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• W3081080889 abstract "Digital home rehabilitation systems require accurate segmentation methods to provide appropriate feedback on repetition counting and exercise technique. Current segmentation methods are not suitable for clinical use; they are not highly accurate or require multiple sensors, which creates usability problems. We propose a model for accurately segmenting inertial measurement unit data for shoulder rehabilitation exercises. This study aims to use inertial measurement unit data to train and test a machine learning segmentation model for single- and multiple-inertial measurement unit systems and to identify the optimal single-sensor location.A focus group of specialist physiotherapists selected the exercises, which were performed by participants wearing inertial measurement units on the wrist, arm and scapula. We applied a novel machine learning based segmentation technique involving a convolutional classifier and Finite State Machine to the inertial measurement unit data. An accuracy score was calculated for each possible single- or multiple-sensor system.The wrist inertial measurement unit was chosen as the optimal single-sensor location for future system development (mean overall accuracy 0.871). Flexion and abduction based exercises mostly could be segmented with high accuracy, but scapular movement exercises had poor accuracy.A wrist-worn single inertial measurement unit system can accurately segment shoulder exercise repetitions; however, accuracy varies depending on characteristics of the exercise." @default.
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• W3081080889 date "2020-01-01" @default.
• W3081080889 modified "2023-10-02" @default.
• W3081080889 title "Segmentation of shoulder rehabilitation exercises for single and multiple inertial sensor systems" @default.
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• W3081080889 doi "https://doi.org/10.1177/2055668320915377" @default.
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