Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke.

ABSTRACT: Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and op

SUBMITTER: Pohl J 

PROVIDER: S-EPMC9554104 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature