In Japan's super-aged society, it is important to prolong independent daily living. This paper, we focus on walking, which is indispensable for daily living, to diagnose frailty. This study included acceleration data during limb-loaded walking to increase muscle strength in the analysis. Also, we classified and identified whether middle-aged and elderly persons are in a frail state or not were conducted following the Cardiovascular Health Study. After selecting useful features by random forest, a Support Vector Machine was used to identify the frailty state. As a result, we reported that we could identify frailty with an accuracy rate of 82%, suggesting the possibility of detecting low grip strength from the features extracted from gait data.
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