Wearable human activity recognition systems (HAR) using inertial measurement units (IMU) play a key role in the development of smart rehabilitation systems. Training of a HAR system with patient data is costly, time-consuming, and difficult for the patients. This study proposes a new scheme for the optimal design of HARs with minimal involvement of the patients. It uses healthy subject data for optimal design for a set of activities used in the rehabilitation of PD1 patients. It maintains its performance for individual PD subjects using a single session data collection and an adaptation procedure. In the optimal design, several classifiers (i.e. NM, k-NN, MLP with RBF as a hidden layer, and multistage RBF SVM) were investigated. Features were signal-based in the time, frequency, and time-frequency domains. Double-stage feature extraction by PCA and fisher technique was used. The optimal design reached a recall of 95% on healthy subjects using only two sensors on the left thigh and forearm. Implementing the adaptation procedure on two PD subjects, the performance was maintained above 80%. Post analysis on the performance of the adapted HAR showed a slight drop in precision (above 87% to above 81%) for activities that was performed in sitting condition.
Keywords: Classification; Human activity recognition; Parkinson's disease; Tele-rehabilitation.
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