Evaluating the performance of raw and epoch non-wear algorithms using multiple accelerometers and electrocardiogram recordings.

ABSTRACT: Accurate detection of accelerometer non-wear time is crucial for calculating physical activity summary statistics. In this study, we evaluated three epoch-based non-wear algorithms (Hecht, Troiano, and Choi) and one raw-based algorithm (Hees). In addition, we performed a sensitivity analysis to provide insight into the relationship between the algorithms' hyperparameters and classification performance, as well as to generate tuned hyperparameter values to better detect episodes of wear and non-wear time. We used machine learning to construct a gold-standard dataset by combining two accelerometers and electrocardiogram recordings. The Hecht and Troiano algorithms achieved poor classification performance, while Choi exhibited moderate performance. Meanwhile, Hees outperformed all epoch-based

SUBMITTER: Syed S 

PROVIDER: S-EPMC7125135 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature