Automatically Segmenting Physical Performance Test Items for Older Adults Using a Doppler Radar: a Proof of Concept Study

Assessing the performance of physical activities through the modified physical performance test (mPPT) is a known approach for predicting the frailty level in older adults. This study proposes a system comprising of a continuous-wave (CW) radar for data acquisition and deep neural network (DNN) models (convolutional neural network (CNN) and convolutional recurrent neural network (CRNN)) as classifiers to automatically segment the mPPT items. These two DNN models were trained and evaluated in a leave-one- participant-out (LOPO) cross-validation procedure with a transfer learning method. To segment the mPPT items during recording by the radar, an additional flag activity was employed, which involves having the volunteer wave their hands at the start of each activity. Compared to the CNN, the CRNN achieved better classification performance, with the f1-score ranging from 0.3445 (lifting a book) to 0.9509 (standing balance). The recognition result was then used to segment the time series data and predict each item's duration. The average absolute duration prediction error ranged from 0.78 s (standing balance) to 2.78 s (climbing stairs). The result implied that the system has the potential to segment mPPT items automatically. Future works will be focused on accomplish all the evaluation criteria automatically, e.g., the steadiness and continuity of steps while turning 360°, and improve the low classification result of some mPPT items like lifting a book.