Classification-based Segmentation for Rehabilitation Exercise Monitoring

Exercise segmentation, the process of isolating individual repetitions from continuous time series measurement of human motion, is key to providing online feedback to patients during rehabilitation and enables the computation of useful metrics such as joint velocity and range of motion that are otherwise difficult to measure in the clinical setting.

This paper proposes a classifier-based approach, where the motion segmentation problem is formulated as a two-class classification problem, classifying between segment and non-segment points. The proposed approach does not require domain knowledge of the exercises and generalizes to groups of participants and exercises that were not part of the training set, allowing for more robustness in clinical applications.

Using only data from healthy participants for training, the proposed algorithm achieves an average segmentation accuracy of 92% on a 30-participant healthy dataset and 87% on a 44-patient rehabilitation dataset.

A real-time approach for segmentation of rehabilitation exercises is proposed, based on two-class classification approach. The method is validated on both healthy and rehabilitation motion datasets and generalizes to a variety of demographics and exercises not part of the training set.