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
