system

Abstract

Provide a system. [Solution] A means for collecting vital data in real time from a wearable device attached to the player; A means for analyzing the collected vital data and location information and determining abnormal values; means for generating alerts based on the determined anomalies and notifying designated contacts; A means of storing the history of collected data and issued alerts; A system including:

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] Ensuring the health and safety of athletes is becoming increasingly important in the sports industry. However, current systems lack the means to continuously monitor athletes' vital signs in real time and respond immediately when abnormalities are detected. This increases the risk of athletes suffering injuries and serious health problems. Furthermore, delays in appropriate responses can lead to a loss of trust from fans, clubs, and sponsors. A system is needed to improve this situation and maintain the trust of stakeholders while ensuring athletes' safety. [Means for solving the problem]

[0005] The present invention provides a means for collecting vital data in real time from wearable devices worn by players and analyzing the data and location information to identify abnormal values. It also provides a means for generating alerts based on the identified abnormal values ​​and notifying designated contacts. The system includes a means for storing the history of collected data and issued alerts. It also has a function for collecting and analyzing video data of plays and detecting abnormalities in player movements. Alerts are sent in real time to designated contacts, such as coaches, trainers, and doctors, and an application is also provided for them to take appropriate action. In this way, the present invention provides a comprehensive system for monitoring player health and safety in real time and enabling rapid response when abnormalities are detected.

[0006] A "wearable device" is an electronic device that can be worn by an athlete and has the ability to measure vital data such as heart rate, body temperature, and blood pressure in real time.

[0007] "Vital data" refers to biological data that serve as basic indicators of vital activity, such as an athlete's heart rate, body temperature, and blood pressure.

[0008] "Location information" is data that indicates the current coordinate information of a player, and includes GPS data and the like.

[0009] "Analysis" refers to data processing means for identifying abnormal values ​​based on collected data and conducting risk assessments.

[0010] An "outlier" is data indicating vital data or behavior that deviates from the normal range.

[0011] An "alert" is warning information that is generated when an abnormal value is detected, and prompts immediate action.

[0012] "Designated Contacts" refers to coaches, trainers, doctors, and other contacts who are registered in advance to monitor and respond to the athlete's condition.

[0013] "Notification" is a means of sending alerts to designated contacts in real time.

[0014] "Database" refers to a storage system for storing and managing collected vital data, location information, and alert history.

[0015] "Application" refers to software used by coaches, trainers, and physicians to receive real-time notifications and implement appropriate responses.

[0016] "Video data" refers to video data obtained from a video capture device such as a camera, and is used to visually monitor the state of play. [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 relates to a system for collecting, analyzing, and notifying data in real time to ensure the health and safety of athletes. Specific embodiments of this system will be described below.

[0039] Data collection

[0040] 1. Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second. The device transmits the measurement data to a dedicated application via Bluetooth or Wi-Fi.

[0041] 2. Device: Data from the wearable device is received by a dedicated application installed on the athlete's smartphone or tablet. At the same time, the athlete's location information is also collected from the GPS device and sent to the application.

[0042] Data transmission and storage

[0043] 1. Server: Vital data and location information received from the device are sent to the server via the Internet.

[0044] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[0045] Data analysis

[0046] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time to detect abnormal patterns, including comparing it with historical data.

[0047] 2. Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities.

[0048] Alert generation and notification

[0049] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert content includes the type of abnormality and its urgency.

[0050] 2. Server: Generated alerts are sent in real time to pre-designated contacts such as coaches, trainers, and doctors.

[0051] Actual response

[0052] 1. Device: Coaches and trainers who receive the alert can check the notification via an application on their smartphone or tablet.

[0053] 2. User: Based on the notification, take appropriate action on the spot, such as pausing the player's play and directly checking their health status.

[0054] Data logging and management

[0055] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[0056] 2. Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0057] Specific examples

[0058] When Player A is playing a soccer match, the wearable device measures his heart rate every second, and the data is sent to a server via a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player A's heart rate is abnormal. Immediate check is required." The coach checks the notification on his smartphone and takes action to directly check on Player A's condition.

[0059] In this way, the present invention provides a concrete method for implementing a system that ensures the health and safety of athletes and allows for rapid response.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[0063] Step 2:

[0064] Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[0065] Step 3:

[0066] Terminal: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[0067] Step 4:

[0068] Server: Converts the received data into an appropriate format and stores it in the database in order to save it in the database.

[0069] Step 5:

[0070] Server: Runs machine learning algorithms to analyze stored data in real time and detect abnormal patterns.

[0071] Step 6:

[0072] Server: Based on the analysis results, an alert is generated if an abnormal value is detected. The alert includes the type of abnormality and its urgency.

[0073] Step 7:

[0074] Server: Sends generated alerts in real time to designated contacts such as coaches, trainers, and doctors.

[0075] Step 8:

[0076] Device: Displays a notification on the smartphone or tablet of the coach or trainer who received the alert.

[0077] Step 9:

[0078] User: Review the notification and take appropriate action on the spot (e.g., pause play and check on player health).

[0079] Step 10:

[0080] Server: All data and alert history is stored in a database for later analysis and reporting.

[0081] Step 11:

[0082] Users: Log in to the system to view past data and use it to manage athlete performance and health over the long term.

[0083] Example 1

[0084] 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."

[0085] The present invention relates to a system that efficiently collects and analyzes real-time biometric data and location information to ensure the health and safety of athletes. However, current systems have difficulty quickly detecting abnormalities and notifying relevant personnel, which can result in athletes' safety being compromised. Furthermore, the system lacks effective storage and management of past data and warning history, which is another area where improvement is needed.

[0086] 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.

[0087] In this invention, the server includes means for collecting biometric data in real time from wearable devices worn by players, data analysis means for analyzing the collected biometric data and location information and identifying abnormal values, means for generating warnings based on the identified abnormal values ​​and notifying designated contacts, and means for saving the history of the collected data and issued warnings, thereby enabling real-time monitoring of player health and safety and enabling prompt response.

[0088] A "wearable device" is a device that is worn by an athlete to measure biometric data in real time.

[0089] "Biometric data" refers to physiological data used to assess an athlete's health, such as heart rate, body temperature, and blood pressure.

[0090] "Location Information" is data used to measure a player's current location, typically obtained using a GPS device.

[0091] "Data analysis means" refers to algorithms and software used to analyze collected biometric data and location information and identify abnormal values.

[0092] An "alert" is a notification generated when an abnormality related to a player's health or safety is detected based on analyzed data.

[0093] "Designated Contact" refers to the contact person(s) involved in an athlete's well-being, such as a coach, trainer, or medical professional.

[0094] "History" means a record of data collected and warnings issued that is stored for later reference and analysis.

[0095] The present invention relates to a system that uses wearable devices, terminals, and servers to collect, analyze, and notify biometric data and location information in real time to ensure the health and safety of athletes. An embodiment of this system is described in detail below.

[0096] Data collection

[0097] Device: A wearable device worn by the athlete measures biometric data such as heart rate, body temperature, and blood pressure every second. The specific wearable device used is a common wearable device (e.g., fitness monitor or smartwatch). This device transmits the measurement data to a dedicated application using Bluetooth or Wi-Fi.

[0098] Device: Data from the wearable device is received by a dedicated application installed on the athlete's smartphone or tablet. The athlete's location information is also collected from the GPS device and sent to the application.

[0099] Data transmission and storage

[0100] Device: Biometric data and location information stored on the device (smartphone or tablet) is sent to a server via the Internet. A dedicated application uploads the data to the server at pre-set intervals.

[0101] Server: The server stores the received data in a database, typically MySQL (registered trademark) or MongoDB, so that the data can be used for later analysis and historical reference.

[0102] Data analysis

[0103] Server: The server runs machine learning algorithms to analyze the collected biometric data in real time. Specific machine learning frameworks used include Tensorflow (registered trademark) and Scikit-learn. This analysis also includes comparison with past data.

[0104] Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities. Computer vision libraries such as OpenCV are used for video analysis.

[0105] Alert generation and notification

[0106] Server: If an abnormal value is detected based on the analysis results, the server immediately generates an alert, which includes the type of abnormality, such as a sudden increase in heart rate, and the urgency of the alert.

[0107] Server: Generated alerts are sent in real time to pre-designated contacts such as coaches, trainers, medical professionals, etc. Notification methods include SMS, email, and push notifications.

[0108] Actual response

[0109] Device: Coaches and trainers who receive the alert can check the notification through a dedicated application on their smartphone or tablet.

[0110] User: Based on the notification, coaches and trainers take appropriate action on the ground, such as suspending the player and directly checking their health status.

[0111] Data logging and management

[0112] Server: All data and alert history is stored in a database for later detailed analysis and reporting.

[0113] Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0114] Specific examples

[0115] When Player A is playing during a soccer match, the wearable device measures his heart rate every second and sends the data to the player's smartphone via Bluetooth. A dedicated application uploads the data to a server, which analyzes the heart rate data in real time. If the heart rate is abnormally high, a warning is sent to the coach's smartphone stating, "Player A's heart rate is abnormal. Immediate check is required." The coach checks the notification and takes action to directly check Player A's health condition on the spot.

[0116] Prompt sentence for generative AI model

[0117] "Please explain in detail the programming process for the system that analyzes players' health data in real time and sends alerts to coaches if abnormal values ​​are detected."

[0118] Thus, the present invention provides a concrete method for implementing a system that ensures the health and safety of athletes and allows for rapid response.

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] Data measurement by terminal

[0122] A wearable device installed on the terminal measures biometric data (e.g., heart rate, body temperature, blood pressure) every second. The input is the athlete's real-time condition, and the output is the measured biometric data. For example, a fitness monitor measures the athlete's heart rate and temporarily stores it in its internal memory.

[0123] Step 2:

[0124] Data reception by the terminal

[0125] Biometric data from the wearable device is sent to the athlete's smartphone or tablet via Bluetooth or Wi-Fi. The input is the measurement data from the wearable device, and the output is the biometric data received by a dedicated application on the device. The dedicated application organizes and saves this data as analyzed data. At the same time, location information from the GPS device is also received.

[0126] Step 3:

[0127] Data transmission by the terminal

[0128] The biometric data and location information received by the device are sent to a server via the Internet. The input is the measurement data and location information stored on the device, and the output is the data sent to the server. A dedicated application uploads the data to the server at pre-set intervals.

[0129] Step 4:

[0130] Data storage by server

[0131] The server stores the received biometric data and location information in a database. The input is the data sent from the device, and the output is the data stored in the database. Specifically, the data is stored as entries in a MySQL or MongoDB database.

[0132] Step 5:

[0133] Data analysis by server

[0134] The server analyzes the collected biometric data in real time. The input is the biometric data and location information stored in the database, and the output is the analyzed abnormal patterns and outliers. Machine learning algorithms (e.g., TensorFlow, Scikit-learn) are used to detect outliers in real time, and comparisons with past data are also performed.

[0135] Step 6:

[0136] Video data analysis by server

[0137] Video data from the game is sent to a server, where a video analysis algorithm is used to monitor the player's movements. The input is the video data, and the output is the detection result of movement anomalies. Specifically, a computer vision library such as OpenCV is used to perform the movement analysis.

[0138] Step 7:

[0139] Server-generated warnings

[0140] If an abnormal value is detected based on the analysis results, the server will immediately generate a warning. The input is the result of the abnormal value detection, and the output is the generated warning message. For example, a message such as "Player A's heart rate is abnormal. Immediate check required" may be generated.

[0141] Step 8:

[0142] Server warning notification

[0143] The generated alerts are sent in real time to designated contacts (coaches, trainers, medical professionals). The input is the generated alert message and the output is the notification sent to the contact. Notification methods used are SMS, email, and push notifications.

[0144] Step 9:

[0145] Check notifications on your device

[0146] The coach or trainer who receives the warning checks the notification through a dedicated application on their smartphone or tablet. The input is the warning message sent from the server, and the output is the notification displayed on the device. An emergency notification pops up on the coach's device.

[0147] Step 10:

[0148] User action taken

[0149] Based on the notification, coaches and trainers take appropriate action on the spot. The input is a notification displayed on the device, and the output is an action such as checking the player's health status or pausing play. Specifically, the coach takes action on the spot to directly check the player's health status.

[0150] Step 11:

[0151] Data log storage by server

[0152] The collected data and alert history are stored in a database. The input is all the data received and generated, and the output is a history in the database, which can be used for further analysis and reporting at a later date.

[0153] Step 12:

[0154] User confirmation of data

[0155] Athletes and officials log into the system to view past data. The input is historical data stored in a database, and the output is historical data displayed visually through an application or web portal. This allows for long-term management of athletes' performance and health.

[0156] (Application example 1)

[0157] 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."

[0158] The problem that this invention aims to solve is to provide a system for ensuring the health and safety of factory workers in real time. Conventional methods do not collect workers' vital signs or detect abnormal values ​​in real time, which means that even if an abnormality occurs, it is sometimes difficult to respond in a timely manner. Furthermore, when an abnormality is detected, the system is not notified promptly. This increases the risk that the health status of workers will be overlooked, which may lead to reduced productivity or serious accidents.

[0159] 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.

[0160] In this invention, the server includes means for collecting vital data of factory workers from wearable devices and sending it to the server, means for analyzing the data in real time on the server and generating and notifying an alert if an abnormality is detected, and means for saving the history of the collected data and issued alerts. This makes it possible to monitor the health status of factory workers in real time and respond quickly when an abnormality is detected.

[0161] "Athlete" means a person who engages in sports competition or physical activity.

[0162] A "factory worker" refers to a person who works in a factory in the manufacturing or processing industry.

[0163] A "wearable device" is a digital device that can be worn by the user and has the ability to collect vital data, location information, etc.

[0164] "Vital data" refers to data that indicates basic physiological indicators of living organisms, such as heart rate, body temperature, and blood pressure.

[0165] "Real-time" means that data collection and analysis occur in real time.

[0166] "Location information" refers to information that indicates the geographic location of a particular object using technology such as GPS.

[0167] A "server" refers to a computer system that stores data, analyzes data, notifies data, and performs other processes via a network.

[0168] "Analysis" refers to the process of examining collected data using calculations and algorithms.

[0169] An "outlier" refers to abnormal data that deviates from a pre-established standard value.

[0170] An "alert" is a notification generated to warn or alert you when an abnormality is detected.

[0171] "History" refers to a record of data collected and actions taken in the past.

[0172] "Responsible person" refers to a person in a position to manage or supervise a specific task.

[0173] The present invention provides a system for collecting, analyzing, and notifying data in real time to ensure the health and safety of factory workers. A specific embodiment of this system will be described below.

[0174] Data collection

[0175] 1. Terminal: Factory workers wear a wearable device that measures vital data such as heart rate, body temperature, and blood pressure every second. The device transmits the measurement data to a dedicated application using Bluetooth or Wi-Fi.

[0176] 2. Terminal: Data from the wearable device is received by a dedicated application installed on the worker's smartphone or tablet. At the same time, the worker's location information is also collected from the GPS device and sent to the application.

[0177] Data transmission and storage

[0178] 1. Server: Vital data and location information received from the device are sent to the server via the Internet.

[0179] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[0180] Data analysis

[0181] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time to detect abnormal patterns, including comparing it with historical data.

[0182] 2. Server: The server simultaneously receives video data during gameplay and uses a video analysis algorithm to monitor the worker's movements and check for any abnormalities.

[0183] Alert generation and notification

[0184] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert content includes the type of abnormality and its urgency.

[0185] 2. Server: Generated alerts are sent in real time to pre-designated responsible parties.

[0186] Actual response

[0187] 1. Terminal: The person in charge who receives the alert can check the notification through an application on their smartphone or tablet.

[0188] 2. User: Based on the notification, take appropriate action on-site, such as temporarily suspending workers' activities and directly checking their health status.

[0189] Data logging and management

[0190] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[0191] 2. Users: Workers and other stakeholders can log in to the system and check past data, enabling long-term management of worker performance and health status.

[0192] Specific examples

[0193] When Worker A is working in the factory, the wearable device measures his heart rate every second and sends the data to a server using a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the person in charge saying, "Worker A's heart rate is abnormal. Immediate check is required." The person in charge checks the notification on their smartphone and takes action to directly check on Worker A's condition.

[0194] Prompt Sentence Examples

[0195] Design a system to monitor the vital data (heart rate, body temperature, blood pressure) of factory workers in real time and notify supervisors if an abnormality is detected. Write a Python program that includes modules for data collection, transmission, analysis, and alert generation.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1:

[0198] The terminal collects vital data (heart rate, body temperature, blood pressure, etc.) from the wearable device. This data is sent to a dedicated application on the terminal via Bluetooth or Wi-Fi. The input is the vital data obtained in real time from the wearable device, and the output is the vital data being sent to the terminal application.

[0199] Step 2:

[0200] The terminal transmits the vital data and location information received from the wearable device to a server via the Internet. The input is the vital data and location information received by the terminal, and the output is the data being sent to the server. The data is transmitted in an encrypted format to ensure privacy and security.

[0201] Step 3:

[0202] The server stores the vital data and location information received from the device in a database. The input is the vital data and location information sent to the server via the Internet, and the output is that these data are stored in the database. When the data is stored, a timestamp of the data is also recorded.

[0203] Step 4:

[0204] The server analyzes the collected vital data in real time. Specifically, it runs a machine learning algorithm to compare the data with abnormal patterns and detect abnormalities. The input is the vital data stored in the database, and the output is the analysis results. The server also compares the data with past data to determine whether or not there are any abnormalities.

[0205] Step 5:

[0206] If the server detects an abnormal value as a result of real-time analysis, it generates an alert. The input is the analysis result from step 4, and the output is the generated alert. The alert content includes the type of abnormality and its urgency.

[0207] Step 6:

[0208] The server sends the generated alert to the relevant person in real time. The input is the generated alert, and the output is a notification that appears on the person's smartphone or tablet. The notification includes a specific action to be taken.

[0209] Step 7:

[0210] The terminal (smartphone or tablet of the person in charge) receives the alert notification and the person in charge checks it. The input is the alert notification sent from the server, and the output is checking the notification.

[0211] Step 8:

[0212] The user (responsible person) takes action on-site based on the notification content. Specifically, they may temporarily suspend the worker's activities and directly check their health status. The input is the content of the received alert, and the output is the appropriate response action.

[0213] Step 9:

[0214] The server stores all data and alert history in a database for later analysis and reporting. The input is real-time collected data and issued alerts, and the output is historical data stored in the database. The stored data is backed up regularly.

[0215] 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.

[0216] The present invention relates to a system for ensuring the health and safety of athletes, incorporating an emotion engine, and is equipped with functions for collecting data from wearable devices, analyzing the data, generating alerts, and notifying the athletes. A specific embodiment of this system will be described below.

[0217] Data collection

[0218] 1. Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[0219] 2. Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[0220] 3. Device: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[0221] Data transmission and storage

[0222] 1. Server: The vital data and location information received from the device are sent to the server via the Internet.

[0223] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[0224] Data analysis

[0225] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time, detecting abnormal patterns, including comparing it with historical data.

[0226] 2. Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities.

[0227] 3. Server: The emotion engine analyzes the player's vital and behavioral data to assess their emotional state.

[0228] Alert generation and notification

[0229] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert includes the type of abnormality and its urgency.

[0230] 2. Server: The emotion engine evaluates the player's emotional state, and if the emotional state is determined to be abnormal, it generates an alert that takes the emotional state into account.

[0231] 3. Server: Sends generated alerts in real time to designated contacts such as coaches, trainers, and doctors.

[0232] 4. Server: If necessary, analyze the emotional state of coaches, trainers, and doctors and send alerts at the appropriate time.

[0233] Actual response

[0234] 1. Device: Coaches and trainers who receive the alert can check the notification via an application on their smartphone or tablet.

[0235] 2. User: Based on the notification, take appropriate action on the spot (e.g., pause play and check on the health status of players).

[0236] Data logging and management

[0237] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[0238] 2. Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0239] Specific examples

[0240] While Player B is playing a basketball game, the wearable device measures his heart rate every second, and the data is sent to a server via a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player B's heart rate is abnormal. Immediate check is required." Furthermore, if the emotion engine determines that Player B's emotional state is also abnormal, it generates an additional alert based on that. The coach checks the notification on his smartphone and takes action to directly check on Player B's condition. In this way, the present invention provides a specific method for implementing a system that provides real-time data collection, analysis, alert generation, and notification functions to ensure the health and safety of players.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[0244] Step 2:

[0245] Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[0246] Step 3:

[0247] Terminal: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[0248] Step 4:

[0249] Server: The received vital data and location information are converted into an appropriate format and stored in the database.

[0250] Step 5:

[0251] Server: Runs machine learning algorithms to analyze stored vital data in real time and detect abnormal patterns.

[0252] Step 6:

[0253] Server: At the same time, it receives video data during play and uses video analysis algorithms to monitor players' movements and check for any abnormalities.

[0254] Step 7:

[0255] Server: The emotion engine analyzes the player's vital and behavioral data to assess their emotional state.

[0256] Step 8:

[0257] Server: Based on the analysis results and the evaluation of the emotion engine, an alert is generated if an abnormal value is detected. The alert includes the type of abnormality, urgency, and the evaluation result of the emotional state.

[0258] Step 9:

[0259] Server: Generated alerts are sent in real time to designated contacts such as coaches, trainers, and doctors. If necessary, the server also analyzes the emotional state of the coaches, trainers, and doctors and notifies them of the alerts at the appropriate time.

[0260] Step 10:

[0261] Device: Displays a notification on the smartphone or tablet of the coach or trainer who received the alert.

[0262] Step 11:

[0263] User: Review the notification and take appropriate action on the spot (e.g., pause play and check on player health).

[0264] Step 12:

[0265] Server: All data and alert history is stored in a database for later analysis and reporting.

[0266] Step 13:

[0267] Users: Log in to the system to view past data and use it to manage athlete performance and health over the long term.

[0268] Example 2

[0269] 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."

[0270] In recent years, there has been an increasing emphasis on managing the health and safety of athletes in sports. In particular, there is a demand for systems that can monitor athletes' vital signs and behavioral data in real time and detect abnormalities. However, conventional methods can sometimes be slow to detect abnormalities, increasing the risk to athletes' health. Furthermore, there are also issues with athletes and staff not being able to respond appropriately because they are unable to take into account their emotional state. Furthermore, there are very few systems that take into account the emotional state of coaches and trainers and issue alerts at the appropriate time.

[0271] 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 vital data in real time from wearable devices worn by athletes; means for analyzing the collected vital data and location information and identifying abnormal values; means for comparing past data and detecting abnormal patterns in the vital data using a machine learning algorithm; means for generating an alert based on the identified abnormal values ​​and notifying designated contacts; means for saving the history of collected data and issued alerts; and means for evaluating the athletes' emotional states using an emotion engine and detecting abnormalities. This makes it possible to monitor the athletes' health conditions in real time, and to immediately issue alerts and notify relevant parties if an abnormality is detected. Furthermore, taking the athletes' emotional states into consideration allows athletes and staff to respond quickly and appropriately. Furthermore, analyzing the emotional states of coaches and trainers and notifying them at optimal times enables more effective responses.

[0272] A "wearable device" refers to a device worn by an athlete that measures vital data in real time and transmits the data.

[0273] "Vital data" refers to biometric information that indicates an athlete's health status, such as heart rate, body temperature, and blood pressure.

[0274] "Location information" refers to data indicating the player's current location, and is information obtained using technology such as GPS.

[0275] "Analysis" refers to the process of evaluating and analyzing collected data to detect abnormal patterns or characteristics.

[0276] A "machine learning algorithm" refers to a computational method that learns from past data and automatically recognizes patterns in new data.

[0277] An "abnormal pattern" refers to a player's vital or behavioral data that deviates from the normal range and may indicate a health risk.

[0278] An "alert" is a warning notification that is generated when an abnormal value or pattern is detected and is transmitted to designated contacts.

[0279] The "emotion engine" refers to a system that evaluates and judges a player's emotional state by analyzing their vital and behavioral data.

[0280] "Notification" means the act of sending a generated Alert to designated contacts in real time.

[0281] A "database" refers to a system that stores collected data and the history of issued alerts, and manages them so that they can be referenced and analyzed as needed.

[0282] The present invention is a system that combines a wearable device, an analysis algorithm, an emotion engine, and a notification function to ensure the health and safety of athletes. A specific embodiment of this system will be described below.

[0283] Data collection

[0284] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second. This device could be a commercial fitness tracker or sports watch, for example.

[0285] Terminal: The measured data is sent to a dedicated application via Bluetooth or Wi-Fi. The dedicated application is typically a smartphone application.

[0286] Terminal: The dedicated application sends the received vital data and GPS data to a server via the Internet. The GPS device used here can be a smartphone or a dedicated GPS tracker.

[0287] Data transmission and storage

[0288] Server: A cloud-based server is used to process vital data and location information received from the device via the internet.

[0289] Server: The received data is stored in a database, typically an SQL database or a NoSQL database.

[0290] Data analysis

[0291] Server: Runs machine learning algorithms to analyze the vital data stored in the database in real time, using programming languages ​​such as Python and R and machine learning libraries such as TensorFlow and Scikit-learn.

[0292] Server: Applying video analysis algorithms to analyze collected video data in parallel. Specifically, video analysis libraries such as OpenCV and DeepStream are used.

[0293] Server: The emotion engine analyzes the players' vital data and behavioral data to evaluate their emotional state. The emotion engine uses Python NLP libraries and emotion analysis APIs.

[0294] Alert generation and notification

[0295] Server: If an abnormal value is detected based on the analysis results, an alert is generated. This alert includes the type of abnormality and its urgency.

[0296] Server: The emotion engine evaluates the player's emotional state, and if it is determined to be abnormal, it generates an alert that takes the emotional state into account.

[0297] Server: Generated alerts are sent in real time to designated contacts, which may include coaches, trainers, doctors, etc.

[0298] Server: If necessary, analyze the emotional state of coaches, trainers, and doctors and send alerts at the appropriate time.

[0299] Actual response

[0300] Device: Coaches and trainers who receive the alert can check the notification through an application on their smartphone or tablet.

[0301] User: Based on the notification, take appropriate action on the ground—for example, pausing play and directly checking on the player's health status.

[0302] Data logging and management

[0303] Server: All data and alert history is stored in a database for future data analysis and reporting.

[0304] Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0305] Specific examples and prompts for the generative AI model

[0306] 1. Example: When Player B is playing a basketball game, the wearable device measures his heart rate every second, and the data is sent to the server by a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player B's heart rate is abnormal. Immediate check is required." Furthermore, if the emotion engine determines that Player B's emotional state is also abnormal, it generates an additional alert based on that. The coach checks the notification on his smartphone and takes action to directly check on Player B's condition. This system provides real-time data collection, analysis, alert generation, and notification functions to ensure the health and safety of players.

[0307] 2. Generative AI model prompt:

[0308] Using the information below, please describe in detail a program that analyzes player health and generates necessary alerts.

[0309] Wearable devices measure vital data such as heart rate, body temperature, and blood pressure every second.

[0310] Measurement data is received by a dedicated application and sent to a server via the Internet.

[0311] The server stores the data in a database and analyzes it in real time using machine learning algorithms.

[0312] Emotion engine analyzes vital and behavioral data to assess emotional state

[0313] If anomalies or abnormal emotions are detected, alerts are generated and notify coaches and trainers in real time.

[0314] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0315] Step 1:

[0316] Device: A wearable device attached to the player measures vital data such as heart rate, body temperature, and blood pressure every second. Here, sensors acquire each vital data and store it in internal memory. The input is the player's vital signs, and the output is the vital data stored in the device's internal memory.

[0317] Step 2:

[0318] Terminal: The wearable device sends the measured data to a dedicated application via Bluetooth or Wi-Fi. At this time, the data transmission module inside the device is activated and sends the vital data in packet form to the application. The input is the vital data in the device's internal memory, and the output is the data sent to the dedicated application.

[0319] Step 3:

[0320] Terminal: The dedicated application sends the received vital data and GPS data to a server via the Internet. The application receives the data and then sends it to the server using the TCP / IP protocol. The input is the vital data and GPS data received by the dedicated application, and the output is the data sent to the server via the Internet.

[0321] Step 4:

[0322] Server: The vital data and location information received via the Internet are stored in a database. The server buffers the data and then uses an SQL query to store it in the database. The input is the vital data and location information received from the Internet, and the output is the data stored in the database.

[0323] Step 5:

[0324] Server: Executes machine learning algorithms to analyze the vital data stored in the database. The server reads data from the database and performs analysis using Python or R. For example, comparison with past data and time series analysis are used to detect abnormal patterns. The input is the vital data read from the database, and the output is the analysis results.

[0325] Step 6:

[0326] Server: Analyzes the collected video data using a video analysis algorithm. The server receives the video data and analyzes the player's movements using libraries such as OpenCV and DeepStream. The input is the video data, and the output is the analysis result indicating whether or not there are any abnormalities in the player's movements.

[0327] Step 7:

[0328] Server: The emotion engine analyzes the player's vital data and behavioral data to evaluate their emotional state. Here, emotion recognition is performed using an NLP library and emotion analysis API. The input is vital data and behavioral data, and the output is the evaluation result of the player's emotional state.

[0329] Step 8:

[0330] Server: Generates an alert if an abnormal value is detected based on the analysis results. The server evaluates the analysis results and generates an alert message if an abnormality is detected. The input is the analysis results and the output is the generated alert.

[0331] Step 9:

[0332] Server: Sends generated alerts in real time to designated contacts. The server prepares alert messages and sends them to contacts such as coaches, trainers, doctors, etc. The input is the generated alert and the output is the notification sent to the designated contacts.

[0333] Step 10:

[0334] Device: The coach or trainer who receives the alert checks the notification through an application on their smartphone or tablet. The application notifies the user of the alert message via a pop-up display or push notification. The input is the received alert message, and the output is the notification displayed to the user.

[0335] Step 11:

[0336] User: Based on the notification content, take appropriate action on the spot. For example, pause play and directly check the health status of the players. In this case, the user follows the instructions within the application to check and take action in real time. The input is the notification content, and the output is the action taken.

[0337] Step 12:

[0338] Server: All data and alert history is stored in a database for later analysis and reporting. The server adds new data and manages history. The input is newly acquired data and alert history, and the output is an updated database.

[0339] Step 13:

[0340] Users: Athletes and related personnel log in to the system to check past data. This enables long-term health management and performance analysis. The input is past data, and the output is displayed data.

[0341] (Application example 2)

[0342] 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."

[0343] It is difficult to monitor the health and safety of factory workers in real time, and there is a high risk of serious accidents occurring due to poor health or stress while working. In particular, conventional systems have been slow to collect and analyze vital data, making it difficult to respond quickly. Another issue is that they are unable to properly assess workers' emotional state and provide support at the appropriate time.

[0344] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data in real time from the wearable devices worn by the athletes, means for analyzing the collected vital data and location information and identifying abnormal values, and means for generating an alert based on the identified abnormal values ​​and notifying designated contacts. This enables health and safety management of factory workers.

[0345] A "wearable device" is a device that is worn on the body and collects vital data in real time.

[0346] "Vital data" refers to physiological data that indicates the health status of the human body, such as heart rate, body temperature, and blood pressure.

[0347] "Location Information" means data on the current geographic location of athletes and workers, obtained using a GPS device or the like.

[0348] An "emotion analysis engine" is a system that analyzes vital data and behavioral data to assess a subject's emotional state.

[0349] An "abnormal value" is data such as vital data or location information that deviates from the normal range and indicates a physical or mental abnormality.

[0350] An "alert" is a warning message that is generated when an abnormal value is detected and sent to designated contacts.

[0351] "Contacts" are people such as coaches, trainers, doctors, etc. who are set to receive alerts and their contact methods.

[0352] "Factory worker health and safety management" is a process that monitors the health and safety of factory workers in real time and prompts rapid response when abnormalities are detected.

[0353] A "database" is a system for storing collected data and the history of issued alerts.

[0354] The present invention is a system for monitoring the health and safety of factory workers in real time, and can be implemented as follows.

[0355] Hardware and Software Configuration

[0356] Hardware:

[0357] Wearable devices: Worn by factory workers to measure vital data such as heart rate, body temperature, and blood pressure.

[0358] Communication device: Uses Bluetooth or Wi-Fi to transmit data from the wearable device to a dedicated application.

[0359] Server: A processing device that analyzes collected data and detects abnormal values.

[0360] software:

[0361] Dedicated application: Sends data collected from the wearable device to a server.

[0362] Data analysis software: Using the Python libraries pandas and IsolationForest, the collected vital data is analyzed to detect anomalies.

[0363] Sentiment Analysis Engine: Uses Hugging Face transformers to assess the emotional state of factory workers from text data.

[0364] Data collection and transmission

[0365] The terminal collects vital data in real time from wearable devices worn by factory workers. The collected data is sent to a dedicated application via Bluetooth or Wi-Fi, and then transmitted over the Internet to a server. The server stores the collected data in a database.

[0366] Analyzing data and generating alerts

[0367] The server analyzes the received vital data and location information in real time to detect abnormal patterns. This analysis is performed using the machine learning algorithm IsolationForest. If an abnormality is detected, an alert is generated and notified to designated contacts in real time. In addition, an emotion analysis engine is used to evaluate the emotional state of factory workers, and if an abnormality is detected, an emotion-based alert is also generated.

[0368] Alert notification and response

[0369] Alerts generated by the server are sent in real time to designated coaches, trainers, medical staff, etc. Users who receive the notifications can check the content of the notifications through an application on their smartphone or tablet and take necessary action, such as temporarily halting work and directly checking the health status of factory workers.

[0370] Specific examples

[0371] As a concrete example, consider a situation where Worker A is performing welding work in a factory. At this time, a wearable device collects data that Worker A's heart rate exceeds 150 and his body temperature exceeds 38 degrees. This data is sent to a server via a dedicated application, which analyzes it and detects abnormalities. If an abnormality is detected, an alert is sent to the administrator stating, "Worker A's heart rate and body temperature are abnormal. Immediate check is required." The emotion analysis engine then analyzes the text data, "I'm feeling very stressed out," and an additional emotion alert is generated.

[0372] Prompt Sentence Examples

[0373] While welding, Worker A's heart rate exceeds 150 and his body temperature exceeds 38°C. The system determines this to be an abnormality and sends an emergency alert to the administrator.

[0374] Worker B enters the text "I'm feeling very stressed out" into the system. The emotion analysis engine determines this as a high-stress state and generates an alert.

[0375] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0376] Step 1:

[0377] The terminal collects real-time vital data (heart rate, body temperature, blood pressure) from wearable devices worn by factory workers. The collected data is measured every second. The input data is heart rate, body temperature, and blood pressure, and the output is sent to a dedicated application via Bluetooth or Wi-Fi.

[0378] Step 2:

[0379] The device sends the collected vital data to a dedicated application via Bluetooth or Wi-Fi. The application then sends the data to a server via the Internet. The input data is the vital data, and the output is the raw data sent to the server.

[0380] Step 3:

[0381] The server stores the received vital data in a database. The input is the raw data sent via the internet, and the output is the data stored in the database. The database also contains historical data.

[0382] Step 4:

[0383] The server analyzes the collected data in real time and uses the Isolation Forest algorithm to detect abnormal patterns. The input is the latest vital data stored in the database, and the output is a judgment result on whether the value is abnormal or not.

[0384] Step 5:

[0385] The server uses an emotion analysis engine to evaluate the emotional state of the factory workers. The input is the text data of the factory workers, and the output is the evaluation result of the emotional state. Hugging Face Transformers are used for emotion analysis.

[0386] Step 6:

[0387] The server generates alerts based on the results of anomaly analysis and emotion analysis. The inputs are the anomaly detection results and the emotional state evaluation results, and the output is the generated alert. The alert includes the type of anomaly and its urgency.

[0388] Step 7:

[0389] The server notifies designated contacts (such as administrators or medical staff) of generated alerts in real time. The input is the generated alert and the output is the notification sent to the contact.

[0390] Step 8:

[0391] Users (administrators or medical staff) check the notification content through a smartphone or tablet application and take the necessary action. The input is the notification content, and the output is specific actions such as checking the health status of factory workers or suspending work.

[0392] 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.

[0393] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0394] 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.

[0395] [Second embodiment]

[0396] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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."

[0408] The present invention relates to a system for collecting, analyzing, and notifying data in real time to ensure the health and safety of athletes. Specific embodiments of this system will be described below.

[0409] Data collection

[0410] 1. Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second. The device transmits the measurement data to a dedicated application via Bluetooth or Wi-Fi.

[0411] 2. Device: Data from the wearable device is received by a dedicated application installed on the athlete's smartphone or tablet. At the same time, the athlete's location information is also collected from the GPS device and sent to the application.

[0412] Data transmission and storage

[0413] 1. Server: Vital data and location information received from the device are sent to the server via the Internet.

[0414] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[0415] Data analysis

[0416] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time to detect abnormal patterns, including comparing it with historical data.

[0417] 2. Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities.

[0418] Alert generation and notification

[0419] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert content includes the type of abnormality and its urgency.

[0420] 2. Server: Generated alerts are sent in real time to pre-designated contacts such as coaches, trainers, and doctors.

[0421] Actual response

[0422] 1. Device: Coaches and trainers who receive the alert can check the notification via an application on their smartphone or tablet.

[0423] 2. User: Based on the notification, take appropriate action on the spot, such as pausing the player's play and directly checking their health status.

[0424] Data logging and management

[0425] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[0426] 2. Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0427] Specific examples

[0428] When Player A is playing a soccer match, the wearable device measures his heart rate every second, and the data is sent to a server via a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player A's heart rate is abnormal. Immediate check is required." The coach checks the notification on his smartphone and takes action to directly check on Player A's condition.

[0429] In this way, the present invention provides a concrete method for implementing a system that ensures the health and safety of athletes and allows for rapid response.

[0430] The processing flow will be explained below.

[0431] Step 1:

[0432] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[0433] Step 2:

[0434] Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[0435] Step 3:

[0436] Terminal: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[0437] Step 4:

[0438] Server: Converts the received data into an appropriate format and stores it in the database in order to save it in the database.

[0439] Step 5:

[0440] Server: Runs machine learning algorithms to analyze stored data in real time and detect abnormal patterns.

[0441] Step 6:

[0442] Server: Based on the analysis results, an alert is generated if an abnormal value is detected. The alert includes the type of abnormality and its urgency.

[0443] Step 7:

[0444] Server: Sends generated alerts in real time to designated contacts such as coaches, trainers, and doctors.

[0445] Step 8:

[0446] Device: Displays a notification on the smartphone or tablet of the coach or trainer who received the alert.

[0447] Step 9:

[0448] User: Review the notification and take appropriate action on the spot (e.g., pause play and check on player health).

[0449] Step 10:

[0450] Server: All data and alert history is stored in a database for later analysis and reporting.

[0451] Step 11:

[0452] Users: Log in to the system to view past data and use it to manage athlete performance and health over the long term.

[0453] Example 1

[0454] 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."

[0455] The present invention relates to a system that efficiently collects and analyzes real-time biometric data and location information to ensure the health and safety of athletes. However, current systems have difficulty quickly detecting abnormalities and notifying relevant personnel, which can result in athletes' safety being compromised. Furthermore, the system lacks effective storage and management of past data and warning history, which is another area where improvement is needed.

[0456] 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.

[0457] In this invention, the server includes means for collecting biometric data in real time from wearable devices worn by players, data analysis means for analyzing the collected biometric data and location information and identifying abnormal values, means for generating warnings based on the identified abnormal values ​​and notifying designated contacts, and means for saving the history of the collected data and issued warnings, thereby enabling real-time monitoring of player health and safety and enabling prompt response.

[0458] A "wearable device" is a device that is worn by an athlete to measure biometric data in real time.

[0459] "Biometric data" refers to physiological data used to assess an athlete's health, such as heart rate, body temperature, and blood pressure.

[0460] "Location Information" is data used to measure a player's current location, typically obtained using a GPS device.

[0461] "Data analysis means" refers to algorithms and software used to analyze collected biometric data and location information and identify abnormal values.

[0462] An "alert" is a notification generated when an abnormality related to a player's health or safety is detected based on analyzed data.

[0463] "Designated Contact" refers to the contact person(s) involved in an athlete's well-being, such as a coach, trainer, or medical professional.

[0464] "History" means a record of data collected and warnings issued that is stored for later reference and analysis.

[0465] The present invention relates to a system that uses wearable devices, terminals, and servers to collect, analyze, and notify biometric data and location information in real time to ensure the health and safety of athletes. An embodiment of this system is described in detail below.

[0466] Data collection

[0467] Device: A wearable device worn by the athlete measures biometric data such as heart rate, body temperature, and blood pressure every second. The specific wearable device used is a common wearable device (e.g., fitness monitor or smartwatch). This device transmits the measurement data to a dedicated application using Bluetooth or Wi-Fi.

[0468] Device: Data from the wearable device is received by a dedicated application installed on the athlete's smartphone or tablet. The athlete's location information is also collected from the GPS device and sent to the application.

[0469] Data transmission and storage

[0470] Device: Biometric data and location information stored on the device (smartphone or tablet) is sent to a server via the Internet. A dedicated application uploads the data to the server at pre-set intervals.

[0471] Server: The server stores the received data in a database, typically MySQL or MongoDB, so that the data can be used for later analysis and historical reference.

[0472] Data analysis

[0473] Server: The server runs machine learning algorithms to analyze the collected biometric data in real time. Specific machine learning frameworks used are TensorFlow and Scikit-learn. This analysis includes comparison with past data.

[0474] Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities. Computer vision libraries such as OpenCV are used for video analysis.

[0475] Alert generation and notification

[0476] Server: If an abnormal value is detected based on the analysis results, the server immediately generates an alert, which includes the type of abnormality, such as a sudden increase in heart rate, and the urgency of the alert.

[0477] Server: Generated alerts are sent in real time to pre-designated contacts such as coaches, trainers, medical professionals, etc. Notification methods include SMS, email, and push notifications.

[0478] Actual response

[0479] Device: Coaches and trainers who receive the alert can check the notification through a dedicated application on their smartphone or tablet.

[0480] User: Based on the notification, coaches and trainers take appropriate action on the ground, such as suspending the player and directly checking their health status.

[0481] Data logging and management

[0482] Server: All data and alert history is stored in a database for later detailed analysis and reporting.

[0483] Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0484] Specific examples

[0485] When Player A is playing during a soccer match, the wearable device measures his heart rate every second and sends the data to the player's smartphone via Bluetooth. A dedicated application uploads the data to a server, which analyzes the heart rate data in real time. If the heart rate is abnormally high, a warning is sent to the coach's smartphone stating, "Player A's heart rate is abnormal. Immediate check is required." The coach checks the notification and takes action to directly check Player A's health condition on the spot.

[0486] Prompt sentence for generative AI model

[0487] "Please explain in detail the programming process for the system that analyzes players' health data in real time and sends alerts to coaches if abnormal values ​​are detected."

[0488] Thus, the present invention provides a concrete method for implementing a system that ensures the health and safety of athletes and allows for rapid response.

[0489] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0490] Step 1:

[0491] Data measurement by terminal

[0492] A wearable device installed on the terminal measures biometric data (e.g., heart rate, body temperature, blood pressure) every second. The input is the athlete's real-time condition, and the output is the measured biometric data. For example, a fitness monitor measures the athlete's heart rate and temporarily stores it in its internal memory.

[0493] Step 2:

[0494] Data reception by the terminal

[0495] Biometric data from the wearable device is sent to the athlete's smartphone or tablet via Bluetooth or Wi-Fi. The input is the measurement data from the wearable device, and the output is the biometric data received by a dedicated application on the device. The dedicated application organizes and saves this data as analyzed data. At the same time, location information from the GPS device is also received.

[0496] Step 3:

[0497] Data transmission by the terminal

[0498] The biometric data and location information received by the device are sent to a server via the Internet. The input is the measurement data and location information stored on the device, and the output is the data sent to the server. A dedicated application uploads the data to the server at pre-set intervals.

[0499] Step 4:

[0500] Data storage by server

[0501] The server stores the received biometric data and location information in a database. The input is the data sent from the device, and the output is the data stored in the database. Specifically, the data is stored as entries in a MySQL or MongoDB database.

[0502] Step 5:

[0503] Data analysis by server

[0504] The server analyzes the collected biometric data in real time. The input is the biometric data and location information stored in the database, and the output is the analyzed abnormal patterns and outliers. Machine learning algorithms (e.g., TensorFlow, Scikit-learn) are used to detect outliers in real time, and comparisons with past data are also performed.

[0505] Step 6:

[0506] Video data analysis by server

[0507] Video data from the game is sent to a server, where a video analysis algorithm is used to monitor the player's movements. The input is the video data, and the output is the detection result of movement anomalies. Specifically, a computer vision library such as OpenCV is used to perform the movement analysis.

[0508] Step 7:

[0509] Server-generated warnings

[0510] If an abnormal value is detected based on the analysis results, the server will immediately generate a warning. The input is the result of the abnormal value detection, and the output is the generated warning message. For example, a message such as "Player A's heart rate is abnormal. Immediate check required" may be generated.

[0511] Step 8:

[0512] Server warning notification

[0513] The generated alerts are sent in real time to designated contacts (coaches, trainers, medical professionals). The input is the generated alert message and the output is the notification sent to the contact. Notification methods used are SMS, email, and push notifications.

[0514] Step 9:

[0515] Check notifications on your device

[0516] The coach or trainer who receives the warning checks the notification through a dedicated application on their smartphone or tablet. The input is the warning message sent from the server, and the output is the notification displayed on the device. An emergency notification pops up on the coach's device.

[0517] Step 10:

[0518] User action taken

[0519] Based on the notification, coaches and trainers take appropriate action on the spot. The input is a notification displayed on the device, and the output is an action such as checking the player's health status or pausing play. Specifically, the coach takes action on the spot to directly check the player's health status.

[0520] Step 11:

[0521] Data log storage by server

[0522] The collected data and alert history are stored in a database. The input is all the data received and generated, and the output is a history in the database, which can be used for further analysis and reporting at a later date.

[0523] Step 12:

[0524] User confirmation of data

[0525] Athletes and officials log into the system to view past data. The input is historical data stored in a database, and the output is historical data displayed visually through an application or web portal. This allows for long-term management of athletes' performance and health.

[0526] (Application example 1)

[0527] 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."

[0528] The problem that this invention aims to solve is to provide a system for ensuring the health and safety of factory workers in real time. Conventional methods do not collect workers' vital signs or detect abnormal values ​​in real time, which means that even if an abnormality occurs, it is sometimes difficult to respond in a timely manner. Furthermore, when an abnormality is detected, the system is not notified promptly. This increases the risk that the health status of workers will be overlooked, which may lead to reduced productivity or serious accidents.

[0529] 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.

[0530] In this invention, the server includes means for collecting vital data of factory workers from wearable devices and sending it to the server, means for analyzing the data in real time on the server and generating and notifying an alert if an abnormality is detected, and means for saving the history of the collected data and issued alerts. This makes it possible to monitor the health status of factory workers in real time and respond quickly when an abnormality is detected.

[0531] "Athlete" means a person who engages in sports competition or physical activity.

[0532] A "factory worker" refers to a person who works in a factory in the manufacturing or processing industry.

[0533] A "wearable device" is a digital device that can be worn by the user and has the ability to collect vital data, location information, etc.

[0534] "Vital data" refers to data that indicates basic physiological indicators of living organisms, such as heart rate, body temperature, and blood pressure.

[0535] "Real-time" means that data collection and analysis occur in real time.

[0536] "Location information" refers to information that indicates the geographic location of a particular object using technology such as GPS.

[0537] A "server" refers to a computer system that stores data, analyzes data, notifies data, and performs other processes via a network.

[0538] "Analysis" refers to the process of examining collected data using calculations and algorithms.

[0539] An "outlier" refers to abnormal data that deviates from a pre-established standard value.

[0540] An "alert" is a notification generated to warn or alert you when an abnormality is detected.

[0541] "History" refers to a record of data collected and actions taken in the past.

[0542] "Responsible person" refers to a person in a position to manage or supervise a specific task.

[0543] The present invention provides a system for collecting, analyzing, and notifying data in real time to ensure the health and safety of factory workers. A specific embodiment of this system will be described below.

[0544] Data collection

[0545] 1. Terminal: Factory workers wear a wearable device that measures vital data such as heart rate, body temperature, and blood pressure every second. The device transmits the measurement data to a dedicated application using Bluetooth or Wi-Fi.

[0546] 2. Terminal: Data from the wearable device is received by a dedicated application installed on the worker's smartphone or tablet. At the same time, the worker's location information is also collected from the GPS device and sent to the application.

[0547] Data transmission and storage

[0548] 1. Server: Vital data and location information received from the device are sent to the server via the Internet.

[0549] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[0550] Data analysis

[0551] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time to detect abnormal patterns, including comparing it with historical data.

[0552] 2. Server: The server simultaneously receives video data during gameplay and uses a video analysis algorithm to monitor the worker's movements and check for any abnormalities.

[0553] Alert generation and notification

[0554] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert content includes the type of abnormality and its urgency.

[0555] 2. Server: Generated alerts are sent in real time to pre-designated responsible parties.

[0556] Actual response

[0557] 1. Terminal: The person in charge who receives the alert can check the notification through an application on their smartphone or tablet.

[0558] 2. User: Based on the notification, take appropriate action on-site, such as temporarily suspending workers' activities and directly checking their health status.

[0559] Data logging and management

[0560] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[0561] 2. Users: Workers and other stakeholders can log in to the system and check past data, enabling long-term management of worker performance and health status.

[0562] Specific examples

[0563] When Worker A is working in the factory, the wearable device measures his heart rate every second and sends the data to a server using a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the person in charge saying, "Worker A's heart rate is abnormal. Immediate check is required." The person in charge checks the notification on their smartphone and takes action to directly check on Worker A's condition.

[0564] Prompt Sentence Examples

[0565] Design a system to monitor the vital data (heart rate, body temperature, blood pressure) of factory workers in real time and notify supervisors if an abnormality is detected. Write a Python program that includes modules for data collection, transmission, analysis, and alert generation.

[0566] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0567] Step 1:

[0568] The terminal collects vital data (heart rate, body temperature, blood pressure, etc.) from the wearable device. This data is sent to a dedicated application on the terminal via Bluetooth or Wi-Fi. The input is the vital data obtained in real time from the wearable device, and the output is the vital data being sent to the terminal application.

[0569] Step 2:

[0570] The terminal transmits the vital data and location information received from the wearable device to a server via the Internet. The input is the vital data and location information received by the terminal, and the output is the data being sent to the server. The data is transmitted in an encrypted format to ensure privacy and security.

[0571] Step 3:

[0572] The server stores the vital data and location information received from the device in a database. The input is the vital data and location information sent to the server via the Internet, and the output is that these data are stored in the database. When the data is stored, a timestamp of the data is also recorded.

[0573] Step 4:

[0574] The server analyzes the collected vital data in real time. Specifically, it runs a machine learning algorithm to compare the data with abnormal patterns and detect abnormalities. The input is the vital data stored in the database, and the output is the analysis results. The server also compares the data with past data to determine whether or not there are any abnormalities.

[0575] Step 5:

[0576] If the server detects an abnormal value as a result of real-time analysis, it generates an alert. The input is the analysis result from step 4, and the output is the generated alert. The alert content includes the type of abnormality and its urgency.

[0577] Step 6:

[0578] The server sends the generated alert to the relevant person in real time. The input is the generated alert, and the output is a notification that appears on the person's smartphone or tablet. The notification includes a specific action to be taken.

[0579] Step 7:

[0580] The terminal (smartphone or tablet of the person in charge) receives the alert notification and the person in charge checks it. The input is the alert notification sent from the server, and the output is checking the notification.

[0581] Step 8:

[0582] The user (responsible person) takes action on-site based on the notification content. Specifically, they may temporarily suspend the worker's activities and directly check their health status. The input is the content of the received alert, and the output is the appropriate response action.

[0583] Step 9:

[0584] The server stores all data and alert history in a database for later analysis and reporting. The input is real-time collected data and issued alerts, and the output is historical data stored in the database. The stored data is backed up regularly.

[0585] 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.

[0586] The present invention relates to a system for ensuring the health and safety of athletes, incorporating an emotion engine, and is equipped with functions for collecting data from wearable devices, analyzing the data, generating alerts, and notifying the athletes. A specific embodiment of this system will be described below.

[0587] Data collection

[0588] 1. Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[0589] 2. Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[0590] 3. Device: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[0591] Data transmission and storage

[0592] 1. Server: The vital data and location information received from the device are sent to the server via the Internet.

[0593] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[0594] Data analysis

[0595] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time, detecting abnormal patterns, including comparing it with historical data.

[0596] 2. Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities.

[0597] 3. Server: The emotion engine analyzes the player's vital and behavioral data to assess their emotional state.

[0598] Alert generation and notification

[0599] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert includes the type of abnormality and its urgency.

[0600] 2. Server: The emotion engine evaluates the player's emotional state, and if the emotional state is determined to be abnormal, it generates an alert that takes the emotional state into account.

[0601] 3. Server: Sends generated alerts in real time to designated contacts such as coaches, trainers, and doctors.

[0602] 4. Server: If necessary, analyze the emotional state of coaches, trainers, and doctors and send alerts at the appropriate time.

[0603] Actual response

[0604] 1. Device: Coaches and trainers who receive the alert can check the notification via an application on their smartphone or tablet.

[0605] 2. User: Based on the notification, take appropriate action on the spot (e.g., pause play and check on the health status of players).

[0606] Data logging and management

[0607] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[0608] 2. Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0609] Specific examples

[0610] While Player B is playing a basketball game, the wearable device measures his heart rate every second, and the data is sent to a server via a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player B's heart rate is abnormal. Immediate check is required." Furthermore, if the emotion engine determines that Player B's emotional state is also abnormal, it generates an additional alert based on that. The coach checks the notification on his smartphone and takes action to directly check on Player B's condition. In this way, the present invention provides a specific method for implementing a system that provides real-time data collection, analysis, alert generation, and notification functions to ensure the health and safety of players.

[0611] The processing flow will be explained below.

[0612] Step 1:

[0613] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[0614] Step 2:

[0615] Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[0616] Step 3:

[0617] Terminal: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[0618] Step 4:

[0619] Server: The received vital data and location information are converted into an appropriate format and stored in the database.

[0620] Step 5:

[0621] Server: Runs machine learning algorithms to analyze stored vital data in real time and detect abnormal patterns.

[0622] Step 6:

[0623] Server: At the same time, it receives video data during play and uses video analysis algorithms to monitor players' movements and check for any abnormalities.

[0624] Step 7:

[0625] Server: The emotion engine analyzes the player's vital and behavioral data to assess their emotional state.

[0626] Step 8:

[0627] Server: Based on the analysis results and the evaluation of the emotion engine, an alert is generated if an abnormal value is detected. The alert includes the type of abnormality, urgency, and the evaluation result of the emotional state.

[0628] Step 9:

[0629] Server: Generated alerts are sent in real time to designated contacts such as coaches, trainers, and doctors. If necessary, the server also analyzes the emotional state of the coaches, trainers, and doctors and notifies them of the alerts at the appropriate time.

[0630] Step 10:

[0631] Device: Displays a notification on the smartphone or tablet of the coach or trainer who received the alert.

[0632] Step 11:

[0633] User: Review the notification and take appropriate action on the spot (e.g., pause play and check on player health).

[0634] Step 12:

[0635] Server: All data and alert history is stored in a database for later analysis and reporting.

[0636] Step 13:

[0637] Users: Log in to the system to view past data and use it to manage athlete performance and health over the long term.

[0638] Example 2

[0639] 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."

[0640] In recent years, there has been an increasing emphasis on managing the health and safety of athletes in sports. In particular, there is a demand for systems that can monitor athletes' vital signs and behavioral data in real time and detect abnormalities. However, conventional methods can sometimes be slow to detect abnormalities, increasing the risk to athletes' health. Furthermore, there are also issues with athletes and staff not being able to respond appropriately because they are unable to take into account their emotional state. Furthermore, there are very few systems that take into account the emotional state of coaches and trainers and issue alerts at the appropriate time.

[0641] 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 vital data in real time from wearable devices worn by athletes; means for analyzing the collected vital data and location information and identifying abnormal values; means for comparing past data and detecting abnormal patterns in the vital data using a machine learning algorithm; means for generating an alert based on the identified abnormal values ​​and notifying designated contacts; means for saving the history of collected data and issued alerts; and means for evaluating the athletes' emotional states using an emotion engine and detecting abnormalities. This makes it possible to monitor the athletes' health conditions in real time, and to immediately issue alerts and notify relevant parties if an abnormality is detected. Furthermore, taking the athletes' emotional states into consideration allows athletes and staff to respond quickly and appropriately. Furthermore, analyzing the emotional states of coaches and trainers and notifying them at optimal times enables more effective responses.

[0642] A "wearable device" refers to a device worn by an athlete that measures vital data in real time and transmits the data.

[0643] "Vital data" refers to biometric information that indicates an athlete's health status, such as heart rate, body temperature, and blood pressure.

[0644] "Location information" refers to data indicating the player's current location, and is information obtained using technology such as GPS.

[0645] "Analysis" refers to the process of evaluating and analyzing collected data to detect abnormal patterns or characteristics.

[0646] A "machine learning algorithm" refers to a computational method that learns from past data and automatically recognizes patterns in new data.

[0647] An "abnormal pattern" refers to a player's vital or behavioral data that deviates from the normal range and may indicate a health risk.

[0648] An "alert" is a warning notification that is generated when an abnormal value or pattern is detected and is transmitted to designated contacts.

[0649] The "emotion engine" refers to a system that evaluates and judges a player's emotional state by analyzing their vital and behavioral data.

[0650] "Notification" means the act of sending a generated Alert to designated contacts in real time.

[0651] A "database" refers to a system that stores collected data and the history of issued alerts, and manages them so that they can be referenced and analyzed as needed.

[0652] The present invention is a system that combines a wearable device, an analysis algorithm, an emotion engine, and a notification function to ensure the health and safety of athletes. A specific embodiment of this system will be described below.

[0653] Data collection

[0654] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second. This device could be a commercial fitness tracker or sports watch, for example.

[0655] Terminal: The measured data is sent to a dedicated application via Bluetooth or Wi-Fi. The dedicated application is typically a smartphone application.

[0656] Terminal: The dedicated application sends the received vital data and GPS data to a server via the Internet. The GPS device used here can be a smartphone or a dedicated GPS tracker.

[0657] Data transmission and storage

[0658] Server: A cloud-based server is used to process vital data and location information received from the device via the internet.

[0659] Server: The received data is stored in a database, typically an SQL database or a NoSQL database.

[0660] Data analysis

[0661] Server: Runs machine learning algorithms to analyze the vital data stored in the database in real time, using programming languages ​​such as Python and R and machine learning libraries such as TensorFlow and Scikit-learn.

[0662] Server: Applying video analysis algorithms to analyze collected video data in parallel. Specifically, video analysis libraries such as OpenCV and DeepStream are used.

[0663] Server: The emotion engine analyzes the players' vital data and behavioral data to evaluate their emotional state. The emotion engine uses Python NLP libraries and emotion analysis APIs.

[0664] Alert generation and notification

[0665] Server: If an abnormal value is detected based on the analysis results, an alert is generated. This alert includes the type of abnormality and its urgency.

[0666] Server: The emotion engine evaluates the player's emotional state, and if it is determined to be abnormal, it generates an alert that takes the emotional state into account.

[0667] Server: Generated alerts are sent in real time to designated contacts, which may include coaches, trainers, doctors, etc.

[0668] Server: If necessary, analyze the emotional state of coaches, trainers, and doctors and send alerts at the appropriate time.

[0669] Actual response

[0670] Device: Coaches and trainers who receive the alert can check the notification through an application on their smartphone or tablet.

[0671] User: Based on the notification, take appropriate action on the ground—for example, pausing play and directly checking on the player's health status.

[0672] Data logging and management

[0673] Server: All data and alert history is stored in a database for future data analysis and reporting.

[0674] Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0675] Specific examples and prompts for the generative AI model

[0676] 1. Example: When Player B is playing a basketball game, the wearable device measures his heart rate every second, and the data is sent to the server by a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player B's heart rate is abnormal. Immediate check is required." Furthermore, if the emotion engine determines that Player B's emotional state is also abnormal, it generates an additional alert based on that. The coach checks the notification on his smartphone and takes action to directly check on Player B's condition. This system provides real-time data collection, analysis, alert generation, and notification functions to ensure the health and safety of players.

[0677] 2. Generative AI model prompt:

[0678] Using the information below, please describe in detail a program that analyzes player health and generates necessary alerts.

[0679] Wearable devices measure vital data such as heart rate, body temperature, and blood pressure every second.

[0680] Measurement data is received by a dedicated application and sent to a server via the Internet.

[0681] The server stores the data in a database and analyzes it in real time using machine learning algorithms.

[0682] Emotion engine analyzes vital and behavioral data to assess emotional state

[0683] If anomalies or abnormal emotions are detected, alerts are generated and notify coaches and trainers in real time.

[0684] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0685] Step 1:

[0686] Device: A wearable device attached to the player measures vital data such as heart rate, body temperature, and blood pressure every second. Here, sensors acquire each vital data and store it in internal memory. The input is the player's vital signs, and the output is the vital data stored in the device's internal memory.

[0687] Step 2:

[0688] Terminal: The wearable device sends the measured data to a dedicated application via Bluetooth or Wi-Fi. At this time, the data transmission module inside the device is activated and sends the vital data in packet form to the application. The input is the vital data in the device's internal memory, and the output is the data sent to the dedicated application.

[0689] Step 3:

[0690] Terminal: The dedicated application sends the received vital data and GPS data to a server via the Internet. The application receives the data and then sends it to the server using the TCP / IP protocol. The input is the vital data and GPS data received by the dedicated application, and the output is the data sent to the server via the Internet.

[0691] Step 4:

[0692] Server: The vital data and location information received via the Internet are stored in a database. The server buffers the data and then uses an SQL query to store it in the database. The input is the vital data and location information received from the Internet, and the output is the data stored in the database.

[0693] Step 5:

[0694] Server: Executes machine learning algorithms to analyze the vital data stored in the database. The server reads data from the database and performs analysis using Python or R. For example, comparison with past data and time series analysis are used to detect abnormal patterns. The input is the vital data read from the database, and the output is the analysis results.

[0695] Step 6:

[0696] Server: Analyzes the collected video data using a video analysis algorithm. The server receives the video data and analyzes the player's movements using libraries such as OpenCV and DeepStream. The input is the video data, and the output is the analysis result indicating whether or not there are any abnormalities in the player's movements.

[0697] Step 7:

[0698] Server: The emotion engine analyzes the player's vital data and behavioral data to evaluate their emotional state. Here, emotion recognition is performed using an NLP library and emotion analysis API. The input is vital data and behavioral data, and the output is the evaluation result of the player's emotional state.

[0699] Step 8:

[0700] Server: Generates an alert if an abnormal value is detected based on the analysis results. The server evaluates the analysis results and generates an alert message if an abnormality is detected. The input is the analysis results and the output is the generated alert.

[0701] Step 9:

[0702] Server: Sends generated alerts in real time to designated contacts. The server prepares alert messages and sends them to contacts such as coaches, trainers, doctors, etc. The input is the generated alert and the output is the notification sent to the designated contacts.

[0703] Step 10:

[0704] Device: The coach or trainer who receives the alert checks the notification through an application on their smartphone or tablet. The application notifies the user of the alert message via a pop-up display or push notification. The input is the received alert message, and the output is the notification displayed to the user.

[0705] Step 11:

[0706] User: Based on the notification content, take appropriate action on the spot. For example, pause play and directly check the health status of the players. In this case, the user follows the instructions within the application to check and take action in real time. The input is the notification content, and the output is the action taken.

[0707] Step 12:

[0708] Server: All data and alert history is stored in a database for later analysis and reporting. The server adds new data and manages history. The input is newly acquired data and alert history, and the output is an updated database.

[0709] Step 13:

[0710] Users: Athletes and related personnel log in to the system to check past data. This enables long-term health management and performance analysis. The input is past data, and the output is displayed data.

[0711] (Application example 2)

[0712] 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."

[0713] It is difficult to monitor the health and safety of factory workers in real time, and there is a high risk of serious accidents occurring due to poor health or stress while working. In particular, conventional systems have been slow to collect and analyze vital data, making it difficult to respond quickly. Another issue is that they are unable to properly assess workers' emotional state and provide support at the appropriate time.

[0714] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data in real time from the wearable devices worn by the athletes, means for analyzing the collected vital data and location information and identifying abnormal values, and means for generating an alert based on the identified abnormal values ​​and notifying designated contacts. This enables health and safety management of factory workers.

[0715] A "wearable device" is a device that is worn on the body and collects vital data in real time.

[0716] "Vital data" refers to physiological data that indicates the health status of the human body, such as heart rate, body temperature, and blood pressure.

[0717] "Location Information" means data on the current geographic location of athletes and workers, obtained using a GPS device or the like.

[0718] An "emotion analysis engine" is a system that analyzes vital data and behavioral data to assess a subject's emotional state.

[0719] An "abnormal value" is data such as vital data or location information that deviates from the normal range and indicates a physical or mental abnormality.

[0720] An "alert" is a warning message that is generated when an abnormal value is detected and sent to designated contacts.

[0721] "Contacts" are people such as coaches, trainers, doctors, etc. who are set to receive alerts and their contact methods.

[0722] "Factory worker health and safety management" is a process that monitors the health and safety of factory workers in real time and prompts rapid response when abnormalities are detected.

[0723] A "database" is a system for storing collected data and the history of issued alerts.

[0724] The present invention is a system for monitoring the health and safety of factory workers in real time, and can be implemented as follows.

[0725] Hardware and Software Configuration

[0726] Hardware:

[0727] Wearable devices: Worn by factory workers to measure vital data such as heart rate, body temperature, and blood pressure.

[0728] Communication device: Uses Bluetooth or Wi-Fi to transmit data from the wearable device to a dedicated application.

[0729] Server: A processing device that analyzes collected data and detects abnormal values.

[0730] software:

[0731] Dedicated application: Sends data collected from the wearable device to a server.

[0732] Data analysis software: Using the Python libraries pandas and IsolationForest, the collected vital data is analyzed to detect anomalies.

[0733] Sentiment Analysis Engine: Uses Hugging Face transformers to assess the emotional state of factory workers from text data.

[0734] Data collection and transmission

[0735] The terminal collects vital data in real time from wearable devices worn by factory workers. The collected data is sent to a dedicated application via Bluetooth or Wi-Fi, and then transmitted over the Internet to a server. The server stores the collected data in a database.

[0736] Analyzing data and generating alerts

[0737] The server analyzes the received vital data and location information in real time to detect abnormal patterns. This analysis is performed using the machine learning algorithm IsolationForest. If an abnormality is detected, an alert is generated and notified to designated contacts in real time. In addition, an emotion analysis engine is used to evaluate the emotional state of factory workers, and if an abnormality is detected, an emotion-based alert is also generated.

[0738] Alert notification and response

[0739] Alerts generated by the server are sent in real time to designated coaches, trainers, medical staff, etc. Users who receive the notifications can check the content of the notifications through an application on their smartphone or tablet and take necessary action, such as temporarily halting work and directly checking the health status of factory workers.

[0740] Specific examples

[0741] As a concrete example, consider a situation where Worker A is performing welding work in a factory. At this time, a wearable device collects data that Worker A's heart rate exceeds 150 and his body temperature exceeds 38 degrees. This data is sent to a server via a dedicated application, which analyzes it and detects abnormalities. If an abnormality is detected, an alert is sent to the administrator stating, "Worker A's heart rate and body temperature are abnormal. Immediate check is required." The emotion analysis engine then analyzes the text data, "I'm feeling very stressed out," and an additional emotion alert is generated.

[0742] Prompt Sentence Examples

[0743] While welding, Worker A's heart rate exceeds 150 and his body temperature exceeds 38°C. The system determines this to be an abnormality and sends an emergency alert to the administrator.

[0744] Worker B enters the text "I'm feeling very stressed out" into the system. The emotion analysis engine determines this as a high-stress state and generates an alert.

[0745] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0746] Step 1:

[0747] The terminal collects real-time vital data (heart rate, body temperature, blood pressure) from wearable devices worn by factory workers. The collected data is measured every second. The input data is heart rate, body temperature, and blood pressure, and the output is sent to a dedicated application via Bluetooth or Wi-Fi.

[0748] Step 2:

[0749] The device sends the collected vital data to a dedicated application via Bluetooth or Wi-Fi. The application then sends the data to a server via the Internet. The input data is the vital data, and the output is the raw data sent to the server.

[0750] Step 3:

[0751] The server stores the received vital data in a database. The input is the raw data sent via the internet, and the output is the data stored in the database. The database also contains historical data.

[0752] Step 4:

[0753] The server analyzes the collected data in real time and uses the Isolation Forest algorithm to detect abnormal patterns. The input is the latest vital data stored in the database, and the output is a judgment result on whether the value is abnormal or not.

[0754] Step 5:

[0755] The server uses an emotion analysis engine to evaluate the emotional state of the factory workers. The input is the text data of the factory workers, and the output is the evaluation result of the emotional state. Hugging Face Transformers are used for emotion analysis.

[0756] Step 6:

[0757] The server generates alerts based on the results of anomaly analysis and emotion analysis. The inputs are the anomaly detection results and the emotional state evaluation results, and the output is the generated alert. The alert includes the type of anomaly and its urgency.

[0758] Step 7:

[0759] The server notifies designated contacts (such as administrators or medical staff) of generated alerts in real time. The input is the generated alert and the output is the notification sent to the contact.

[0760] Step 8:

[0761] Users (administrators or medical staff) check the notification content through a smartphone or tablet application and take the necessary action. The input is the notification content, and the output is specific actions such as checking the health status of factory workers or suspending work.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] [Third embodiment]

[0766] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0767] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0768] 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).

[0769] 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.

[0770] 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.

[0771] 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).

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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."

[0778] The present invention relates to a system for collecting, analyzing, and notifying data in real time to ensure the health and safety of athletes. Specific embodiments of this system will be described below.

[0779] Data collection

[0780] 1. Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second. The device transmits the measurement data to a dedicated application via Bluetooth or Wi-Fi.

[0781] 2. Device: Data from the wearable device is received by a dedicated application installed on the athlete's smartphone or tablet. At the same time, the athlete's location information is also collected from the GPS device and sent to the application.

[0782] Data transmission and storage

[0783] 1. Server: Vital data and location information received from the device are sent to the server via the Internet.

[0784] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[0785] Data analysis

[0786] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time to detect abnormal patterns, including comparing it with historical data.

[0787] 2. Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities.

[0788] Alert generation and notification

[0789] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert content includes the type of abnormality and its urgency.

[0790] 2. Server: Generated alerts are sent in real time to pre-designated contacts such as coaches, trainers, and doctors.

[0791] Actual response

[0792] 1. Device: Coaches and trainers who receive the alert can check the notification via an application on their smartphone or tablet.

[0793] 2. User: Based on the notification, take appropriate action on the spot, such as pausing the player's play and directly checking their health status.

[0794] Data logging and management

[0795] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[0796] 2. Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0797] Specific examples

[0798] When Player A is playing a soccer match, the wearable device measures his heart rate every second, and the data is sent to a server via a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player A's heart rate is abnormal. Immediate check is required." The coach checks the notification on his smartphone and takes action to directly check on Player A's condition.

[0799] In this way, the present invention provides a concrete method for implementing a system that ensures the health and safety of athletes and allows for rapid response.

[0800] The processing flow will be explained below.

[0801] Step 1:

[0802] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[0803] Step 2:

[0804] Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[0805] Step 3:

[0806] Terminal: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[0807] Step 4:

[0808] Server: Converts the received data into an appropriate format and stores it in the database in order to save it in the database.

[0809] Step 5:

[0810] Server: Runs machine learning algorithms to analyze stored data in real time and detect abnormal patterns.

[0811] Step 6:

[0812] Server: Based on the analysis results, an alert is generated if an abnormal value is detected. The alert includes the type of abnormality and its urgency.

[0813] Step 7:

[0814] Server: Sends generated alerts in real time to designated contacts such as coaches, trainers, and doctors.

[0815] Step 8:

[0816] Device: Displays a notification on the smartphone or tablet of the coach or trainer who received the alert.

[0817] Step 9:

[0818] User: Review the notification and take appropriate action on the spot (e.g., pause play and check on player health).

[0819] Step 10:

[0820] Server: All data and alert history is stored in a database for later analysis and reporting.

[0821] Step 11:

[0822] Users: Log in to the system to view past data and use it to manage athlete performance and health over the long term.

[0823] Example 1

[0824] 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."

[0825] The present invention relates to a system that efficiently collects and analyzes real-time biometric data and location information to ensure the health and safety of athletes. However, current systems have difficulty quickly detecting abnormalities and notifying relevant personnel, which can result in athletes' safety being compromised. Furthermore, the system lacks effective storage and management of past data and warning history, which is another area where improvement is needed.

[0826] 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.

[0827] In this invention, the server includes means for collecting biometric data in real time from wearable devices worn by players, data analysis means for analyzing the collected biometric data and location information and identifying abnormal values, means for generating warnings based on the identified abnormal values ​​and notifying designated contacts, and means for saving the history of the collected data and issued warnings, thereby enabling real-time monitoring of player health and safety and enabling prompt response.

[0828] A "wearable device" is a device that is worn by an athlete to measure biometric data in real time.

[0829] "Biometric data" refers to physiological data used to assess an athlete's health, such as heart rate, body temperature, and blood pressure.

[0830] "Location Information" is data used to measure a player's current location, typically obtained using a GPS device.

[0831] "Data analysis means" refers to algorithms and software used to analyze collected biometric data and location information and identify abnormal values.

[0832] An "alert" is a notification generated when an abnormality related to a player's health or safety is detected based on analyzed data.

[0833] "Designated Contact" refers to the contact person(s) involved in an athlete's well-being, such as a coach, trainer, or medical professional.

[0834] "History" means a record of data collected and warnings issued that is stored for later reference and analysis.

[0835] The present invention relates to a system that uses wearable devices, terminals, and servers to collect, analyze, and notify biometric data and location information in real time to ensure the health and safety of athletes. An embodiment of this system is described in detail below.

[0836] Data collection

[0837] Device: A wearable device worn by the athlete measures biometric data such as heart rate, body temperature, and blood pressure every second. The specific wearable device used is a common wearable device (e.g., fitness monitor or smartwatch). This device transmits the measurement data to a dedicated application using Bluetooth or Wi-Fi.

[0838] Device: Data from the wearable device is received by a dedicated application installed on the athlete's smartphone or tablet. The athlete's location information is also collected from the GPS device and sent to the application.

[0839] Data transmission and storage

[0840] Device: Biometric data and location information stored on the device (smartphone or tablet) is sent to a server via the Internet. A dedicated application uploads the data to the server at pre-set intervals.

[0841] Server: The server stores the received data in a database, typically MySQL or MongoDB, so that the data can be used for later analysis and historical reference.

[0842] Data analysis

[0843] Server: The server runs machine learning algorithms to analyze the collected biometric data in real time. Specific machine learning frameworks used are TensorFlow and Scikit-learn. This analysis includes comparison with past data.

[0844] Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities. Computer vision libraries such as OpenCV are used for video analysis.

[0845] Alert generation and notification

[0846] Server: If an abnormal value is detected based on the analysis results, the server immediately generates an alert, which includes the type of abnormality, such as a sudden increase in heart rate, and the urgency of the alert.

[0847] Server: Generated alerts are sent in real time to pre-designated contacts such as coaches, trainers, medical professionals, etc. Notification methods include SMS, email, and push notifications.

[0848] Actual response

[0849] Device: Coaches and trainers who receive the alert can check the notification through a dedicated application on their smartphone or tablet.

[0850] User: Based on the notification, coaches and trainers take appropriate action on the ground, such as suspending the player and directly checking their health status.

[0851] Data logging and management

[0852] Server: All data and alert history is stored in a database for later detailed analysis and reporting.

[0853] Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0854] Specific examples

[0855] When Player A is playing during a soccer match, the wearable device measures his heart rate every second and sends the data to the player's smartphone via Bluetooth. A dedicated application uploads the data to a server, which analyzes the heart rate data in real time. If the heart rate is abnormally high, a warning is sent to the coach's smartphone stating, "Player A's heart rate is abnormal. Immediate check is required." The coach checks the notification and takes action to directly check Player A's health condition on the spot.

[0856] Prompt sentence for generative AI model

[0857] "Please explain in detail the programming process for the system that analyzes players' health data in real time and sends alerts to coaches if abnormal values ​​are detected."

[0858] Thus, the present invention provides a concrete method for implementing a system that ensures the health and safety of athletes and allows for rapid response.

[0859] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0860] Step 1:

[0861] Data measurement by terminal

[0862] A wearable device installed on the terminal measures biometric data (e.g., heart rate, body temperature, blood pressure) every second. The input is the athlete's real-time condition, and the output is the measured biometric data. For example, a fitness monitor measures the athlete's heart rate and temporarily stores it in its internal memory.

[0863] Step 2:

[0864] Data reception by the terminal

[0865] Biometric data from the wearable device is sent to the athlete's smartphone or tablet via Bluetooth or Wi-Fi. The input is the measurement data from the wearable device, and the output is the biometric data received by a dedicated application on the device. The dedicated application organizes and saves this data as analyzed data. At the same time, location information from the GPS device is also received.

[0866] Step 3:

[0867] Data transmission by the terminal

[0868] The biometric data and location information received by the device are sent to a server via the Internet. The input is the measurement data and location information stored on the device, and the output is the data sent to the server. A dedicated application uploads the data to the server at pre-set intervals.

[0869] Step 4:

[0870] Data storage by server

[0871] The server stores the received biometric data and location information in a database. The input is the data sent from the device, and the output is the data stored in the database. Specifically, the data is stored as entries in a MySQL or MongoDB database.

[0872] Step 5:

[0873] Data analysis by server

[0874] The server analyzes the collected biometric data in real time. The input is the biometric data and location information stored in the database, and the output is the analyzed abnormal patterns and outliers. Machine learning algorithms (e.g., TensorFlow, Scikit-learn) are used to detect outliers in real time, and comparisons with past data are also performed.

[0875] Step 6:

[0876] Video data analysis by server

[0877] Video data from the game is sent to a server, where a video analysis algorithm is used to monitor the player's movements. The input is the video data, and the output is the detection result of movement anomalies. Specifically, a computer vision library such as OpenCV is used to perform the movement analysis.

[0878] Step 7:

[0879] Server-generated warnings

[0880] If an abnormal value is detected based on the analysis results, the server will immediately generate a warning. The input is the result of the abnormal value detection, and the output is the generated warning message. For example, a message such as "Player A's heart rate is abnormal. Immediate check required" may be generated.

[0881] Step 8:

[0882] Server warning notification

[0883] The generated alerts are sent in real time to designated contacts (coaches, trainers, medical professionals). The input is the generated alert message and the output is the notification sent to the contact. Notification methods used are SMS, email, and push notifications.

[0884] Step 9:

[0885] Check notifications on your device

[0886] The coach or trainer who receives the warning checks the notification through a dedicated application on their smartphone or tablet. The input is the warning message sent from the server, and the output is the notification displayed on the device. An emergency notification pops up on the coach's device.

[0887] Step 10:

[0888] User action taken

[0889] Based on the notification, coaches and trainers take appropriate action on the spot. The input is a notification displayed on the device, and the output is an action such as checking the player's health status or pausing play. Specifically, the coach takes action on the spot to directly check the player's health status.

[0890] Step 11:

[0891] Data log storage by server

[0892] The collected data and alert history are stored in a database. The input is all the data received and generated, and the output is a history in the database, which can be used for further analysis and reporting at a later date.

[0893] Step 12:

[0894] User confirmation of data

[0895] Athletes and officials log into the system to view past data. The input is historical data stored in a database, and the output is historical data displayed visually through an application or web portal. This allows for long-term management of athletes' performance and health.

[0896] (Application example 1)

[0897] 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."

[0898] The problem that this invention aims to solve is to provide a system for ensuring the health and safety of factory workers in real time. Conventional methods do not collect workers' vital signs or detect abnormal values ​​in real time, which means that even if an abnormality occurs, it is sometimes difficult to respond in a timely manner. Furthermore, when an abnormality is detected, the system is not notified promptly. This increases the risk that the health status of workers will be overlooked, which may lead to reduced productivity or serious accidents.

[0899] 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.

[0900] In this invention, the server includes means for collecting vital data of factory workers from wearable devices and sending it to the server, means for analyzing the data in real time on the server and generating and notifying an alert if an abnormality is detected, and means for saving the history of the collected data and issued alerts. This makes it possible to monitor the health status of factory workers in real time and respond quickly when an abnormality is detected.

[0901] "Athlete" means a person who engages in sports competition or physical activity.

[0902] A "factory worker" refers to a person who works in a factory in the manufacturing or processing industry.

[0903] A "wearable device" is a digital device that can be worn by the user and has the ability to collect vital data, location information, etc.

[0904] "Vital data" refers to data that indicates basic physiological indicators of living organisms, such as heart rate, body temperature, and blood pressure.

[0905] "Real-time" means that data collection and analysis occur in real time.

[0906] "Location information" refers to information that indicates the geographic location of a particular object using technology such as GPS.

[0907] A "server" refers to a computer system that stores data, analyzes data, notifies data, and performs other processes via a network.

[0908] "Analysis" refers to the process of examining collected data using calculations and algorithms.

[0909] An "outlier" refers to abnormal data that deviates from a pre-established standard value.

[0910] An "alert" is a notification generated to warn or alert you when an abnormality is detected.

[0911] "History" refers to a record of data collected and actions taken in the past.

[0912] "Responsible person" refers to a person in a position to manage or supervise a specific task.

[0913] The present invention provides a system for collecting, analyzing, and notifying data in real time to ensure the health and safety of factory workers. A specific embodiment of this system will be described below.

[0914] Data collection

[0915] 1. Terminal: Factory workers wear a wearable device that measures vital data such as heart rate, body temperature, and blood pressure every second. The device transmits the measurement data to a dedicated application using Bluetooth or Wi-Fi.

[0916] 2. Terminal: Data from the wearable device is received by a dedicated application installed on the worker's smartphone or tablet. At the same time, the worker's location information is also collected from the GPS device and sent to the application.

[0917] Data transmission and storage

[0918] 1. Server: Vital data and location information received from the device are sent to the server via the Internet.

[0919] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[0920] Data analysis

[0921] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time to detect abnormal patterns, including comparing it with historical data.

[0922] 2. Server: The server simultaneously receives video data during gameplay and uses a video analysis algorithm to monitor the worker's movements and check for any abnormalities.

[0923] Alert generation and notification

[0924] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert content includes the type of abnormality and its urgency.

[0925] 2. Server: Generated alerts are sent in real time to pre-designated responsible parties.

[0926] Actual response

[0927] 1. Terminal: The person in charge who receives the alert can check the notification through an application on their smartphone or tablet.

[0928] 2. User: Based on the notification, take appropriate action on-site, such as temporarily suspending workers' activities and directly checking their health status.

[0929] Data logging and management

[0930] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[0931] 2. Users: Workers and other stakeholders can log in to the system and check past data, enabling long-term management of worker performance and health status.

[0932] Specific examples

[0933] When Worker A is working in the factory, the wearable device measures his heart rate every second and sends the data to a server using a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the person in charge saying, "Worker A's heart rate is abnormal. Immediate check is required." The person in charge checks the notification on their smartphone and takes action to directly check on Worker A's condition.

[0934] Prompt Sentence Examples

[0935] Design a system to monitor the vital data (heart rate, body temperature, blood pressure) of factory workers in real time and notify supervisors if an abnormality is detected. Write a Python program that includes modules for data collection, transmission, analysis, and alert generation.

[0936] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0937] Step 1:

[0938] The terminal collects vital data (heart rate, body temperature, blood pressure, etc.) from the wearable device. This data is sent to a dedicated application on the terminal via Bluetooth or Wi-Fi. The input is the vital data obtained in real time from the wearable device, and the output is the vital data being sent to the terminal application.

[0939] Step 2:

[0940] The terminal transmits the vital data and location information received from the wearable device to a server via the Internet. The input is the vital data and location information received by the terminal, and the output is the data being sent to the server. The data is transmitted in an encrypted format to ensure privacy and security.

[0941] Step 3:

[0942] The server stores the vital data and location information received from the device in a database. The input is the vital data and location information sent to the server via the Internet, and the output is that these data are stored in the database. When the data is stored, a timestamp of the data is also recorded.

[0943] Step 4:

[0944] The server analyzes the collected vital data in real time. Specifically, it runs a machine learning algorithm to compare the data with abnormal patterns and detect abnormalities. The input is the vital data stored in the database, and the output is the analysis results. The server also compares the data with past data to determine whether or not there are any abnormalities.

[0945] Step 5:

[0946] If the server detects an abnormal value as a result of real-time analysis, it generates an alert. The input is the analysis result from step 4, and the output is the generated alert. The alert content includes the type of abnormality and its urgency.

[0947] Step 6:

[0948] The server sends the generated alert to the relevant person in real time. The input is the generated alert, and the output is a notification that appears on the person's smartphone or tablet. The notification includes a specific action to be taken.

[0949] Step 7:

[0950] The terminal (smartphone or tablet of the person in charge) receives the alert notification and the person in charge checks it. The input is the alert notification sent from the server, and the output is checking the notification.

[0951] Step 8:

[0952] The user (responsible person) takes action on-site based on the notification content. Specifically, they may temporarily suspend the worker's activities and directly check their health status. The input is the content of the received alert, and the output is the appropriate response action.

[0953] Step 9:

[0954] The server stores all data and alert history in a database for later analysis and reporting. The input is real-time collected data and issued alerts, and the output is historical data stored in the database. The stored data is backed up regularly.

[0955] 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.

[0956] The present invention relates to a system for ensuring the health and safety of athletes, incorporating an emotion engine, and is equipped with functions for collecting data from wearable devices, analyzing the data, generating alerts, and notifying the athletes. A specific embodiment of this system will be described below.

[0957] Data collection

[0958] 1. Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[0959] 2. Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[0960] 3. Device: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[0961] Data transmission and storage

[0962] 1. Server: The vital data and location information received from the device are sent to the server via the Internet.

[0963] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[0964] Data analysis

[0965] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time, detecting abnormal patterns, including comparing it with historical data.

[0966] 2. Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities.

[0967] 3. Server: The emotion engine analyzes the player's vital and behavioral data to assess their emotional state.

[0968] Alert generation and notification

[0969] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert includes the type of abnormality and its urgency.

[0970] 2. Server: The emotion engine evaluates the player's emotional state, and if the emotional state is determined to be abnormal, it generates an alert that takes the emotional state into account.

[0971] 3. Server: Sends generated alerts in real time to designated contacts such as coaches, trainers, and doctors.

[0972] 4. Server: If necessary, analyze the emotional state of coaches, trainers, and doctors and send alerts at the appropriate time.

[0973] Actual response

[0974] 1. Device: Coaches and trainers who receive the alert can check the notification via an application on their smartphone or tablet.

[0975] 2. User: Based on the notification, take appropriate action on the spot (e.g., pause play and check on the health status of players).

[0976] Data logging and management

[0977] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[0978] 2. Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[0979] Specific examples

[0980] While Player B is playing a basketball game, the wearable device measures his heart rate every second, and the data is sent to a server via a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player B's heart rate is abnormal. Immediate check is required." Furthermore, if the emotion engine determines that Player B's emotional state is also abnormal, it generates an additional alert based on that. The coach checks the notification on his smartphone and takes action to directly check on Player B's condition. In this way, the present invention provides a specific method for implementing a system that provides real-time data collection, analysis, alert generation, and notification functions to ensure the health and safety of players.

[0981] The processing flow will be explained below.

[0982] Step 1:

[0983] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[0984] Step 2:

[0985] Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[0986] Step 3:

[0987] Terminal: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[0988] Step 4:

[0989] Server: The received vital data and location information are converted into an appropriate format and stored in the database.

[0990] Step 5:

[0991] Server: Runs machine learning algorithms to analyze stored vital data in real time and detect abnormal patterns.

[0992] Step 6:

[0993] Server: At the same time, it receives video data during play and uses video analysis algorithms to monitor players' movements and check for any abnormalities.

[0994] Step 7:

[0995] Server: The emotion engine analyzes the player's vital and behavioral data to assess their emotional state.

[0996] Step 8:

[0997] Server: Based on the analysis results and the evaluation of the emotion engine, an alert is generated if an abnormal value is detected. The alert includes the type of abnormality, urgency, and the evaluation result of the emotional state.

[0998] Step 9:

[0999] Server: Generated alerts are sent in real time to designated contacts such as coaches, trainers, and doctors. If necessary, the server also analyzes the emotional state of the coaches, trainers, and doctors and notifies them of the alerts at the appropriate time.

[1000] Step 10:

[1001] Device: Displays a notification on the smartphone or tablet of the coach or trainer who received the alert.

[1002] Step 11:

[1003] User: Review the notification and take appropriate action on the spot (e.g., pause play and check on player health).

[1004] Step 12:

[1005] Server: All data and alert history is stored in a database for later analysis and reporting.

[1006] Step 13:

[1007] Users: Log in to the system to view past data and use it to manage athlete performance and health over the long term.

[1008] Example 2

[1009] 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."

[1010] In recent years, there has been an increasing emphasis on managing the health and safety of athletes in sports. In particular, there is a demand for systems that can monitor athletes' vital signs and behavioral data in real time and detect abnormalities. However, conventional methods can sometimes be slow to detect abnormalities, increasing the risk to athletes' health. Furthermore, there are also issues with athletes and staff not being able to respond appropriately because they are unable to take into account their emotional state. Furthermore, there are very few systems that take into account the emotional state of coaches and trainers and issue alerts at the appropriate time.

[1011] 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 vital data in real time from wearable devices worn by athletes; means for analyzing the collected vital data and location information and identifying abnormal values; means for comparing past data and detecting abnormal patterns in the vital data using a machine learning algorithm; means for generating an alert based on the identified abnormal values ​​and notifying designated contacts; means for saving the history of collected data and issued alerts; and means for evaluating the athletes' emotional states using an emotion engine and detecting abnormalities. This makes it possible to monitor the athletes' health conditions in real time, and to immediately issue alerts and notify relevant parties if an abnormality is detected. Furthermore, taking the athletes' emotional states into consideration allows athletes and staff to respond quickly and appropriately. Furthermore, analyzing the emotional states of coaches and trainers and notifying them at optimal times enables more effective responses.

[1012] A "wearable device" refers to a device worn by an athlete that measures vital data in real time and transmits the data.

[1013] "Vital data" refers to biometric information that indicates an athlete's health status, such as heart rate, body temperature, and blood pressure.

[1014] "Location information" refers to data indicating the player's current location, and is information obtained using technology such as GPS.

[1015] "Analysis" refers to the process of evaluating and analyzing collected data to detect abnormal patterns or characteristics.

[1016] A "machine learning algorithm" refers to a computational method that learns from past data and automatically recognizes patterns in new data.

[1017] An "abnormal pattern" refers to a player's vital or behavioral data that deviates from the normal range and may indicate a health risk.

[1018] An "alert" is a warning notification that is generated when an abnormal value or pattern is detected and is transmitted to designated contacts.

[1019] The "emotion engine" refers to a system that evaluates and judges a player's emotional state by analyzing their vital and behavioral data.

[1020] "Notification" means the act of sending a generated Alert to designated contacts in real time.

[1021] A "database" refers to a system that stores collected data and the history of issued alerts, and manages them so that they can be referenced and analyzed as needed.

[1022] The present invention is a system that combines a wearable device, an analysis algorithm, an emotion engine, and a notification function to ensure the health and safety of athletes. A specific embodiment of this system will be described below.

[1023] Data collection

[1024] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second. This device could be a commercial fitness tracker or sports watch, for example.

[1025] Terminal: The measured data is sent to a dedicated application via Bluetooth or Wi-Fi. The dedicated application is typically a smartphone application.

[1026] Terminal: The dedicated application sends the received vital data and GPS data to a server via the Internet. The GPS device used here can be a smartphone or a dedicated GPS tracker.

[1027] Data transmission and storage

[1028] Server: A cloud-based server is used to process vital data and location information received from the device via the internet.

[1029] Server: The received data is stored in a database, typically an SQL database or a NoSQL database.

[1030] Data analysis

[1031] Server: Runs machine learning algorithms to analyze the vital data stored in the database in real time, using programming languages ​​such as Python and R and machine learning libraries such as TensorFlow and Scikit-learn.

[1032] Server: Applying video analysis algorithms to analyze collected video data in parallel. Specifically, video analysis libraries such as OpenCV and DeepStream are used.

[1033] Server: The emotion engine analyzes the players' vital data and behavioral data to evaluate their emotional state. The emotion engine uses Python NLP libraries and emotion analysis APIs.

[1034] Alert generation and notification

[1035] Server: If an abnormal value is detected based on the analysis results, an alert is generated. This alert includes the type of abnormality and its urgency.

[1036] Server: The emotion engine evaluates the player's emotional state, and if it is determined to be abnormal, it generates an alert that takes the emotional state into account.

[1037] Server: Generated alerts are sent in real time to designated contacts, which may include coaches, trainers, doctors, etc.

[1038] Server: If necessary, analyze the emotional state of coaches, trainers, and doctors and send alerts at the appropriate time.

[1039] Actual response

[1040] Device: Coaches and trainers who receive the alert can check the notification through an application on their smartphone or tablet.

[1041] User: Based on the notification, take appropriate action on the ground—for example, pausing play and directly checking on the player's health status.

[1042] Data logging and management

[1043] Server: All data and alert history is stored in a database for future data analysis and reporting.

[1044] Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[1045] Specific examples and prompts for the generative AI model

[1046] 1. Example: When Player B is playing a basketball game, the wearable device measures his heart rate every second, and the data is sent to the server by a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player B's heart rate is abnormal. Immediate check is required." Furthermore, if the emotion engine determines that Player B's emotional state is also abnormal, it generates an additional alert based on that. The coach checks the notification on his smartphone and takes action to directly check on Player B's condition. This system provides real-time data collection, analysis, alert generation, and notification functions to ensure the health and safety of players.

[1047] 2. Generative AI model prompt:

[1048] Using the information below, please describe in detail a program that analyzes player health and generates necessary alerts.

[1049] Wearable devices measure vital data such as heart rate, body temperature, and blood pressure every second.

[1050] Measurement data is received by a dedicated application and sent to a server via the Internet.

[1051] The server stores the data in a database and analyzes it in real time using machine learning algorithms.

[1052] Emotion engine analyzes vital and behavioral data to assess emotional state

[1053] If anomalies or abnormal emotions are detected, alerts are generated and notify coaches and trainers in real time.

[1054] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1055] Step 1:

[1056] Device: A wearable device attached to the player measures vital data such as heart rate, body temperature, and blood pressure every second. Here, sensors acquire each vital data and store it in internal memory. The input is the player's vital signs, and the output is the vital data stored in the device's internal memory.

[1057] Step 2:

[1058] Terminal: The wearable device sends the measured data to a dedicated application via Bluetooth or Wi-Fi. At this time, the data transmission module inside the device is activated and sends the vital data in packet form to the application. The input is the vital data in the device's internal memory, and the output is the data sent to the dedicated application.

[1059] Step 3:

[1060] Terminal: The dedicated application sends the received vital data and GPS data to a server via the Internet. The application receives the data and then sends it to the server using the TCP / IP protocol. The input is the vital data and GPS data received by the dedicated application, and the output is the data sent to the server via the Internet.

[1061] Step 4:

[1062] Server: The vital data and location information received via the Internet are stored in a database. The server buffers the data and then uses an SQL query to store it in the database. The input is the vital data and location information received from the Internet, and the output is the data stored in the database.

[1063] Step 5:

[1064] Server: Executes machine learning algorithms to analyze the vital data stored in the database. The server reads data from the database and performs analysis using Python or R. For example, comparison with past data and time series analysis are used to detect abnormal patterns. The input is the vital data read from the database, and the output is the analysis results.

[1065] Step 6:

[1066] Server: Analyzes the collected video data using a video analysis algorithm. The server receives the video data and analyzes the player's movements using libraries such as OpenCV and DeepStream. The input is the video data, and the output is the analysis result indicating whether or not there are any abnormalities in the player's movements.

[1067] Step 7:

[1068] Server: The emotion engine analyzes the player's vital data and behavioral data to evaluate their emotional state. Here, emotion recognition is performed using an NLP library and emotion analysis API. The input is vital data and behavioral data, and the output is the evaluation result of the player's emotional state.

[1069] Step 8:

[1070] Server: Generates an alert if an abnormal value is detected based on the analysis results. The server evaluates the analysis results and generates an alert message if an abnormality is detected. The input is the analysis results and the output is the generated alert.

[1071] Step 9:

[1072] Server: Sends generated alerts in real time to designated contacts. The server prepares alert messages and sends them to contacts such as coaches, trainers, doctors, etc. The input is the generated alert and the output is the notification sent to the designated contacts.

[1073] Step 10:

[1074] Device: The coach or trainer who receives the alert checks the notification through an application on their smartphone or tablet. The application notifies the user of the alert message via a pop-up display or push notification. The input is the received alert message, and the output is the notification displayed to the user.

[1075] Step 11:

[1076] User: Based on the notification content, take appropriate action on the spot. For example, pause play and directly check the health status of the players. In this case, the user follows the instructions within the application to check and take action in real time. The input is the notification content, and the output is the action taken.

[1077] Step 12:

[1078] Server: All data and alert history is stored in a database for later analysis and reporting. The server adds new data and manages history. The input is newly acquired data and alert history, and the output is an updated database.

[1079] Step 13:

[1080] Users: Athletes and related personnel log in to the system to check past data. This enables long-term health management and performance analysis. The input is past data, and the output is displayed data.

[1081] (Application example 2)

[1082] 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."

[1083] It is difficult to monitor the health and safety of factory workers in real time, and there is a high risk of serious accidents occurring due to poor health or stress while working. In particular, conventional systems have been slow to collect and analyze vital data, making it difficult to respond quickly. Another issue is that they are unable to properly assess workers' emotional state and provide support at the appropriate time.

[1084] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data in real time from the wearable devices worn by the athletes, means for analyzing the collected vital data and location information and identifying abnormal values, and means for generating an alert based on the identified abnormal values ​​and notifying designated contacts. This enables health and safety management of factory workers.

[1085] A "wearable device" is a device that is worn on the body and collects vital data in real time.

[1086] "Vital data" refers to physiological data that indicates the health status of the human body, such as heart rate, body temperature, and blood pressure.

[1087] "Location Information" means data on the current geographic location of athletes and workers, obtained using a GPS device or the like.

[1088] An "emotion analysis engine" is a system that analyzes vital data and behavioral data to assess a subject's emotional state.

[1089] An "abnormal value" is data such as vital data or location information that deviates from the normal range and indicates a physical or mental abnormality.

[1090] An "alert" is a warning message that is generated when an abnormal value is detected and sent to designated contacts.

[1091] "Contacts" are people such as coaches, trainers, doctors, etc. who are set to receive alerts and their contact methods.

[1092] "Factory worker health and safety management" is a process that monitors the health and safety of factory workers in real time and prompts rapid response when abnormalities are detected.

[1093] A "database" is a system for storing collected data and the history of issued alerts.

[1094] The present invention is a system for monitoring the health and safety of factory workers in real time, and can be implemented as follows.

[1095] Hardware and Software Configuration

[1096] Hardware:

[1097] Wearable devices: Worn by factory workers to measure vital data such as heart rate, body temperature, and blood pressure.

[1098] Communication device: Uses Bluetooth or Wi-Fi to transmit data from the wearable device to a dedicated application.

[1099] Server: A processing device that analyzes collected data and detects abnormal values.

[1100] software:

[1101] Dedicated application: Sends data collected from the wearable device to a server.

[1102] Data analysis software: Using the Python libraries pandas and IsolationForest, the collected vital data is analyzed to detect anomalies.

[1103] Sentiment Analysis Engine: Uses Hugging Face transformers to assess the emotional state of factory workers from text data.

[1104] Data collection and transmission

[1105] The terminal collects vital data in real time from wearable devices worn by factory workers. The collected data is sent to a dedicated application via Bluetooth or Wi-Fi, and then transmitted over the Internet to a server. The server stores the collected data in a database.

[1106] Analyzing data and generating alerts

[1107] The server analyzes the received vital data and location information in real time to detect abnormal patterns. This analysis is performed using the machine learning algorithm IsolationForest. If an abnormality is detected, an alert is generated and notified to designated contacts in real time. In addition, an emotion analysis engine is used to evaluate the emotional state of factory workers, and if an abnormality is detected, an emotion-based alert is also generated.

[1108] Alert notification and response

[1109] Alerts generated by the server are sent in real time to designated coaches, trainers, medical staff, etc. Users who receive the notifications can check the content of the notifications through an application on their smartphone or tablet and take necessary action, such as temporarily halting work and directly checking the health status of factory workers.

[1110] Specific examples

[1111] As a concrete example, consider a situation where Worker A is performing welding work in a factory. At this time, a wearable device collects data that Worker A's heart rate exceeds 150 and his body temperature exceeds 38 degrees. This data is sent to a server via a dedicated application, which analyzes it and detects abnormalities. If an abnormality is detected, an alert is sent to the administrator stating, "Worker A's heart rate and body temperature are abnormal. Immediate check is required." The emotion analysis engine then analyzes the text data, "I'm feeling very stressed out," and an additional emotion alert is generated.

[1112] Prompt Sentence Examples

[1113] While welding, Worker A's heart rate exceeds 150 and his body temperature exceeds 38°C. The system determines this to be an abnormality and sends an emergency alert to the administrator.

[1114] Worker B enters the text "I'm feeling very stressed out" into the system. The emotion analysis engine determines this as a high-stress state and generates an alert.

[1115] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1116] Step 1:

[1117] The terminal collects real-time vital data (heart rate, body temperature, blood pressure) from wearable devices worn by factory workers. The collected data is measured every second. The input data is heart rate, body temperature, and blood pressure, and the output is sent to a dedicated application via Bluetooth or Wi-Fi.

[1118] Step 2:

[1119] The device sends the collected vital data to a dedicated application via Bluetooth or Wi-Fi. The application then sends the data to a server via the Internet. The input data is the vital data, and the output is the raw data sent to the server.

[1120] Step 3:

[1121] The server stores the received vital data in a database. The input is the raw data sent via the internet, and the output is the data stored in the database. The database also contains historical data.

[1122] Step 4:

[1123] The server analyzes the collected data in real time and uses the Isolation Forest algorithm to detect abnormal patterns. The input is the latest vital data stored in the database, and the output is a judgment result on whether the value is abnormal or not.

[1124] Step 5:

[1125] The server uses an emotion analysis engine to evaluate the emotional state of the factory workers. The input is the text data of the factory workers, and the output is the evaluation result of the emotional state. Hugging Face Transformers are used for emotion analysis.

[1126] Step 6:

[1127] The server generates alerts based on the results of anomaly analysis and emotion analysis. The inputs are the anomaly detection results and the emotional state evaluation results, and the output is the generated alert. The alert includes the type of anomaly and its urgency.

[1128] Step 7:

[1129] The server notifies designated contacts (such as administrators or medical staff) of generated alerts in real time. The input is the generated alert and the output is the notification sent to the contact.

[1130] Step 8:

[1131] Users (administrators or medical staff) check the notification content through a smartphone or tablet application and take the necessary action. The input is the notification content, and the output is specific actions such as checking the health status of factory workers or suspending work.

[1132] 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.

[1133] 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.

[1134] 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.

[1135] [Fourth embodiment]

[1136] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1137] 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.

[1138] 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).

[1139] 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.

[1140] 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.

[1141] 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).

[1142] 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.

[1143] 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.

[1144] 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.

[1145] 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.

[1146] 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.

[1147] 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.

[1148] 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."

[1149] The present invention relates to a system for collecting, analyzing, and notifying data in real time to ensure the health and safety of athletes. Specific embodiments of this system will be described below.

[1150] Data collection

[1151] 1. Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second. The device transmits the measurement data to a dedicated application via Bluetooth or Wi-Fi.

[1152] 2. Device: Data from the wearable device is received by a dedicated application installed on the athlete's smartphone or tablet. At the same time, the athlete's location information is also collected from the GPS device and sent to the application.

[1153] Data transmission and storage

[1154] 1. Server: Vital data and location information received from the device are sent to the server via the Internet.

[1155] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[1156] Data analysis

[1157] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time to detect abnormal patterns, including comparing it with historical data.

[1158] 2. Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities.

[1159] Alert generation and notification

[1160] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert content includes the type of abnormality and its urgency.

[1161] 2. Server: Generated alerts are sent in real time to pre-designated contacts such as coaches, trainers, and doctors.

[1162] Actual response

[1163] 1. Device: Coaches and trainers who receive the alert can check the notification via an application on their smartphone or tablet.

[1164] 2. User: Based on the notification, take appropriate action on the spot, such as pausing the player's play and directly checking their health status.

[1165] Data logging and management

[1166] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[1167] 2. Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[1168] Specific examples

[1169] When Player A is playing a soccer match, the wearable device measures his heart rate every second, and the data is sent to a server via a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player A's heart rate is abnormal. Immediate check is required." The coach checks the notification on his smartphone and takes action to directly check on Player A's condition.

[1170] In this way, the present invention provides a concrete method for implementing a system that ensures the health and safety of athletes and allows for rapid response.

[1171] The processing flow will be explained below.

[1172] Step 1:

[1173] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[1174] Step 2:

[1175] Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[1176] Step 3:

[1177] Terminal: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[1178] Step 4:

[1179] Server: Converts the received data into an appropriate format and stores it in the database in order to save it in the database.

[1180] Step 5:

[1181] Server: Runs machine learning algorithms to analyze stored data in real time and detect abnormal patterns.

[1182] Step 6:

[1183] Server: Based on the analysis results, an alert is generated if an abnormal value is detected. The alert includes the type of abnormality and its urgency.

[1184] Step 7:

[1185] Server: Sends generated alerts in real time to designated contacts such as coaches, trainers, and doctors.

[1186] Step 8:

[1187] Device: Displays a notification on the smartphone or tablet of the coach or trainer who received the alert.

[1188] Step 9:

[1189] User: Review the notification and take appropriate action on the spot (e.g., pause play and check on player health).

[1190] Step 10:

[1191] Server: All data and alert history is stored in a database for later analysis and reporting.

[1192] Step 11:

[1193] Users: Log in to the system to view past data and use it to manage athlete performance and health over the long term.

[1194] Example 1

[1195] 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."

[1196] The present invention relates to a system that efficiently collects and analyzes real-time biometric data and location information to ensure the health and safety of athletes. However, current systems have difficulty quickly detecting abnormalities and notifying relevant personnel, which can result in athletes' safety being compromised. Furthermore, the system lacks effective storage and management of past data and warning history, which is another area where improvement is needed.

[1197] 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.

[1198] In this invention, the server includes means for collecting biometric data in real time from wearable devices worn by players, data analysis means for analyzing the collected biometric data and location information and identifying abnormal values, means for generating warnings based on the identified abnormal values ​​and notifying designated contacts, and means for saving the history of the collected data and issued warnings, thereby enabling real-time monitoring of player health and safety and enabling prompt response.

[1199] A "wearable device" is a device that is worn by an athlete to measure biometric data in real time.

[1200] "Biometric data" refers to physiological data used to assess an athlete's health, such as heart rate, body temperature, and blood pressure.

[1201] "Location Information" is data used to measure a player's current location, typically obtained using a GPS device.

[1202] "Data analysis means" refers to algorithms and software used to analyze collected biometric data and location information and identify abnormal values.

[1203] An "alert" is a notification generated when an abnormality related to a player's health or safety is detected based on analyzed data.

[1204] "Designated Contact" refers to the contact person(s) involved in an athlete's well-being, such as a coach, trainer, or medical professional.

[1205] "History" means a record of data collected and warnings issued that is stored for later reference and analysis.

[1206] The present invention relates to a system that uses wearable devices, terminals, and servers to collect, analyze, and notify biometric data and location information in real time to ensure the health and safety of athletes. An embodiment of this system is described in detail below.

[1207] Data collection

[1208] Device: A wearable device worn by the athlete measures biometric data such as heart rate, body temperature, and blood pressure every second. The specific wearable device used is a common wearable device (e.g., fitness monitor or smartwatch). This device transmits the measurement data to a dedicated application using Bluetooth or Wi-Fi.

[1209] Device: Data from the wearable device is received by a dedicated application installed on the athlete's smartphone or tablet. The athlete's location information is also collected from the GPS device and sent to the application.

[1210] Data transmission and storage

[1211] Device: Biometric data and location information stored on the device (smartphone or tablet) is sent to a server via the Internet. A dedicated application uploads the data to the server at pre-set intervals.

[1212] Server: The server stores the received data in a database, typically MySQL or MongoDB, so that the data can be used for later analysis and historical reference.

[1213] Data analysis

[1214] Server: The server runs machine learning algorithms to analyze the collected biometric data in real time. Specific machine learning frameworks used are TensorFlow and Scikit-learn. This analysis includes comparison with past data.

[1215] Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities. Computer vision libraries such as OpenCV are used for video analysis.

[1216] Alert generation and notification

[1217] Server: If an abnormal value is detected based on the analysis results, the server immediately generates an alert, which includes the type of abnormality, such as a sudden increase in heart rate, and the urgency of the alert.

[1218] Server: Generated alerts are sent in real time to pre-designated contacts such as coaches, trainers, medical professionals, etc. Notification methods include SMS, email, and push notifications.

[1219] Actual response

[1220] Device: Coaches and trainers who receive the alert can check the notification through a dedicated application on their smartphone or tablet.

[1221] User: Based on the notification, coaches and trainers take appropriate action on the ground, such as suspending the player and directly checking their health status.

[1222] Data logging and management

[1223] Server: All data and alert history is stored in a database for later detailed analysis and reporting.

[1224] Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[1225] Specific examples

[1226] When Player A is playing during a soccer match, the wearable device measures his heart rate every second and sends the data to the player's smartphone via Bluetooth. A dedicated application uploads the data to a server, which analyzes the heart rate data in real time. If the heart rate is abnormally high, a warning is sent to the coach's smartphone stating, "Player A's heart rate is abnormal. Immediate check is required." The coach checks the notification and takes action to directly check Player A's health condition on the spot.

[1227] Prompt sentence for generative AI model

[1228] "Please explain in detail the programming process for the system that analyzes players' health data in real time and sends alerts to coaches if abnormal values ​​are detected."

[1229] Thus, the present invention provides a concrete method for implementing a system that ensures the health and safety of athletes and allows for rapid response.

[1230] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1231] Step 1:

[1232] Data measurement by terminal

[1233] A wearable device installed on the terminal measures biometric data (e.g., heart rate, body temperature, blood pressure) every second. The input is the athlete's real-time condition, and the output is the measured biometric data. For example, a fitness monitor measures the athlete's heart rate and temporarily stores it in its internal memory.

[1234] Step 2:

[1235] Data reception by the terminal

[1236] Biometric data from the wearable device is sent to the athlete's smartphone or tablet via Bluetooth or Wi-Fi. The input is the measurement data from the wearable device, and the output is the biometric data received by a dedicated application on the device. The dedicated application organizes and saves this data as analyzed data. At the same time, location information from the GPS device is also received.

[1237] Step 3:

[1238] Data transmission by the terminal

[1239] The biometric data and location information received by the device are sent to a server via the Internet. The input is the measurement data and location information stored on the device, and the output is the data sent to the server. A dedicated application uploads the data to the server at pre-set intervals.

[1240] Step 4:

[1241] Data storage by server

[1242] The server stores the received biometric data and location information in a database. The input is the data sent from the device, and the output is the data stored in the database. Specifically, the data is stored as entries in a MySQL or MongoDB database.

[1243] Step 5:

[1244] Data analysis by server

[1245] The server analyzes the collected biometric data in real time. The input is the biometric data and location information stored in the database, and the output is the analyzed abnormal patterns and outliers. Machine learning algorithms (e.g., TensorFlow, Scikit-learn) are used to detect outliers in real time, and comparisons with past data are also performed.

[1246] Step 6:

[1247] Video data analysis by server

[1248] Video data from the game is sent to a server, where a video analysis algorithm is used to monitor the player's movements. The input is the video data, and the output is the detection result of movement anomalies. Specifically, a computer vision library such as OpenCV is used to perform the movement analysis.

[1249] Step 7:

[1250] Server-generated warnings

[1251] If an abnormal value is detected based on the analysis results, the server will immediately generate a warning. The input is the result of the abnormal value detection, and the output is the generated warning message. For example, a message such as "Player A's heart rate is abnormal. Immediate check required" may be generated.

[1252] Step 8:

[1253] Server warning notification

[1254] The generated alerts are sent in real time to designated contacts (coaches, trainers, medical professionals). The input is the generated alert message and the output is the notification sent to the contact. Notification methods used are SMS, email, and push notifications.

[1255] Step 9:

[1256] Check notifications on your device

[1257] The coach or trainer who receives the warning checks the notification through a dedicated application on their smartphone or tablet. The input is the warning message sent from the server, and the output is the notification displayed on the device. An emergency notification pops up on the coach's device.

[1258] Step 10:

[1259] User action taken

[1260] Based on the notification, coaches and trainers take appropriate action on the spot. The input is a notification displayed on the device, and the output is an action such as checking the player's health status or pausing play. Specifically, the coach takes action on the spot to directly check the player's health status.

[1261] Step 11:

[1262] Data log storage by server

[1263] The collected data and alert history are stored in a database. The input is all the data received and generated, and the output is a history in the database, which can be used for further analysis and reporting at a later date.

[1264] Step 12:

[1265] User confirmation of data

[1266] Athletes and officials log into the system to view past data. The input is historical data stored in a database, and the output is historical data displayed visually through an application or web portal. This allows for long-term management of athletes' performance and health.

[1267] (Application example 1)

[1268] 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."

[1269] The problem that this invention aims to solve is to provide a system for ensuring the health and safety of factory workers in real time. Conventional methods do not collect workers' vital signs or detect abnormal values ​​in real time, which means that even if an abnormality occurs, it is sometimes difficult to respond in a timely manner. Furthermore, when an abnormality is detected, the system is not notified promptly. This increases the risk that the health status of workers will be overlooked, which may lead to reduced productivity or serious accidents.

[1270] 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.

[1271] In this invention, the server includes means for collecting vital data of factory workers from wearable devices and sending it to the server, means for analyzing the data in real time on the server and generating and notifying an alert if an abnormality is detected, and means for saving the history of the collected data and issued alerts. This makes it possible to monitor the health status of factory workers in real time and respond quickly when an abnormality is detected.

[1272] "Athlete" means a person who engages in sports competition or physical activity.

[1273] A "factory worker" refers to a person who works in a factory in the manufacturing or processing industry.

[1274] A "wearable device" is a digital device that can be worn by the user and has the ability to collect vital data, location information, etc.

[1275] "Vital data" refers to data that indicates basic physiological indicators of living organisms, such as heart rate, body temperature, and blood pressure.

[1276] "Real-time" means that data collection and analysis occur in real time.

[1277] "Location information" refers to information that indicates the geographic location of a particular object using technology such as GPS.

[1278] A "server" refers to a computer system that stores data, analyzes data, notifies data, and performs other processes via a network.

[1279] "Analysis" refers to the process of examining collected data using calculations and algorithms.

[1280] An "outlier" refers to abnormal data that deviates from a pre-established standard value.

[1281] An "alert" is a notification generated to warn or alert you when an abnormality is detected.

[1282] "History" refers to a record of data collected and actions taken in the past.

[1283] "Responsible person" refers to a person in a position to manage or supervise a specific task.

[1284] The present invention provides a system for collecting, analyzing, and notifying data in real time to ensure the health and safety of factory workers. A specific embodiment of this system will be described below.

[1285] Data collection

[1286] 1. Terminal: Factory workers wear a wearable device that measures vital data such as heart rate, body temperature, and blood pressure every second. The device transmits the measurement data to a dedicated application using Bluetooth or Wi-Fi.

[1287] 2. Terminal: Data from the wearable device is received by a dedicated application installed on the worker's smartphone or tablet. At the same time, the worker's location information is also collected from the GPS device and sent to the application.

[1288] Data transmission and storage

[1289] 1. Server: Vital data and location information received from the device are sent to the server via the Internet.

[1290] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[1291] Data analysis

[1292] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time to detect abnormal patterns, including comparing it with historical data.

[1293] 2. Server: The server simultaneously receives video data during gameplay and uses a video analysis algorithm to monitor the worker's movements and check for any abnormalities.

[1294] Alert generation and notification

[1295] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert content includes the type of abnormality and its urgency.

[1296] 2. Server: Generated alerts are sent in real time to pre-designated responsible parties.

[1297] Actual response

[1298] 1. Terminal: The person in charge who receives the alert can check the notification through an application on their smartphone or tablet.

[1299] 2. User: Based on the notification, take appropriate action on-site, such as temporarily suspending workers' activities and directly checking their health status.

[1300] Data logging and management

[1301] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[1302] 2. Users: Workers and other stakeholders can log in to the system and check past data, enabling long-term management of worker performance and health status.

[1303] Specific examples

[1304] When Worker A is working in the factory, the wearable device measures his heart rate every second and sends the data to a server using a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the person in charge saying, "Worker A's heart rate is abnormal. Immediate check is required." The person in charge checks the notification on their smartphone and takes action to directly check on Worker A's condition.

[1305] Prompt Sentence Examples

[1306] Design a system to monitor the vital data (heart rate, body temperature, blood pressure) of factory workers in real time and notify supervisors if an abnormality is detected. Write a Python program that includes modules for data collection, transmission, analysis, and alert generation.

[1307] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1308] Step 1:

[1309] The terminal collects vital data (heart rate, body temperature, blood pressure, etc.) from the wearable device. This data is sent to a dedicated application on the terminal via Bluetooth or Wi-Fi. The input is the vital data obtained in real time from the wearable device, and the output is the vital data being sent to the terminal application.

[1310] Step 2:

[1311] The terminal transmits the vital data and location information received from the wearable device to a server via the Internet. The input is the vital data and location information received by the terminal, and the output is the data being sent to the server. The data is transmitted in an encrypted format to ensure privacy and security.

[1312] Step 3:

[1313] The server stores the vital data and location information received from the device in a database. The input is the vital data and location information sent to the server via the Internet, and the output is that these data are stored in the database. When the data is stored, a timestamp of the data is also recorded.

[1314] Step 4:

[1315] The server analyzes the collected vital data in real time. Specifically, it runs a machine learning algorithm to compare the data with abnormal patterns and detect abnormalities. The input is the vital data stored in the database, and the output is the analysis results. The server also compares the data with past data to determine whether or not there are any abnormalities.

[1316] Step 5:

[1317] If the server detects an abnormal value as a result of real-time analysis, it generates an alert. The input is the analysis result from step 4, and the output is the generated alert. The alert content includes the type of abnormality and its urgency.

[1318] Step 6:

[1319] The server sends the generated alert to the relevant person in real time. The input is the generated alert, and the output is a notification that appears on the person's smartphone or tablet. The notification includes a specific action to be taken.

[1320] Step 7:

[1321] The terminal (smartphone or tablet of the person in charge) receives the alert notification and the person in charge checks it. The input is the alert notification sent from the server, and the output is checking the notification.

[1322] Step 8:

[1323] The user (responsible person) takes action on-site based on the notification content. Specifically, they may temporarily suspend the worker's activities and directly check their health status. The input is the content of the received alert, and the output is the appropriate response action.

[1324] Step 9:

[1325] The server stores all data and alert history in a database for later analysis and reporting. The input is real-time collected data and issued alerts, and the output is historical data stored in the database. The stored data is backed up regularly.

[1326] 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.

[1327] The present invention relates to a system for ensuring the health and safety of athletes, incorporating an emotion engine, and is equipped with functions for collecting data from wearable devices, analyzing the data, generating alerts, and notifying the athletes. A specific embodiment of this system will be described below.

[1328] Data collection

[1329] 1. Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[1330] 2. Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[1331] 3. Device: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[1332] Data transmission and storage

[1333] 1. Server: The vital data and location information received from the device are sent to the server via the Internet.

[1334] 2. Server: The server stores the received data in a database for later analysis and historical reference.

[1335] Data analysis

[1336] 1. Server: The server runs machine learning algorithms to analyze collected vital data in real time, detecting abnormal patterns, including comparing it with historical data.

[1337] 2. Server: The server simultaneously receives video data during play and uses video analysis algorithms to monitor the players' movements and check for any abnormalities.

[1338] 3. Server: The emotion engine analyzes the player's vital and behavioral data to assess their emotional state.

[1339] Alert generation and notification

[1340] 1. Server: If the server detects an abnormal value based on the analysis results, it immediately generates an alert. The alert includes the type of abnormality and its urgency.

[1341] 2. Server: The emotion engine evaluates the player's emotional state, and if the emotional state is determined to be abnormal, it generates an alert that takes the emotional state into account.

[1342] 3. Server: Sends generated alerts in real time to designated contacts such as coaches, trainers, and doctors.

[1343] 4. Server: If necessary, analyze the emotional state of coaches, trainers, and doctors and send alerts at the appropriate time.

[1344] Actual response

[1345] 1. Device: Coaches and trainers who receive the alert can check the notification via an application on their smartphone or tablet.

[1346] 2. User: Based on the notification, take appropriate action on the spot (e.g., pause play and check on the health status of players).

[1347] Data logging and management

[1348] 1. Server: All data and alert history is stored in a database for later analysis and reporting.

[1349] 2. Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[1350] Specific examples

[1351] While Player B is playing a basketball game, the wearable device measures his heart rate every second, and the data is sent to a server via a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player B's heart rate is abnormal. Immediate check is required." Furthermore, if the emotion engine determines that Player B's emotional state is also abnormal, it generates an additional alert based on that. The coach checks the notification on his smartphone and takes action to directly check on Player B's condition. In this way, the present invention provides a specific method for implementing a system that provides real-time data collection, analysis, alert generation, and notification functions to ensure the health and safety of players.

[1352] The processing flow will be explained below.

[1353] Step 1:

[1354] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second.

[1355] Step 2:

[1356] Terminal: Transmits the measured data to a dedicated application via Bluetooth or Wi-Fi.

[1357] Step 3:

[1358] Terminal: The dedicated application sends the received vital data to the server via the Internet. It also sends the location information obtained from the GPS device to the server.

[1359] Step 4:

[1360] Server: The received vital data and location information are converted into an appropriate format and stored in the database.

[1361] Step 5:

[1362] Server: Runs machine learning algorithms to analyze stored vital data in real time and detect abnormal patterns.

[1363] Step 6:

[1364] Server: At the same time, it receives video data during play and uses video analysis algorithms to monitor players' movements and check for any abnormalities.

[1365] Step 7:

[1366] Server: The emotion engine analyzes the player's vital and behavioral data to assess their emotional state.

[1367] Step 8:

[1368] Server: Based on the analysis results and the evaluation of the emotion engine, an alert is generated if an abnormal value is detected. The alert includes the type of abnormality, urgency, and the evaluation result of the emotional state.

[1369] Step 9:

[1370] Server: Generated alerts are sent in real time to designated contacts such as coaches, trainers, and doctors. If necessary, the server also analyzes the emotional state of the coaches, trainers, and doctors and notifies them of the alerts at the appropriate time.

[1371] Step 10:

[1372] Device: Displays a notification on the smartphone or tablet of the coach or trainer who received the alert.

[1373] Step 11:

[1374] User: Review the notification and take appropriate action on the spot (e.g., pause play and check on player health).

[1375] Step 12:

[1376] Server: All data and alert history is stored in a database for later analysis and reporting.

[1377] Step 13:

[1378] Users: Log in to the system to view past data and use it to manage athlete performance and health over the long term.

[1379] Example 2

[1380] 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."

[1381] In recent years, there has been an increasing emphasis on managing the health and safety of athletes in sports. In particular, there is a demand for systems that can monitor athletes' vital signs and behavioral data in real time and detect abnormalities. However, conventional methods can sometimes be slow to detect abnormalities, increasing the risk to athletes' health. Furthermore, there are also issues with athletes and staff not being able to respond appropriately because they are unable to take into account their emotional state. Furthermore, there are very few systems that take into account the emotional state of coaches and trainers and issue alerts at the appropriate time.

[1382] 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 vital data in real time from wearable devices worn by athletes; means for analyzing the collected vital data and location information and identifying abnormal values; means for comparing past data and detecting abnormal patterns in the vital data using a machine learning algorithm; means for generating an alert based on the identified abnormal values ​​and notifying designated contacts; means for saving the history of collected data and issued alerts; and means for evaluating the athletes' emotional states using an emotion engine and detecting abnormalities. This makes it possible to monitor the athletes' health conditions in real time, and to immediately issue alerts and notify relevant parties if an abnormality is detected. Furthermore, taking the athletes' emotional states into consideration allows athletes and staff to respond quickly and appropriately. Furthermore, analyzing the emotional states of coaches and trainers and notifying them at optimal times enables more effective responses.

[1383] A "wearable device" refers to a device worn by an athlete that measures vital data in real time and transmits the data.

[1384] "Vital data" refers to biometric information that indicates an athlete's health status, such as heart rate, body temperature, and blood pressure.

[1385] "Location information" refers to data indicating the player's current location, and is information obtained using technology such as GPS.

[1386] "Analysis" refers to the process of evaluating and analyzing collected data to detect abnormal patterns or characteristics.

[1387] A "machine learning algorithm" refers to a computational method that learns from past data and automatically recognizes patterns in new data.

[1388] An "abnormal pattern" refers to a player's vital or behavioral data that deviates from the normal range and may indicate a health risk.

[1389] An "alert" is a warning notification that is generated when an abnormal value or pattern is detected and is transmitted to designated contacts.

[1390] The "emotion engine" refers to a system that evaluates and judges a player's emotional state by analyzing their vital and behavioral data.

[1391] "Notification" means the act of sending a generated Alert to designated contacts in real time.

[1392] A "database" refers to a system that stores collected data and the history of issued alerts, and manages them so that they can be referenced and analyzed as needed.

[1393] The present invention is a system that combines a wearable device, an analysis algorithm, an emotion engine, and a notification function to ensure the health and safety of athletes. A specific embodiment of this system will be described below.

[1394] Data collection

[1395] Device: A wearable device attached to the athlete measures vital data such as heart rate, body temperature, and blood pressure every second. This device could be a commercial fitness tracker or sports watch, for example.

[1396] Terminal: The measured data is sent to a dedicated application via Bluetooth or Wi-Fi. The dedicated application is typically a smartphone application.

[1397] Terminal: The dedicated application sends the received vital data and GPS data to a server via the Internet. The GPS device used here can be a smartphone or a dedicated GPS tracker.

[1398] Data transmission and storage

[1399] Server: A cloud-based server is used to process vital data and location information received from the device via the internet.

[1400] Server: The received data is stored in a database, typically an SQL database or a NoSQL database.

[1401] Data analysis

[1402] Server: Runs machine learning algorithms to analyze the vital data stored in the database in real time, using programming languages ​​such as Python and R and machine learning libraries such as TensorFlow and Scikit-learn.

[1403] Server: Applying video analysis algorithms to analyze collected video data in parallel. Specifically, video analysis libraries such as OpenCV and DeepStream are used.

[1404] Server: The emotion engine analyzes the players' vital data and behavioral data to evaluate their emotional state. The emotion engine uses Python NLP libraries and emotion analysis APIs.

[1405] Alert generation and notification

[1406] Server: If an abnormal value is detected based on the analysis results, an alert is generated. This alert includes the type of abnormality and its urgency.

[1407] Server: The emotion engine evaluates the player's emotional state, and if it is determined to be abnormal, it generates an alert that takes the emotional state into account.

[1408] Server: Generated alerts are sent in real time to designated contacts, which may include coaches, trainers, doctors, etc.

[1409] Server: If necessary, analyze the emotional state of coaches, trainers, and doctors and send alerts at the appropriate time.

[1410] Actual response

[1411] Device: Coaches and trainers who receive the alert can check the notification through an application on their smartphone or tablet.

[1412] User: Based on the notification, take appropriate action on the ground—for example, pausing play and directly checking on the player's health status.

[1413] Data logging and management

[1414] Server: All data and alert history is stored in a database for future data analysis and reporting.

[1415] Users: Athletes and officials can log in to the system and check past data, enabling long-term management of athletes' performance and health.

[1416] Specific examples and prompts for the generative AI model

[1417] 1. Example: When Player B is playing a basketball game, the wearable device measures his heart rate every second, and the data is sent to the server by a dedicated application. The server analyzes the data in real time, and if the heart rate is abnormally high, it sends an alert to the coach saying, "Player B's heart rate is abnormal. Immediate check is required." Furthermore, if the emotion engine determines that Player B's emotional state is also abnormal, it generates an additional alert based on that. The coach checks the notification on his smartphone and takes action to directly check on Player B's condition. This system provides real-time data collection, analysis, alert generation, and notification functions to ensure the health and safety of players.

[1418] 2. Generative AI model prompt:

[1419] Using the information below, please describe in detail a program that analyzes player health and generates necessary alerts.

[1420] Wearable devices measure vital data such as heart rate, body temperature, and blood pressure every second.

[1421] Measurement data is received by a dedicated application and sent to a server via the Internet.

[1422] The server stores the data in a database and analyzes it in real time using machine learning algorithms.

[1423] Emotion engine analyzes vital and behavioral data to assess emotional state

[1424] If anomalies or abnormal emotions are detected, alerts are generated and notify coaches and trainers in real time.

[1425] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1426] Step 1:

[1427] Device: A wearable device attached to the player measures vital data such as heart rate, body temperature, and blood pressure every second. Here, sensors acquire each vital data and store it in internal memory. The input is the player's vital signs, and the output is the vital data stored in the device's internal memory.

[1428] Step 2:

[1429] Terminal: The wearable device sends the measured data to a dedicated application via Bluetooth or Wi-Fi. At this time, the data transmission module inside the device is activated and sends the vital data in packet form to the application. The input is the vital data in the device's internal memory, and the output is the data sent to the dedicated application.

[1430] Step 3:

[1431] Terminal: The dedicated application sends the received vital data and GPS data to a server via the Internet. The application receives the data and then sends it to the server using the TCP / IP protocol. The input is the vital data and GPS data received by the dedicated application, and the output is the data sent to the server via the Internet.

[1432] Step 4:

[1433] Server: The vital data and location information received via the Internet are stored in a database. The server buffers the data and then uses an SQL query to store it in the database. The input is the vital data and location information received from the Internet, and the output is the data stored in the database.

[1434] Step 5:

[1435] Server: Executes machine learning algorithms to analyze the vital data stored in the database. The server reads data from the database and performs analysis using Python or R. For example, comparison with past data and time series analysis are used to detect abnormal patterns. The input is the vital data read from the database, and the output is the analysis results.

[1436] Step 6:

[1437] Server: Analyzes the collected video data using a video analysis algorithm. The server receives the video data and analyzes the player's movements using libraries such as OpenCV and DeepStream. The input is the video data, and the output is the analysis result indicating whether or not there are any abnormalities in the player's movements.

[1438] Step 7:

[1439] Server: The emotion engine analyzes the player's vital data and behavioral data to evaluate their emotional state. Here, emotion recognition is performed using an NLP library and emotion analysis API. The input is vital data and behavioral data, and the output is the evaluation result of the player's emotional state.

[1440] Step 8:

[1441] Server: Generates an alert if an abnormal value is detected based on the analysis results. The server evaluates the analysis results and generates an alert message if an abnormality is detected. The input is the analysis results and the output is the generated alert.

[1442] Step 9:

[1443] Server: Sends generated alerts in real time to designated contacts. The server prepares alert messages and sends them to contacts such as coaches, trainers, doctors, etc. The input is the generated alert and the output is the notification sent to the designated contacts.

[1444] Step 10:

[1445] Device: The coach or trainer who receives the alert checks the notification through an application on their smartphone or tablet. The application notifies the user of the alert message via a pop-up display or push notification. The input is the received alert message, and the output is the notification displayed to the user.

[1446] Step 11:

[1447] User: Based on the notification content, take appropriate action on the spot. For example, pause play and directly check the health status of the players. In this case, the user follows the instructions within the application to check and take action in real time. The input is the notification content, and the output is the action taken.

[1448] Step 12:

[1449] Server: All data and alert history is stored in a database for later analysis and reporting. The server adds new data and manages history. The input is newly acquired data and alert history, and the output is an updated database.

[1450] Step 13:

[1451] Users: Athletes and related personnel log in to the system to check past data. This enables long-term health management and performance analysis. The input is past data, and the output is displayed data.

[1452] (Application example 2)

[1453] 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."

[1454] It is difficult to monitor the health and safety of factory workers in real time, and there is a high risk of serious accidents occurring due to poor health or stress while working. In particular, conventional systems have been slow to collect and analyze vital data, making it difficult to respond quickly. Another issue is that they are unable to properly assess workers' emotional state and provide support at the appropriate time.

[1455] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data in real time from the wearable devices worn by the athletes, means for analyzing the collected vital data and location information and identifying abnormal values, and means for generating an alert based on the identified abnormal values ​​and notifying designated contacts. This enables health and safety management of factory workers.

[1456] A "wearable device" is a device that is worn on the body and collects vital data in real time.

[1457] "Vital data" refers to physiological data that indicates the health status of the human body, such as heart rate, body temperature, and blood pressure.

[1458] "Location Information" means data on the current geographic location of athletes and workers, obtained using a GPS device or the like.

[1459] An "emotion analysis engine" is a system that analyzes vital data and behavioral data to assess a subject's emotional state.

[1460] An "abnormal value" is data such as vital data or location information that deviates from the normal range and indicates a physical or mental abnormality.

[1461] An "alert" is a warning message that is generated when an abnormal value is detected and sent to designated contacts.

[1462] "Contacts" are people such as coaches, trainers, doctors, etc. who are set to receive alerts and their contact methods.

[1463] "Factory worker health and safety management" is a process that monitors the health and safety of factory workers in real time and prompts rapid response when abnormalities are detected.

[1464] A "database" is a system for storing collected data and the history of issued alerts.

[1465] The present invention is a system for monitoring the health and safety of factory workers in real time, and can be implemented as follows.

[1466] Hardware and Software Configuration

[1467] Hardware:

[1468] Wearable devices: Worn by factory workers to measure vital data such as heart rate, body temperature, and blood pressure.

[1469] Communication device: Uses Bluetooth or Wi-Fi to transmit data from the wearable device to a dedicated application.

[1470] Server: A processing device that analyzes collected data and detects abnormal values.

[1471] software:

[1472] Dedicated application: Sends data collected from the wearable device to a server.

[1473] Data analysis software: Using the Python libraries pandas and IsolationForest, the collected vital data is analyzed to detect anomalies.

[1474] Sentiment Analysis Engine: Uses Hugging Face transformers to assess the emotional state of factory workers from text data.

[1475] Data collection and transmission

[1476] The terminal collects vital data in real time from wearable devices worn by factory workers. The collected data is sent to a dedicated application via Bluetooth or Wi-Fi, and then transmitted over the Internet to a server. The server stores the collected data in a database.

[1477] Analyzing data and generating alerts

[1478] The server analyzes the received vital data and location information in real time to detect abnormal patterns. This analysis is performed using the machine learning algorithm IsolationForest. If an abnormality is detected, an alert is generated and notified to designated contacts in real time. In addition, an emotion analysis engine is used to evaluate the emotional state of factory workers, and if an abnormality is detected, an emotion-based alert is also generated.

[1479] Alert notification and response

[1480] Alerts generated by the server are sent in real time to designated coaches, trainers, medical staff, etc. Users who receive the notifications can check the content of the notifications through an application on their smartphone or tablet and take necessary action, such as temporarily halting work and directly checking the health status of factory workers.

[1481] Specific examples

[1482] As a concrete example, consider a situation where Worker A is performing welding work in a factory. At this time, a wearable device collects data that Worker A's heart rate exceeds 150 and his body temperature exceeds 38 degrees. This data is sent to a server via a dedicated application, which analyzes it and detects abnormalities. If an abnormality is detected, an alert is sent to the administrator stating, "Worker A's heart rate and body temperature are abnormal. Immediate check is required." The emotion analysis engine then analyzes the text data, "I'm feeling very stressed out," and an additional emotion alert is generated.

[1483] Prompt Sentence Examples

[1484] While welding, Worker A's heart rate exceeds 150 and his body temperature exceeds 38°C. The system determines this to be an abnormality and sends an emergency alert to the administrator.

[1485] Worker B enters the text "I'm feeling very stressed out" into the system. The emotion analysis engine determines this as a high-stress state and generates an alert.

[1486] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1487] Step 1:

[1488] The terminal collects real-time vital data (heart rate, body temperature, blood pressure) from wearable devices worn by factory workers. The collected data is measured every second. The input data is heart rate, body temperature, and blood pressure, and the output is sent to a dedicated application via Bluetooth or Wi-Fi.

[1489] Step 2:

[1490] The device sends the collected vital data to a dedicated application via Bluetooth or Wi-Fi. The application then sends the data to a server via the Internet. The input data is the vital data, and the output is the raw data sent to the server.

[1491] Step 3:

[1492] The server stores the received vital data in a database. The input is the raw data sent via the internet, and the output is the data stored in the database. The database also contains historical data.

[1493] Step 4:

[1494] The server analyzes the collected data in real time and uses the Isolation Forest algorithm to detect abnormal patterns. The input is the latest vital data stored in the database, and the output is a judgment result on whether the value is abnormal or not.

[1495] Step 5:

[1496] The server uses an emotion analysis engine to evaluate the emotional state of the factory workers. The input is the text data of the factory workers, and the output is the evaluation result of the emotional state. Hugging Face Transformers are used for emotion analysis.

[1497] Step 6:

[1498] The server generates alerts based on the results of anomaly analysis and emotion analysis. The inputs are the anomaly detection results and the emotional state evaluation results, and the output is the generated alert. The alert includes the type of anomaly and its urgency.

[1499] Step 7:

[1500] The server notifies designated contacts (such as administrators or medical staff) of generated alerts in real time. The input is the generated alert and the output is the notification sent to the contact.

[1501] Step 8:

[1502] Users (administrators or medical staff) check the notification content through a smartphone or tablet application and take the necessary action. The input is the notification content, and the output is specific actions such as checking the health status of factory workers or suspending work.

[1503] 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.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] 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.

[1508] 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.

[1509] 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).

[1510] 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.

[1511] 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."

[1512] 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.

[1513] 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).

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] 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.

[1520] 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.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] The following is further disclosed regarding the above embodiment.

[1525] (Claim 1)

[1526] A means for collecting vital data in real time from a wearable device attached to the player;

[1527] A means for analyzing the collected vital data and location information and determining abnormal values;

[1528] means for generating alerts based on the determined anomalies and notifying designated contacts;

[1529] A means of storing the history of collected data and issued alerts;

[1530] A system including:

[1531] (Claim 2)

[1532] A means of collecting and analyzing video data of the gameplay;

[1533] 10. The system of claim 1, further comprising: means for detecting an abnormality in a player's movement from the video data and generating an alert.

[1534] (Claim 3)

[1535] A means to notify designated contacts such as coaches, trainers, and doctors of alerts in real time;

[1536] 10. The system of claim 1, further comprising: means for providing an application for a coach, trainer, or doctor to take action on the player based on the notification content.

[1537] "Example 1"

[1538] (Claim 1)

[1539] a means for collecting biometric data in real time from a wearable device attached to the athlete;

[1540] a data analysis means for analyzing the collected biometric data and location information and determining abnormal values;

[1541] means for generating alerts based on the determined anomalies and notifying designated contacts;

[1542] a means for storing a history of collected data and issued alerts;

[1543] A system including:

[1544] (Claim 2)

[1545] A means of collecting and analyzing video data of the gameplay;

[1546] 10. The system of claim 1, further comprising: means for detecting anomalies in player movements from the video data and generating a warning.

[1547] (Claim 3)

[1548] A means of notifying designated contacts, such as coaches, trainers, or medical professionals, of alerts in real time;

[1549] and means for providing an application for a coach, trainer, or medical professional to take action on the athlete based on the notification content.

[1550] "Application Example 1"

[1551] (Claim 1)

[1552] A means for collecting vital data in real time from a wearable device attached to the player;

[1553] A means for analyzing the collected vital data and location information and determining abnormal values;

[1554] means for generating alerts based on the determined anomalies and notifying designated contacts;

[1555] A means for collecting vital data of factory workers from the wearable device and transmitting the data to a server;

[1556] A means to analyze data in real time on the server and generate and notify alerts when an abnormality is detected.

[1557] A means of storing the history of collected data and issued alerts;

[1558] A system including:

[1559] (Claim 2)

[1560] A means of collecting and analyzing video data of the gameplay;

[1561] 10. The system of claim 1, further comprising: means for detecting an abnormality in a player's movement from the video data and generating an alert.

[1562] (Claim 3)

[1563] A means of notifying responsible parties of alerts in real time; and

[1564] and means for providing an application for a responsible person to take action against a factory worker based on the content of the notification.

[1565] "Example 2: Combining Emotion Engines"

[1566] (Claim 1)

[1567] A means for collecting vital data in real time from a wearable device attached to the player;

[1568] A means for analyzing the collected vital data and location information and determining abnormal values;

[1569] a means for detecting abnormal patterns in the vital data by comparing historical data and using machine learning algorithms;

[1570] means for generating alerts based on the determined anomalies and notifying designated contacts;

[1571] A means of storing the history of collected data and issued alerts;

[1572] a means for assessing the emotional state of the player using an emotion engine and detecting anomalies;

[1573] A system including:

[1574] (Claim 2)

[1575] A means of collecting and analyzing video data of the gameplay;

[1576] 10. The system of claim 1, further comprising: means for detecting an abnormality in a player's movement from the video data and generating an alert.

[1577] (Claim 3)

[1578] A means to notify designated contacts such as coaches, trainers, and doctors of alerts in real time;

[1579] A means for providing an application for coaches, trainers, and doctors to take action on athletes based on the notification content;

[1580] 10. The system of claim 1, further comprising: means for analyzing the emotional state of a player or coach and issuing an alert at an appropriate time.

[1581] "Application example 2 when combining emotion engines"

[1582] (Claim 1)

[1583] A means for collecting vital data in real time from a wearable device attached to the player;

[1584] A means for analyzing the collected vital data and location information and determining abnormal values;

[1585] means for generating alerts based on the determined anomalies and notifying designated contacts;

[1586] A means of storing the history of collected data and issued alerts;

[1587] A means to incorporate an emotion analysis engine, evaluate emotional states from vital data and behavioral data, and detect abnormalities;

[1588] Systems including applications aimed at managing the health and safety of factory workers.

[1589] (Claim 2)

[1590] A means of collecting and analyzing video data of the gameplay;

[1591] and means for detecting an abnormality in a player's movement from the video data and generating an alert.

[1592] 10. The system of claim 1.

[1593] (Claim 3)

[1594] A means to notify designated contacts such as coaches, trainers, and doctors of alerts in real time;

[1595] and means for providing an application for a coach, trainer, or doctor to take action against the athlete based on the notification content.

[1596] 10. The system of claim 1. [Explanation of symbols]

[1597] 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 collecting vital data in real time from a wearable device attached to the player; A means for analyzing the collected vital data and location information and determining abnormal values; means for generating alerts based on the determined anomalies and notifying designated contacts; A means of storing the history of collected data and issued alerts; A system including:

2. A means of collecting and analyzing video data of the gameplay; The system of claim 1 , further comprising: means for detecting an abnormality in a player's movement from the video data and generating an alert.

3. A means to notify designated contacts such as coaches, trainers, and doctors of alerts in real time; The system of claim 1, further comprising: means for providing an application for a coach, trainer, or doctor to take action on the player based on the notification content.

Images