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Physiotherapy is a labor-intensive process that has become increasingly inaccessible. Existing telehealth solutions overcome many of the logistical problems, but they are cumbersome to re-calibrate for the various exercises involved. To facilitate self-exercise efficiently, we developed a framework for personalized physiotherapy exercises. Our approach eliminates the need to re-calibrate for different exercises, using only few userspecific demonstrations available during collocated therapy. Two types of augmented feedback are available to the user for self-correction. The framework's utility was demonstrated for the sit-to-stand task, an important activity of daily living. Although further testing is necessary, our results suggest that the framework can be generalized to the learning of arbitrary motor behaviors.

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Understanding effective sit-to-stand (STS) movement is essential for improving rehabilitation strategies and developing services for the rapidly increasing number of elderly people. This study aims at identifying effective STS therapy by analyzing the kinematic synergies of movements induced by therapists of different skill-levels. Three synergies were found to share the same temporal pattern in both joint angles and center-of-mass spaces across all therapists. Effective strategy used by a skilled therapist and strategy flaws of less-experienced therapists were revealed through comparison of spatial patterns.