TY - JOUR AU - Skovbjerg, Frederik AU - Honoré, Helene AU - Mechlenburg, Inger AU - Lipperts, Matthijs AU - Gade, Rikke AU - Næss-Schmidt, Erhard Trillingsgaard PY - 2022 DA - 2022/7/26 TI - Monitoring Physical Behavior in Rehabilitation Using a Machine Learning–Based Algorithm for Thigh-Mounted Accelerometers: Development and Validation Study JO - JMIR Bioinform Biotech SP - e38512 VL - 3 IS - 1 KW - activity recognition KW - random forest KW - acquired brain injury KW - biometric monitoring KW - machine learning KW - physical activity AB - Background: Physical activity is emerging as an outcome measure. Accelerometers have become an important tool in monitoring physical behavior, and newer analytical approaches of recognition methods increase the degree of details. Many studies have achieved high performance in the classification of physical behaviors through the use of multiple wearable sensors; however, multiple wearables can be impractical and lower compliance. Objective: The aim of this study was to develop and validate an algorithm for classifying several daily physical behaviors using a single thigh-mounted accelerometer and a supervised machine-learning scheme. Methods: We collected training data by adding the behavior classes—running, cycling, stair climbing, wheelchair ambulation, and vehicle driving—to an existing algorithm with the classes of sitting, lying, standing, walking, and transitioning. After combining the training data, we used a random forest learning scheme for model development. We validated the algorithm through a simulated free-living procedure using chest-mounted cameras for establishing the ground truth. Furthermore, we adjusted our algorithm and compared the performance with an existing algorithm based on vector thresholds. Results: We developed an algorithm to classify 11 physical behaviors relevant for rehabilitation. In the simulated free-living validation, the performance of the algorithm decreased to 57% as an average for the 11 classes (F-measure). After merging classes into sedentary behavior, standing, walking, running, and cycling, the result revealed high performance in comparison to both the ground truth and the existing algorithm. Conclusions: Using a single thigh-mounted accelerometer, we obtained high classification levels within specific behaviors. The behaviors classified with high levels of performance mostly occur in populations with higher levels of functioning. Further development should aim at describing behaviors within populations with lower levels of functioning. SN - 2563-3570 UR - https://bioinform.jmir.org/2022/1/e38512 UR - https://doi.org/10.2196/38512 DO - 10.2196/38512 ID - info:doi/10.2196/38512 ER -