Old person nursing system based on AI intelligent algorithm

Abstract

The invention discloses an elderly nursing system based on an AI intelligent algorithm, and belongs to the technical field of elderly nursing systems, and the elderly nursing system comprises a non-contact sensing equipment module which is used for collecting physiological data and behavior information of the elderly in real time; the AI intelligent algorithm module is used for analyzing behavior modes of the old people by utilizing an artificial intelligence technology, monitoring daily activities and predicting potential health risks; the real-time early warning system module is used for immediately giving out early warning or alarm when abnormal behaviors or health problems are detected; a data analysis and storage module; according to the elderly nursing system based on the AI intelligent algorithm, the state of the elderly can be monitored in real time, abnormal conditions can be found in time, early warning is carried out, the sleep quality and the life safety level of the elderly are effectively improved, and the system can be widely applied to sickbed nursing of homes, nursing homes and health systems, has high practical value and popularization prospects and is worthy of popularization. The definite operation process of the nursing personnel is beneficial for improving the nursing efficiency and quality, and the old people are ensured to be effectively cared in time.

Description

Technical Field

[0001] The present invention belongs to the technical field of elderly care systems, and in particular relates to an elderly care system based on an AI intelligent algorithm. Background Art

[0002] As the population ages, the safety and health issues of the elderly are becoming increasingly prominent. Traditional care methods often rely on manual supervision, which has problems such as low efficiency and inability to monitor around the clock.

[0003] Therefore, there is an urgent need for an intelligent and automated care system to improve the quality of life and safety level of the elderly. Summary of the invention

[0004] The purpose of the present invention is to propose an elderly care system based on AI intelligent algorithm in order to solve the above-mentioned problems.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solution: an elderly care system based on AI intelligent algorithm, which includes:

[0006] Non-contact sensor device module, which is used to collect physiological data and behavioral information of the elderly in real time;

[0007] AI intelligent algorithm module, which uses artificial intelligence technology to analyze the behavior patterns of the elderly, monitor daily activities and predict potential health risks;

[0008] A real-time early warning system module that immediately issues a warning or alarm when it detects abnormal behavior or health problems;

[0009] The data analysis and storage module stores monitoring data and performs data analysis.

[0010] As a further description of the above technical solution:

[0011] The non-contact sensing device module includes a smart sleep pillow, a fall monitoring camera, an environmental sensor and an intelligent human fall detection radar. The smart sleep pillow has built-in sensors to detect heart rate, breathing rate, body movement status, etc., and analyze sleep quality. The fall monitoring camera monitors people falling and alarms. The environmental sensor monitors indoor CO2 concentration, light, noise, temperature, humidity and other environmental factors. The intelligent human fall detection radar monitors people falling and alarms.

[0012] As a further description of the above technical solution:

[0013] The intelligent human fall detection radar uses 60GHz millimeter wave radar technology to detect falls within a radius of 1.5 meters in real time.

[0014] As a further description of the above technical solution:

[0015] It also includes a user interface module, which includes a mobile terminal app and a large monitoring screen for checking the status of the elderly.

[0016] As a further description of the above technical solution:

[0017] The fall monitoring camera adopts the RTSP protocol.

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0019] In the present invention, the elderly care system based on AI intelligent algorithm can monitor the status of the elderly in real time, detect abnormal situations in time and issue warnings, effectively improving the elderly's sleep quality and life safety level. The system can be widely used for bedside care at home, nursing homes and health systems, and has high practical value and promotion prospects. At the same time, clear caregiver operating procedures help improve care efficiency and quality, ensuring that the elderly receive timely and effective care. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a system block diagram of an elderly care system based on AI intelligent algorithm. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Embodiment 1:

[0023] The non-contact sensor device module includes a smart sleep pillow, a fall monitoring camera, an environmental sensor, and a smart human fall detection radar, which are used to collect the elderly's physiological data and behavioral information in real time, that is, multi-functional monitoring: it can detect multiple indicators such as heart rate, respiratory rate, body movement / in bed / out of bed status, respiratory disorders, sleep status and sleep quality;

[0024] AI intelligent algorithm module: uses artificial intelligence technology to analyze the behavior patterns of the elderly, monitor daily activities and predict potential health risks;

[0025] Real-time warning system module: When abnormal behavior (such as falling) or health problems (such as apnea) are detected, the system will immediately issue a warning or alarm;

[0026] Data analysis and storage module: The system can store monitoring data for later playback and analysis, helping nursing staff evaluate and improve nursing services.

[0027] Specific location:

[0028] Smart Sleep Pillow (size 48x74cm): Analyzes sleep quality through built-in sensors to detect heart rate, breathing rate, body movement, etc.

[0029] Fall monitoring camera (size 11.5x9cm): uses RTSP protocol to monitor people falling in real time and alarm through AI algorithm;

[0030] Environmental sensor (size 17x17cm): monitors indoor CO2 concentration, light, noise, temperature, humidity and other environmental factors;

[0031] Intelligent human fall detection radar (size 7.2x8.5x3cm): uses 60GHz millimeter wave radar technology to detect falls within a radius of 1.5 meters in real time;

[0032] Central processing unit: integrates data from various sensors and runs AI algorithms for analysis and early warning;

[0033] User interface: includes mobile terminal app and large monitoring screen, which allows caregivers to check the elderly’s status in real time.

[0034] Embodiment 2:

[0035] The non-contact sensor device module includes a smart sleep pillow, a fall monitoring camera, an environmental sensor, and a smart human fall detection radar, which are used to collect the elderly's physiological data and behavioral information in real time, that is, multi-functional monitoring: it can detect multiple indicators such as heart rate, respiratory rate, body movement / in bed / out of bed status, respiratory disorders, sleep status and sleep quality;

[0036] AI intelligent algorithm module: uses artificial intelligence technology to analyze the behavior patterns of the elderly, monitor daily activities and predict potential health risks;

[0037] Real-time warning system module: When abnormal behavior (such as falling) or health problems (such as apnea) are detected, the system will immediately issue a warning or alarm;

[0038] Data analysis and storage module: The system can store monitoring data for later playback and analysis, helping nursing staff evaluate and improve nursing services.

[0039] Specific location:

[0040] Non-sense sleep monitoring pillow: built-in sensor, real-time detection of human physiological signals (heart rate, breathing, etc.), and report data to the background for analysis, can detect heart rate, breathing rate, body movement / in bed / out of bed, breathing disorders, sleep state and sleep quality analysis (including light sleep, deep sleep and rapid eye movement (REM) sleep), communication mode is 4G, input voltage is 5V, rated power is less than 5W, working temperature is 0-50℃. You can choose pillows, neck pillows, waist pillows or mattresses according to user needs;

[0041] Fall monitoring camera: HD camera, combined with AI algorithm, performs real-time motion analysis, monitors people falling and issues alarms. The communication method is WIFI, the resolution is 1080p 5MP, the focal length is 2.8mm-3.6mm, the input voltage is DC12V 2A, the photosensitive area is 1 / 3 inch, and the specific specifications can be customized.

[0042] Environmental monitoring sensor: collects indoor temperature / humidity, carbon dioxide, noise, light and other environmental parameters that affect sleep, and analyzes the sleep environment in combination with sleep data. The communication method is 4G, the working voltage is DC5V±0.2V@150mA, the working environment is -10-60℃, and the working humidity is 5-95%RH (no condensation).

[0043] Specific specifications can be customized;

[0044] Intelligent human fall detection radar: millimeter wave human presence sensor, monitors the user's indoor position and activity, and combines AI algorithm for motion analysis to determine if the user has fallen. The communication method is WIFI, the power supply voltage is DC12V, the power is ≤3W, the effective fall monitoring radius is 1.5 meters, the operating frequency is 60GHz, and the operating temperature is -20-60℃. The specific specifications can be customized.

[0045] The system uses non-contact sensor devices to monitor the elderly's body movements, breathing, heart rate and other human physiological data in real time, promptly detects abnormal safety and health conditions, and uses AI technology to analyze the elderly's behavior patterns, monitor daily activities and predict potential health risks, such as apnea, anxiety, abnormal movements, etc. The system also has a built-in interactive entertainment module that provides artificial intelligence dialogue, e-books, music, etc., to enrich the spiritual life of the elderly and promote mental health;

[0046] The intelligent nursing system in nursing homes ensures the safety and health of the elderly by monitoring their activities in real time and providing doctor-patient interaction functions. The system can store surveillance videos for later playback and analysis, helping caregivers evaluate and improve nursing services. Using artificial intelligence technology, the system can identify abnormal behaviors of the elderly and notify caregivers in time for intervention.

[0047] Combining SMS, email, app push and other methods, it ensures that medical staff can receive early warning information in a timely manner in different occasions. By real-time monitoring of patients' vital signs data, the system can immediately detect abnormalities and trigger early warnings. It uses artificial intelligence algorithms to analyze historical data, predict potential risks, and issue early warnings to medical staff.

[0048] The system uses non-contact devices to monitor the elderly's body movements, heart rate, respiratory rate and other human physiological parameters in real time, detects abnormalities in a timely manner, and uses AI to analyze the elderly's daily behavior patterns. Once behavior deviates from the normal range, the system automatically issues an alert. The intelligent system sets medication reminders based on doctor's orders and records the elderly's medication history to ensure medication safety.

[0049] Caregiver operation process:

[0050] S01: Caregivers need to ensure that all devices in the system are operating normally, such as checking whether the non-sense sleep monitoring pillow, fall monitoring camera, environmental monitoring sensor, and intelligent human fall detection radar are connected normally and have sufficient power;

[0051] S02: Regularly check the system background data, including the elderly's physiological data (heart rate, breathing, etc.), sleep status, behavior patterns, etc., to understand the elderly's health status in a timely manner;

[0052] S03: When the system issues a warning or alarm signal, the caregiver should respond immediately, check relevant information, such as the location of the elderly, the type of abnormal situation, etc., and quickly go to the scene to deal with it;

[0053] S04: When dealing with abnormal situations of the elderly, caregivers should take appropriate measures according to the actual situation, such as helping the fallen elderly, checking the elderly's physical condition, contacting doctors, etc.;

[0054] S05: Afterwards, the caregiver should record the abnormal condition of the elderly and the treatment process for subsequent analysis and improvement;

[0055] S06: Regularly maintain and upgrade the system to ensure system stability and accuracy.

[0056] In actual applications, devices such as contactless sleep monitoring pillows, fall monitoring cameras, environmental monitoring sensors and intelligent human fall detection radars are installed in the elderly’s living environment, such as homes, nursing homes or beds in health systems. These devices collect the elderly’s physiological and environmental data in real time, and transmit the data to the background server for analysis and processing.

[0057] The AI ​​intelligent algorithm analyzes the collected data in real time to detect the elderly's movements, behaviors and sleep status. When the elderly are found to have abnormal behavior, fall, sleep disorders, snoring or apnea, the system immediately issues a warning or alarm signal to notify relevant personnel to handle it. At the same time, the system can also provide personalized sleep improvement suggestions based on the elderly's sleep data to improve the elderly's sleep quality.

[0058] Specific technical implementation of AI intelligent algorithm in this elderly care system:

[0059] 1. Deep learning algorithm:

[0060] A convolutional neural network (CNN) is used to process the image data collected by the fall monitoring camera to identify the movements and postures of the elderly and determine whether a fall has occurred.

[0061] The physiological signals obtained by the non-contact sleep monitoring pillow are analyzed using a recurrent neural network (RNN) or a long short-term memory network (LSTM) to predict sleep status and breathing patterns, and detect sleep disorders, snoring, and apnea.

[0062] 2. Feature extraction and pattern recognition:

[0063] Features are extracted from data from environmental monitoring sensors, such as temperature, humidity, carbon dioxide concentration, etc., and machine learning algorithms (such as support vector machines, decision trees, etc.) are used to identify patterns related to the health of the elderly in order to assess the impact of the sleeping environment on the elderly.

[0064] Feature extraction is performed on the daily behavior data of the elderly, including activity level, behavior patterns, etc., and classifiers are trained to detect behavioral abnormalities and potential health risks.

[0065] 3. Anomaly Detection Algorithm:

[0066] Adopt statistical-based anomaly detection methods, such as setting thresholds or using normal distribution models, to monitor the physiological data of the elderly in real time and trigger an early warning when the data exceeds the normal range.

[0067] Apply clustering algorithms or outlier detection algorithms to identify anomalies in the elderly’s behavior patterns and detect abnormal situations in a timely manner.

[0068] 4. Multimodal data fusion:

[0069] The data from multiple data sources, such as the contactless sleep monitoring pillow, fall monitoring camera, and environmental monitoring sensor, are integrated to comprehensively analyze the physiological state, behavior, and environmental factors of the elderly to improve the accuracy of judgment.

[0070] Use multimodal fusion techniques in deep learning, such as attention mechanism or feature fusion layer, to automatically learn the relationship between data of different modalities and achieve more comprehensive monitoring of the elderly's status.

[0071] 5. Reinforcement Learning:

[0072] Through reinforcement learning algorithms, the training system intelligently adjusts early warning strategies and care measures based on the elderly’s historical data and current status to improve care outcomes and the elderly’s quality of life.

[0073] 6. Natural Language Processing

[0074] In the interactive entertainment module, natural language processing technology is used to enable the elderly to have natural conversations with the system to obtain information, entertainment and emotional support.

[0075] Using speech recognition and synthesis technology, it is convenient for the elderly to communicate with caregivers and convey their needs and situations in a timely manner.

[0076] In addition, the system can also be connected with the information system of medical institutions to realize data sharing and telemedicine services, providing more comprehensive health protection for the elderly.

[0077] The system can share data and interconnect with other health monitoring devices through interfaces to achieve more comprehensive health monitoring. For example:

[0078] 1. Data connection: The system can be connected with the information system of medical institutions to transmit the physiological data and monitoring results of the elderly in real time, so as to facilitate doctors to conduct remote diagnosis and treatment.

[0079] 2. Multi-device linkage: Other health monitoring devices (such as blood pressure monitors, blood glucose meters, etc.) can be connected to the system, and data can be integrated through a unified platform to provide a more comprehensive health status assessment.

[0080] 3. Intelligent reminders: The system can set personalized health management plans based on data from other devices, such as medication reminders, dietary recommendations, etc., to ensure that the health management of the elderly is more scientific.

[0081] 4. Data analysis: Comprehensively analyze data from different devices through AI algorithms to identify potential health risks and issue early warnings.

[0082] 5. User interface integration: A unified user interface can be developed to allow caregivers and family members to easily view all health monitoring data and improve management efficiency.

[0083] Through these methods, the system can form a synergistic effect with other health monitoring devices to improve the overall health management level of the elderly.

[0084] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An elderly care system based on AI intelligent algorithm, characterized by: include: Non-contact sensor device module, which is used to collect physiological data and behavioral information of the elderly in real time; AI intelligent algorithm module, which uses artificial intelligence technology to analyze the behavior patterns of the elderly, monitor daily activities and predict potential health risks; A real-time early warning system module that immediately issues a warning or alarm when it detects abnormal behavior or health problems; The data analysis and storage module stores monitoring data and performs data analysis.

2. According to claim 1, the elderly care system based on AI intelligent algorithm is characterized in that: The non-contact sensing device module includes a smart sleep pillow, a fall monitoring camera, an environmental sensor and an intelligent human fall detection radar. The smart sleep pillow has built-in sensors to detect heart rate, breathing rate, body movement status, etc., and analyze sleep quality. The fall monitoring camera monitors people falling and alarms. The environmental sensor monitors indoor CO2 concentration, light, noise, temperature, humidity and other environmental factors. The intelligent human fall detection radar monitors people falling and alarms.

3. The elderly care system based on AI intelligent algorithm according to claim 2 is characterized in that: The intelligent human fall detection radar uses 60GHz millimeter wave radar technology to detect falls within a radius of 1.5 meters in real time.

4. According to claim 3, the elderly care system based on AI intelligent algorithm is characterized in that: It also includes a user interface module, which includes a mobile terminal app and a large monitoring screen for checking the status of the elderly.

5. The elderly care system based on AI intelligent algorithm according to claim 4 is characterized in that: The fall monitoring camera adopts the RTSP protocol.

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