Low-Complexity Complexity Framework for Movement Classification Using Body-Worn Body Sensors
Abstract: We present a low-complexity complexity framework for classifying elementary arm movements (reach retrieve, lift cup to mouth, and rotate arm) using wrist-worn wrist inertial sensors. We propose that this methodology could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies tracking occurrence of specific movements performed by patients with their paretic arm. Movements performed in a controlled training phase are processed to form unique clusters in a multidimensional feature space. Subsequent movements performed in an uncontrolled testing phase are associated with the proximal cluster using a minimum distance classifier (MDC). The framework involves performing the compute-intensive intensive clustering on the training data set offline (MATLAB), ), whereas the computation of selected features on the testing data set and the minimum distance (Euclidean) from precomputed cluster centroids are done in hardware with an aim of low low-power power execution on sensor nodes. The architecture for feature extraction and MDC are realized using coordinate rotation digital computer-based based design that classifies a movement in (9n + 31) clock cycles, n being number of data samples. The design synthesized in STMicroelectronics 130-nm nm technology consumed 5.3 nW at 50 Hz, besides bes being functionally verified up to 20 MHz, making it applicable for real real-time time high-speed high operations. Our experimental results show that the system can recognize all three