Abstract
Digital biofeedback systems (DBS) which use inertial measurement units (IMUs) can support patients during home rehabilitation. Models which accurately segment IMU data for rehabilitation exercises are required to provide biofeedback but assessing accuracy in a clinical context is challenging due to technical and patient-related factors. In this paper, we propose a three-stage validation framework to overcome these challenges. We present the results of stage one and two segmentation accuracy assessment for our DBS for shoulder rehabilitation. The results demonstrate that most of the chosen exercises can be segmented to a high level of accuracy in an unseen, uninstructed dataset. Errors in segmenting and recommendations for improvement are presented, which must be addressed prior to the final stage of validation.
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Brennan, L., Bevilacqua, A., Kechadi, T., Caulfield, B. (2021). A Novel Validation Framework to Assess Segmentation Accuracy of Inertial Sensor Data for Rehabilitation Exercises. In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds) 8th European Medical and Biological Engineering Conference. EMBEC 2020. IFMBE Proceedings, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-64610-3_4
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DOI: https://doi.org/10.1007/978-3-030-64610-3_4
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