A Feasible Fall Evaluation System via Artificial Intelligence Gesture Detection of Gait and Balance for Sub-Healthy Community- Dwelling Older Adults in Taiwan

Abstract

In Taiwan, falls are one of the major causes of permanent disability and seeking medical care in older adults. One in seven people in the Taiwanese population exceeds the age of 65 years, and there are roughly 4 million community-dwelling older adults. One in six older adults have fallen or been diagnosed with Sarcopenia, which can lead to a loss of mobility. The major identified risk factors are impaired balance and gait. Implementing an early-stage prevention system is already an urgent requirement. The primary objective of this study is to propose an artificial intelligence (AI) Internet of Things (IoT) program and to develop an easy-access fall prevention system. This study took the criteria of the Asian Working Group for Sarcopenia (AWGS) and implemented it in field practice. Field experts reviewed data from the combination of gait parameters and gesture parameters and adaptively modified the training course bi-weekly. With 3 months of field practice and intervention, sub-healthy older adults’ average increase in gait speed was 29.83% for male participants and 34.06% for female participants. The results of this study demonstrate that rehabilitation of older adults can significantly improve mobility. This helps to understand the relationship of gait and gesture patterns to walking stability and strategies and adaptive interventions that could be taught in expertise programs to minimize the risk of fall.

Keywords

• Artificial intelligence
• Internet of Things
• health promotion
• physical performance
• long-term care
• sub-healthy aging