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Preventive and therapeutic measures can contribute to maintain or to regain physical abilities. In Germany, the growing number of elderly people is posing serious challenges for the therapeutic sector. Therefore, the objective that has been pursued in recent research is to assist patients during their medical training by reproducing therapists’ feedback. Extant systems have been limited to feedback that is based on the evaluation of only the similarity between a pre-recorded reference and the currently performed motion. To date, very little is known about feedback generation that exceeds such similarity evaluations. Moreover, current systems require a personalised, pre-recorded reference for each patient in order to compare the reference against the motion performed during the exercise and to generate feedback. The aim of this study is to develop and evaluate an error classification algorithm for therapy exercises using Incremental Dynamic Time Warping and 3-D skeleton joint information. Furthermore, a normalisation method that allows the utilisation of nonpersonalised references has been investigated. In our experiments, we were able to successfully identify errors, even for non-personalised reference data, by using normalised hierarchical coordinates.

Motion Error Classification for Assisted Physical Therapy - A Novel Approach using Incremental Dynamic Time Warping and Normalised Hierarchical Skeleton Joint Data