WO2021004510A1 - Système de gestion de santé à reconnaissance de comportement de corps humain déployé séparément à base de capteur - Google Patents
Système de gestion de santé à reconnaissance de comportement de corps humain déployé séparément à base de capteur Download PDFInfo
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- WO2021004510A1 WO2021004510A1 PCT/CN2020/101145 CN2020101145W WO2021004510A1 WO 2021004510 A1 WO2021004510 A1 WO 2021004510A1 CN 2020101145 W CN2020101145 W CN 2020101145W WO 2021004510 A1 WO2021004510 A1 WO 2021004510A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Definitions
- the invention relates to the field of pattern recognition of machine learning in human-computer interaction and artificial intelligence, and in particular to a human behavior recognition health management system based on separate deployment of sensors.
- Human behavior recognition is an important issue of human-computer interaction in the field of pervasive computing. It plays an important role in promoting new types of human-computer interaction and making computers better understand and assist users in completing tasks.
- the human behavior recognition problem is theoretically a pattern recognition problem in machine learning.
- image and video recognition and sensor recognition Both solutions have many corresponding studies.
- the identification scheme generally requires a fixed place, has low portability, and is not suitable for individual users.
- traditional machine learning methods and deep learning methods target specific behaviors, and there are already better solutions in offline computing environments.
- Portable computing devices such as smart phones or smart bracelets can provide a more flexible boundary carrier for the human behavior recognition system, and at the same time provide more exploration for the application of the human behavior recognition system, such as individual user sports and health monitoring. .
- the purpose of the present invention is to overcome the shortcomings of the existing system and propose a separate deployment of a sensor-based human behavior recognition health management system.
- the present invention proposes a separate deployment of sensor-based human behavior recognition health management system, the system includes:
- the client includes a user interaction module and a data collection module, which are deployed on the user's personal terminal such as a smart phone or smart bracelet to interact with the user and collect user behavior data.
- the server includes a model recognition module, a data analysis module, and a suggestion module, which are deployed on a remote host or server to identify the behavior data of the human body and perform corresponding data analysis to provide suggestions.
- the client and the server communicate through the network.
- the user interaction module is composed of user personal information management, personal behavior records, and suggestion reminders, and provides users with basic interactive operations, including user personal information management, displaying the history of their personal behaviors, and displaying suggestions.
- the data collection module collects user behavior data, uses the sensor of the hardware where the client is located to collect corresponding data, including 3-axis acceleration sensor data, inertial sensor data, and preprocesses the collected data to reduce The amount of data transmitted over the network.
- the model recognition module adopts a machine learning method and uses the computing hardware of the server to quickly and accurately recognize the data uploaded by the user, and feedback the recognition result to the user interaction module as feedback, and pass the recognition result to Suggested modules.
- the data analysis module analyzes the user's movement in the past period of time according to the user's historical behavior records and the user's personal status and gives corresponding suggestions.
- the suggestion module makes corresponding suggestions to the user according to the personal status set by the user, such as whether the amount of exercise reaches the standard, whether certain exercises should be reduced too much, and the sitting time is too long to get up and exercise.
- the sensor-based separated deployment human behavior recognition health management system proposed in the present invention provides a separated human behavior recognition system that is easy to deploy, provides more comprehensive recognition behavior types and more complete and effective service reminders, and also The accuracy and speed of recognition are further improved, and it is convenient to provide users with more in-depth personalized services.
- Figure 1 is a system architecture diagram of an embodiment of the present invention
- Fig. 2 is a flowchart of a data collection module according to an embodiment of the present invention.
- Fig. 1 is a system architecture diagram of an embodiment of the present invention. As shown in Fig. 1, the system includes:
- the client and the server are composed of two parts.
- the client includes a user interaction module and a data collection module, which are deployed on the user's personal terminal such as a smart phone or smart bracelet for interacting with the user and collecting user behavior data.
- the server includes a model recognition module, a data analysis module, and a suggestion module, which are deployed on a remote host or server to identify the behavior data of the human body and perform corresponding data analysis to provide suggestions.
- the client and the server communicate through the network.
- the user interaction module is composed of user personal information management, personal behavior records, suggestion reminders, and provides users with basic interactive operations, including user personal information management, displaying the history of their personal behaviors, displaying suggestions, etc. .
- the user uploads personal information in the client interactive module, while the collection module collects user behavior data and preprocesses it.
- the personal information management sub-module can submit and modify the user's personal information, including basic information such as name, age, gender, height, weight, etc. This information will be synchronized to the personal database of the server, and the suggestion module of the server will be combined with the user Personal information and behavioral data provide corresponding suggestions.
- Personal behavior record sub-module which can view daily behavior records in the form of charts, including the occurrence and duration of various behaviors, and the amount of exercise generated.
- the suggestion reminder sub-module the suggestion message of the suggestion module of the server is sent back to the module and displayed to the user, while assisting some simple and basic reminder functions: sedentary reminder, when the sitting time exceeds 1 hour, it will raise activity reminder; Excessive exercise reminder, exercise energy consumption exceeds a certain range to remind rest and supplement energy.
- FIG. S1-2, Figure 2 is a flowchart of a data acquisition module, which is composed of a sensor acquisition sub-module and a data pre-processing sub-module.
- the data collection module collects the user's behavior data, uses the sensor of the hardware where the client is located to collect the corresponding data, including 3-axis acceleration sensor data, inertial sensor data, and preprocesses the collected data to reduce network transmission data the amount.
- the three-axis gyroscope and the three-axis acceleration sensor collect data at a frequency of 20HZ.
- the data is smoothed by sliding window filtering.
- the data is simplified through operations.
- the data is standardized through normalization operations.
- the data is divided into paragraphs through sliding windows.
- the sensor collection sub-module collects data by using the sensor of the client hardware.
- a smart phone such as an Android phone
- the phone’s 3-axis acceleration sensor and inertial sensor (gyro) are collected by the corresponding application program interface.
- the frequency of collecting data is set to 20 Hz.
- Si is the data at time i
- the data preprocessing sub-module performs simple preprocessing operations on the data after collecting the data.
- the data is smoothed by sliding window filtering, and the window size is 2 seconds.
- the specific filtering formula is, Where w is the window size.
- the specific synthesis formula is, Where As represents the composite value of acceleration or gyroscope data, and Ax, Ay, and Az represent the components of its x, y, and z axes, respectively.
- Vmax and Vmin are the maximum and minimum values of the same feature. After normalization, the data is scaled to between 0 and 1.
- the original data is segmented using sliding window segmentation.
- the segmented window size is 2 seconds.
- X min min ⁇ X 1 ,X 2 ,...,X w ⁇
- the user interaction module synchronizes the information with the server and receives the information feedback from the server through the network information exchange interface.
- the characteristic data finally obtained by the data acquisition module is sent to the server through the network information exchange interface of the client.
- the model recognition module adopts a machine learning method, uses the computing hardware of the server to quickly and accurately recognize the data uploaded by the user, and feeds back the recognition result to the user interaction module as feedback, and transmits the recognition result Give suggestions for modules.
- the model recognition module uses the user behavior data from the client to use the hardware calculation of the server to recognize the behavior through the integrated learning method in machine learning.
- the server uses the xgboost model to train a behavior recognition model in an offline state, and uses the trained model to recognize user behavior data from the client. By replacing the new model on the server side, the recognized behavior types can be effectively expanded, making it more flexible to adapt to the user's customized behavior needs.
- the behavior data passed by the client is segmented feature data.
- the parameter settings for the xgboost model are as follows: objective is the training target parameter, select "multi:softmax” for multi-classification, and set the number of categories parameter num_class to the target category number 11; eval_metric is the evaluation index parameter, select "merror” to indicate Multi-class error rate; lambda and alpha are the shadows of L1 and L2 regular penalty items, the parameter is set to 0, eta is the learning step size, set to 0.3; max_depth is the maximum depth, set to 12.
- the behaviors recognized by this system include typing and writing in a sitting state, walking, running, going upstairs, going downstairs, cycling, push-ups, sit-ups, squats, rope skipping, and a total of 11 behaviors, namely recognition behaviors.
- a, a ⁇ writing, typing , walk, run, upstairs, downstairs, riding, pushup, situp, squat, ropeskipping ⁇ by xgboost model
- user behavior data F i is identified as the behavior of the user is most likely carried out a i, A i ⁇ A.
- the data analysis module analyzes the user's exercise situation in the past period of time according to the user's historical behavior record and the user's personal status and gives corresponding suggestions, and forwards the analyzed suggestions to the suggestion module.
- This module will record the user's behavior data history, and according to the change history, statistics and analysis of the user's daily, weekly, and monthly various types of behaviors, combined with the user's height and weight and other information on different types of behavior Make suggestions to increase or decrease, such as for exercise behavior. If the amount of exercise is insufficient, it is recommended to increase the amount of exercise the next day; if the amount of exercise is large, it is recommended to rest the next day; if the running time is too long, it is recommended to reduce the amount of exercise , Protect the knees.
- the suggestion module makes corresponding suggestions to the user according to the personal status set by the user, such as whether the amount of exercise reaches the standard, whether certain exercises should be reduced too much, and the sitting time is too long to get up and exercise.
- the suggestion module returns the current behavior suggestions and the analyzed suggestions to the client, and at the same time gives corresponding suggestions based on real-time behavior monitoring, such as reminding the user based on the behavior time of the user's sitting state, reminding the user to get up and move, for different sitting Behavior, if writing for a long time, remind the active wrist, if the typing time is long, it will additionally remind the user to take a proper rest with the eyes, the user interaction module of the client will show the user and provide suggestion reminders.
- the embodiment of the present invention proposes a separate deployment of a sensor-based human behavior recognition health management system, provides a separate human behavior recognition system that is easy to deploy, provides more comprehensive recognition behavior types and more complete and effective service reminders, and at the same time It also further improves the accuracy and speed of recognition, which is convenient to provide users with more in-depth personalized services.
- the program can be stored in a computer-readable storage medium, and the storage medium can include: Read only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.
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CN116740813A (zh) * | 2023-06-20 | 2023-09-12 | 深圳市视壮科技有限公司 | 一种基于ai图像识别行为监测的分析系统及其方法 |
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CN110443145A (zh) * | 2019-07-09 | 2019-11-12 | 中山大学 | 基于传感器的分离式部署的人体行为识别健康管理系统 |
CN111063437B (zh) * | 2019-12-12 | 2024-01-23 | 中科海微(北京)科技有限公司 | 一种个性化慢病分析系统 |
CN111700624B (zh) * | 2020-07-27 | 2024-03-12 | 中国科学院合肥物质科学研究院 | 一种智能手环检测运动姿态的模式识别方法及系统 |
CN112217837B (zh) * | 2020-10-27 | 2023-07-14 | 常州信息职业技术学院 | 一种人体行为动作信息采集系统 |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090075781A1 (en) * | 2007-09-18 | 2009-03-19 | Sensei, Inc. | System for incorporating data from biometric devices into a feedback message to a mobile device |
CN105095214A (zh) * | 2014-04-22 | 2015-11-25 | 北京三星通信技术研究有限公司 | 基于运动识别进行信息推荐的方法及装置 |
CN105590022A (zh) * | 2014-11-11 | 2016-05-18 | 宏达国际电子股份有限公司 | 身体状况建议方法及电子装置 |
CN109584989A (zh) * | 2018-11-27 | 2019-04-05 | 北京羽扇智信息科技有限公司 | 一种运动提示信息的推送方法、装置、设备及存储介质 |
CN110443145A (zh) * | 2019-07-09 | 2019-11-12 | 中山大学 | 基于传感器的分离式部署的人体行为识别健康管理系统 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP5322834B2 (ja) * | 2009-08-06 | 2013-10-23 | 日本電信電話株式会社 | 情報表示システム及び情報表示方法 |
-
2019
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- 2020-07-09 WO PCT/CN2020/101145 patent/WO2021004510A1/fr active Application Filing
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090075781A1 (en) * | 2007-09-18 | 2009-03-19 | Sensei, Inc. | System for incorporating data from biometric devices into a feedback message to a mobile device |
CN105095214A (zh) * | 2014-04-22 | 2015-11-25 | 北京三星通信技术研究有限公司 | 基于运动识别进行信息推荐的方法及装置 |
CN105590022A (zh) * | 2014-11-11 | 2016-05-18 | 宏达国际电子股份有限公司 | 身体状况建议方法及电子装置 |
CN109584989A (zh) * | 2018-11-27 | 2019-04-05 | 北京羽扇智信息科技有限公司 | 一种运动提示信息的推送方法、装置、设备及存储介质 |
CN110443145A (zh) * | 2019-07-09 | 2019-11-12 | 中山大学 | 基于传感器的分离式部署的人体行为识别健康管理系统 |
Non-Patent Citations (2)
Title |
---|
CHARISSA ANN RONAO ET AL.: "Human activity recognition with smartphone sensors using deep learning neural networks", EXPERT SYSTEMS WITH APPLICATIONS, 26 April 2016 (2016-04-26), XP029539400 * |
SHENG, MING ET AL.: "A Moblie Health Service Platform Based on Big Data Analysis", SMART HEALTHCARE, no. 2,, 29 February 2016 (2016-02-29), ISSN: 2096-1219 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116740813A (zh) * | 2023-06-20 | 2023-09-12 | 深圳市视壮科技有限公司 | 一种基于ai图像识别行为监测的分析系统及其方法 |
CN116740813B (zh) * | 2023-06-20 | 2024-01-05 | 深圳市视壮科技有限公司 | 一种基于ai图像识别行为监测的分析系统及其方法 |
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