Mobility evaluation system based on anomaly detection using an exercise exoskeleton robot

Abstract

Participants: Four Korean researchers will give presentations on their work as follow: K. Jang, J.-G. Lee, C. Lee and M. Kang. ISSUE Mobility is critical for quality of life among older adults. Walking and driving abilities enable older adults to participate in society. Classic assessment tools such as Katz Index of Independence in Activities of Daily Living (ADLs)-in particular the item of transferring-have contributed to enhancing the knowledge related to mobility among older adults. However, the advancement of technology calls for alternative way to assess mobility among older adults. CONTENT This symposium aims to present the use of technology in assessing mobility among older adults in a twofold way: (a) machine learning application into big data and (b) use of wearable robot. STRUCTURE The first two presentations focus on older drivers. K. Jang's research group will present their research findings about driving characteristics of taxi drivers by age groups based on the application of deep learning-based analysis into a large-scale naturalistic data of driving. J.-G. Lee's research group will discuss the differences in driving offenses across four age groups of older taxi drivers using the data from Digital Tachograph (DTG) devices. The second two presentations address the use of wearable robot. Y. Kim's research group will give two presentations, and the first one will present a multi-dimensional mobility evaluation system for people in later life by using an exercise exoskeleton robot. The last presentation will show reliable indicators for evaluating muscle activation using wearable robot. CONCLUSION The advancement of artificial intelligence and robots would contribute to assessing mobility and driving abilities among older adults in a more accurate way. This symposium introduces novel applications of existing technologies in assessing functions of older adults both in micro and macro ways.