Weighted Average of Human Motion Sequences for Improving Rehabilitation Assessment

While human motion analysis has been widely addressed in recent years, the specific task of rehabilitation motion assessment remains challenging due to the lack of available annotated data. To overcome this challenge, data augmentation can be considered. However, classical augmentation techniques applied to human motion sequences often result in meaningless movements. Moreover, in rehabilitation assessment, labels are often continuous values illustrating the quality of a movement. Hence, associating a continuous label to augmented data is not straightforward. In this work, we propose to address data augmentation using an averaging method, called shapeDBA, adapted to rehabilitation motion sequences represented as multivariate time series. We extend the original proposal by weighting the average, hence allowing us to infer continuous labels associated to augmented motion sequences. We evaluated our proposed method on the Kimore dataset. Experimental results show that our method generates coherent rehabilitation sequences that can be efficiently used to extend a small dataset for rehabilitation assessment.