Print Email Facebook Twitter Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data Title Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach Author de Vries, Wiebe H.K. (Swiss Paraplegic Research) Amrein, Sabrina (Swiss Paraplegic Research; ETH Zürich) Arnet, Ursina (Swiss Paraplegic Research) Mayrhuber, Laura (Swiss Paraplegic Research) Ehrmann, Cristina (Swiss Paraplegic Research) Veeger, H.E.J. (TU Delft Biomechanical Engineering) Department Biomechanical Engineering Date 2022 Abstract Shoulder problems (pain and pathology) are highly prevalent in manual wheelchair users with spinal cord injury. These problems lead to limitations in activities of daily life (ADL), labor- and leisure participation, and increase the health care costs. Shoulder problems are often associated with the long-term reliance on the upper limbs, and the accompanying “shoulder load”. To make an estimation of daily shoulder load, it is crucial to know which ADL are performed and how these are executed in the free-living environment (in terms of magnitude, frequency, and duration). The aim of this study was to develop and validate methodology for the classification of wheelchair related shoulder loading ADL (SL-ADL) from wearable sensor data. Ten able bodied participants equipped with five Shimmer sensors on a wheelchair and upper extremity performed eight relevant SL-ADL. Deep learning networks using bidirectional long short-term memory networks were trained on sensor data (acceleration, gyroscope signals and EMG), using video annotated activities as the target. Overall, the trained algorithm performed well, with an accuracy of 98% and specificity of 99%. When reducing the input for training the network to data from only one sensor, the overall performance decreased to around 80% for all performance measures. The use of only forearm sensor data led to a better performance than the use of the upper arm sensor data. It can be concluded that a generalizable algorithm could be trained by a deep learning network to classify wheelchair related SL-ADL from the wearable sensor data. Subject classificationdeep learningshoulder loading activitieswearable sensorswheelchair To reference this document use: http://resolver.tudelft.nl/uuid:848760ed-77ba-4f42-ae83-70c8841c7209 DOI https://doi.org/10.3390/s22197404 ISSN 1424-8220 Source Sensors, 22 (19) Part of collection Institutional Repository Document type journal article Rights © 2022 Wiebe H.K. de Vries, Sabrina Amrein, Ursina Arnet, Laura Mayrhuber, Cristina Ehrmann, H.E.J. Veeger Files PDF sensors_22_07404_v2.pdf 2.67 MB Close viewer /islandora/object/uuid:848760ed-77ba-4f42-ae83-70c8841c7209/datastream/OBJ/view