From activity recognition to motion assessment: delimitate against the other class within a WBAN

Wearable sensor nodes use activity recognition systems to identify human activities. However, several applications such as physical rehabilitation and professional sports coaching require not only the identification of motion but also quality assessment to provide appropriate feedback to the user. In this work, we present AAC, a generalized trainable process chain for the online assessment of periodic human activity within a WBAN. 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 delineates movements of correctly performed activity to faulty ones and provides detailed reasons for the activity assessment. Both are crucial for an appropriate feedback to the user.