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.
Deel drie van een serie artikelen in het vakblad Safety! waarin studenten die onderzoeksopdrachten doen voor innnovaties in de industrie bij het Saxion Kenniscentrum Design en Technologie. In dit artikel beschrijft begeleidend onderzoeker Eliza Bottenberg het onderzoek van de studenten Claudie Müller en Kerstin Stremlau. In het kader van het onderzoeksproject Veiligheid op de werkvloeren hebben zij sensoren ontworpen die bewegingen van werknemers monitoren via kleding.
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