LAUSR

Assessing the performance of physical activities through the modified physical performance test (mPPT) is a known approach for predicting frailty levels in older adults. This study proposes a system comprising 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 participants 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). This result implies that the system has the potential to automatically segment mPPT items. Future works will be focused on accomplishing all the evaluation criteria automatically, for example, the steadiness and continuity of steps while turning 360°, and improving the low classification result of some mPPT items, such as lifting a book.