the health status assessment results from the generative AI model and the emotional status assessment results from the emotion engine to make a comprehensive judgment. If an abnormality is detected, it is marked as an abnormality.

[2007] Input: Health status assessment results, emotional status assessment results

[2008] Output: Overall evaluation result and abnormality mark

[2009] Step 6:

[2010] Abnormality notification and solution suggestions

[2011] If an abnormality is detected, the server generates an abnormality notification message and sends it to the worker and administrator. This message includes information about the worker's health and emotional state, as well as recommended actions to take. In addition, the robot notifies the worker in real time and suggests specific actions to take, such as taking a break or seeking medical attention.

[2012] Input: Overall evaluation result and abnormal mark

[2013] Output: Error notification message and suggested solutions

[2014] In this way, a system has been created that collects and analyzes data at each step, enabling prompt and appropriate action when an abnormality is detected.

[2015] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2016] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2017] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2018] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2019] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2020] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2021] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2022] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2023] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2024] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2025] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2026] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2027] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2028] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2029] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2030] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2031] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2032] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2033] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2034] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2035] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2036] The following is further disclosed regarding the above embodiment.

[2037] (Claim 1)

[2038] A means for detecting heart rate and body temperature in real time using a wearable device worn by the worker;

[2039] means for transmitting the detected heart rate and body temperature data to a server;

[2040] A means for analyzing the detected data using a generating AI in the server and evaluating the health condition and fatigue level of the worker;

[2041] A means for notifying workers and managers when an abnormality is detected based on the analysis results;

[2042] A system including:

[2043] (Claim 2)

[2044] 2. The system of claim 1, wherein the detected data is transmitted to a server at regular intervals.

[2045] (Claim 3)

[2046] The system according to claim 1, wherein an abnormality is detected when the analysis result exceeds a preset reference value.

[2047] "Example 1"

[2048] (Claim 1)

[2049] A means for detecting heart rate and body temperature in real time using a wearable device worn by the worker;

[2050] means for transmitting the detected heart rate and body temperature data to a server via the terminal;

[2051] A means for analyzing the detected data using a generative AI model in the server and evaluating the health condition and fatigue level of the worker;

[2052] A means for notifying workers and managers when an abnormality is detected based on the analysis results;

[2053] A system including:

[2054] (Claim 2)

[2055] 2. The system of claim 1, wherein the detected data is transmitted to a server at regular intervals.

[2056] (Claim 3)

[2057] The system according to claim 1, wherein an abnormality is detected when the analysis result exceeds a preset reference value.

[2058] "Application Example 1"

[2059] (Claim 1)

[2060] A means for detecting heart rate and body temperature in real time using a wearable device worn by the worker;

[2061] means for transmitting the detected heart rate and body temperature data to a server;

[2062] A means for analyzing the detected data using a generating AI in the server and evaluating the health condition and fatigue level of the worker;

[2063] A means for notifying workers and managers when an abnormality is detected based on the analysis results;

[2064] a means for temporarily suspending machine operation when an abnormality is detected;

[2065] A system including:

[2066] (Claim 2)

[2067] 2. The system of claim 1, wherein the detected data is transmitted to a server at regular intervals.

[2068] (Claim 3)

[2069] 2. The system according to claim 1, wherein if the analysis result exceeds a preset reference value, an abnormality is detected and the operation of the machine is temporarily stopped.

[2070] "Example 2: Combining Emotion Engines"

[2071] (Claim 1)

[2072] A means for detecting heart rate and body temperature in real time using a wearable device worn by the worker;

[2073] means for transmitting the detected heart rate and body temperature data and emotion data to a server;

[2074] A means for analyzing the detected data using a generating AI in the server and evaluating the health condition and fatigue level of the worker;

[2075] means for analyzing the emotion data using an emotion engine and assessing the emotional state of the worker;

[2076] A means for notifying workers and managers when an abnormality is detected based on the analysis results;

[2077] A system including:

[2078] (Claim 2)

[2079] 2. The system of claim 1, wherein the detected data is transmitted to a server at regular intervals.

[2080] (Claim 3)

[2081] The system according to claim 1, wherein an abnormality is detected when the analysis result exceeds a preset reference value.

[2082] "Application example 2 when combining emotion engines"

[2083] (Claim 1)

[2084] A means for detecting heart rate and body temperature in real time using a wearable device worn by the worker;

[2085] means for transmitting the detected heart rate and body temperature data to a server;

[2086] A means for analyzing the detected data using a generating AI in the server and evaluating the health condition and fatigue level of the worker;

[2087] A means for notifying workers and managers when an abnormality is detected based on the analysis results;

[2088] Furthermore, the system includes an emotion engine for recognizing the emotions of the worker, and includes means for collecting and analyzing voice and facial expression data to evaluate the worker's emotional state.

[2089] The robot will notify the worker of any abnormalities and suggest measures to deal with the situation, such as taking a break or seeking medical advice.

[2090] A system including:

[2091] (Claim 2)

[2092] 2. The system of claim 1, wherein the detected data is transmitted to a server at regular intervals.

[2093] (Claim 3)

[2094] The system according to claim 1, wherein an abnormality is detected when the analysis result exceeds a preset reference value. [Explanation of symbols]

[2095] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for detecting heart rate and body temperature in real time using a wearable device worn by the worker; means for transmitting the detected heart rate and body temperature data to a server; A means for analyzing the detected data using a generating AI in the server and evaluating the health condition and fatigue level of the worker; A means for notifying workers and managers when an abnormality is detected based on the analysis results; A system including:

2. 2. The system according to claim 1, wherein the detected data is transmitted to a server at regular intervals.

3. The system according to claim 1, wherein an abnormality is detected when the analysis result exceeds a preset reference value.

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