Building a Rehabilitation Walking Behavior Pattern Analysis System in Living Environments

Population aging is a major hidden concern of society.The biggest impact of population aging on society is the increasing demand for medical care and long-term care, resulting in a heavy burden on social welfare.Therefore, in this study, we designed a wearable device for the rehabilitation of the foot muscles of the elderly, combined with the posture analysis system and integrated with timely data for display on the web.Through IoT, we can not only know the foot muscle strength data, but also know whether the walking posture is standard and whether the user's walking posture is normal.The system can provide medical personnel with data to understand the foot strength health of the user during the process, to achieve the effects of prevention and rehabilitation.The rehabilitation of the elderly usually takes a lot of time.If someone needs to continue supervision, it will take a lot of time and personnel costs.In the long run, the cost of hospital services will increase.Therefore, we integrated wearable devices and IoT technology and present the muscle strength values collected by wearable devices on a web page.Through the analysis results of different methods, the data attribute and prediction prognosis system use the Extreme Gradient Boosting method with high accuracy, thus achieving the goal of cost research.The data analysis system classifies and presents the data on the web page.The nursing staff or medical personnel can view and understand the muscle strength of the rehabilitation personnel who are walking through the data analysis system on the web page.

Authors

Publication Details

Journal
Sensors and Materials
Published
2024-12-19
DOI
https://doi.org/10.18494/sam5117
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Building a Rehabilitation Walking Behavior Pattern Analysis System in Living Environments

Yun-Ru Guo, Yeou‐Jiunn Chen, Gwo-Jiun Horng, Chang Dong Cheng
Sensors and Materials
Context-Aware Activity Recognition Systems
article

Building a Rehabilitation Walking Behavior Pattern Analysis System in Living Environments

Yun-Ru Guo, Yeou‐Jiunn Chen, Gwo-Jiun Horng, Chang Dong Cheng
article en

Abstract

Population aging is a major hidden concern of society.The biggest impact of population aging on society is the increasing demand for medical care and long-term care, resulting in a heavy burden on social welfare.Therefore, in this study, we designed a wearable device for the rehabilitation of the foot muscles of the elderly, combined with the posture analysis system and integrated with timely data for display on the web.Through IoT, we can not only know the foot muscle strength data, but also know whether the walking posture is standard and whether the user's walking posture is normal.The system can provide medical personnel with data to understand the foot strength health of the user during the process, to achieve the effects of prevention and rehabilitation.The rehabilitation of the elderly usually takes a lot of time.If someone needs to continue supervision, it will take a lot of time and personnel costs.In the long run, the cost of hospital services will increase.Therefore, we integrated wearable devices and IoT technology and present the muscle strength values collected by wearable devices on a web page.Through the analysis results of different methods, the data attribute and prediction prognosis system use the Extreme Gradient Boosting method with high accuracy, thus achieving the goal of cost research.The data analysis system classifies and presents the data on the web page.The nursing staff or medical personnel can view and understand the muscle strength of the rehabilitation personnel who are walking through the data analysis system on the web page.

Sensors and MaterialsVol. 36(12)
Sustainable cities and communities
Openalex Percentile: Top 77%
Context-Aware Activity Recognition Systems
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