van der Slikke, R.; de Leeuw, A.-W.; de Rooij, A.; Berger, M. The Push Forward in Rehabilitation: Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type. Sensors2024, 24, 657.
van der Slikke, R.; de Leeuw, A.-W.; de Rooij, A.; Berger, M. The Push Forward in Rehabilitation: Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type. Sensors 2024, 24, 657.
van der Slikke, R.; de Leeuw, A.-W.; de Rooij, A.; Berger, M. The Push Forward in Rehabilitation: Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type. Sensors2024, 24, 657.
van der Slikke, R.; de Leeuw, A.-W.; de Rooij, A.; Berger, M. The Push Forward in Rehabilitation: Validation of a Machine Learning Method for Detection of Wheelchair Propulsion Type. Sensors 2024, 24, 657.
Abstract
Within rehabilitation, there is great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self or attendant pushed wheelchair propulsion. The validity was determined by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.
Public Health and Healthcare, Physical Therapy, Sports Therapy and Rehabilitation
Copyright:
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