TY - JOUR A1 - Seiffert, Martin A1 - Holstein, Flavio A1 - Schlosser, Rainer A1 - Schiller, Jochen T1 - Next generation cooperative wearables T2 - IEEE access : practical research, open solutions N2 - Currently available wearables are usually based on a single sensor node with integrated capabilities for classifying different activities. The next generation of cooperative wearables could be able to identify not only activities, but also to evaluate them qualitatively using the data of several sensor nodes attached to the body, to provide detailed feedback for the improvement of the execution. Especially within the application domains of sports and health-care, such immediate feedback to the execution of body movements is crucial for (re-) learning and improving motor skills. To enable such systems for a broad range of activities, generalized approaches for human motion assessment within sensor networks are required. In this paper, we present a generalized trainable activity assessment chain (AAC) for the online assessment of periodic human activity within a wireless body area network. AAC evaluates the execution of separate movements of a prior trained activity on a fine-grained quality scale. We connect qualitative assessment with human knowledge by projecting the AAC on the hierarchical decomposition of motion performed by the human body as well as establishing the assessment on a kinematic evaluation of biomechanically distinct motion fragments. We evaluate AAC in a real-world setting and show that AAC successfully delimits the movements of correctly performed activity from faulty executions and provides detailed reasons for the activity assessment. KW - Body sensor networks KW - distributed computing KW - motion analysis KW - physical activity assessment KW - biomechanics KW - multilevel systems Y1 - 2017 UR - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/55827 SN - 2169-3536 VL - 5 SP - 16793 EP - 16807 PB - Institute of Electrical and Electronics Engineers CY - Piscataway ER -