Qualitative Assessment of Recurrent Human Motion
This addresses the need for qualitative monitoring in fitness applications to prevent injuries and optimize training, but it is incremental as it builds on existing motion tracking methods.
The paper tackled the problem of qualitative assessment of recurrent human motion, such as detecting malpositions during exercises, by processing motion sensor data, achieving 100% success in recognizing six exercise types and 99.3% average success in qualitative assessment.
Smartphone applications designed to track human motion in combination with wearable sensors, e.g., during physical exercising, raised huge attention recently. Commonly, they provide quantitative services, such as personalized training instructions or the counting of distances. But qualitative monitoring and assessment is still missing, e.g., to detect malpositions, to prevent injuries, or to optimize training success. We address this issue by presenting a concept for qualitative as well as generic assessment of recurrent human motion by processing multi-dimensional, continuous time series tracked with motion sensors. Therefore, our segmentation procedure extracts individual events of specific length and we propose expressive features to accomplish a qualitative motion assessment by supervised classification. We verified our approach within a comprehensive study encompassing 27 athletes undertaking different body weight exercises. We are able to recognize six different exercise types with a success rate of 100% and to assess them qualitatively with an average success rate of 99.3%.