Within rehabilitation, there is a 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 aim of this study was to validate this new detection method 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.
Vragen waar Sport & Society zich op richt zijn: – Hoe draagt sport bij aan een gezonde en vitale samenleving? – Welke rol speelt sport bij de opgroeien ontwikkelkansen van jongeren? – Hoe kan sport leiden tot verbroedering?
Pitchpresentatie tijdens tweede stakeholderbijeenkomst in het kader van de Sport Toekomstverkenning (STV), georganiseerd door het Sociaal en Cultureel Planbureau (SCP) en het Rijksinstituut voor Volksgezondheid en Milieu (RIVM).
