Classification-based Segmentation for Rehabilitation Exercise Monitoring:

摘要

IntroductionExercise 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.MethodsThis 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.ResultsUsing 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.ConclusionA real-...