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.
BACKGROUND Physical activity (PA) 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. OBJECTIVE The purpose 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 for adding further behavior classes to an existing algorithm. Combining data, we were potentially able to classify 11 behaviors, using a Random Forest learning scheme. 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 a validated algorithm. RESULTS In the simulated free-living validation, the performance of the algorithm decreased to 64% as a weighted average for the 11 classes (F-measure). After reducing to 5 classes corresponding with the validated algorithm, the result revealed high performance in comparison with both the ground truth and the validated algorithm. CONCLUSIONS We developed an algorithm to classify 11 physical behaviors. We obtained high classification levels within specific behaviors, while others yielded lower classification potential.
Study Design: Retrospective observational study Objectives: 1) Describe weight change during and after in-hospital rehabilitation based on a large sample of retrospectively collected data. 2) Investigate associations between initial functional level and the change in weight, during and after in-hospital rehabilitation. Setting: Spinal Cord Injury Center of Western Denmark Methods: We extracted relevant information from a database of electronic hospital records in the period June 2013 to March 2023 on people admitted for in-hospital rehabilitation after their first time spinal cord injury. We used the routinely gathered information such as weight measurements and Spinal Cord Injury Independence Measure to assess weight change and the association to initial functional levels using multiple linear regression both during and after in-hospital rehabilitation. Results: During in-hospital rehabilitation (n = 579) the mean weight change was estimated at -0.25 kg 95% CI, -1.06–0.56 (p = 0.548), while subgroups of BMI revealed diverse patterns. After rehabilitation (n = 365) mean weight change was estimated at 2.47 kg 95% CI, 0.65–4.28 (p = 0.008). SCIM selfcare was associated with weight gain during in-hospital rehabilitation, and weight loss after discharge. Conclusions: On average individuals with SCI had stable weight during in-hospital rehabilitation. When stratified on BMI groups individuals with initial low BMI increase their weight, while individuals with initial high BMI decrease their weight during in-hospital rehabilitation. After in-hospital rehabilitation individuals on average increase their weight, regardless of their initial BMI. SCIM selfcare revealed an association between weight change both within and after in-hospital rehabilitation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.