Recognizing hand use and hand role at home after stroke from egocentric video.

ABSTRACT: Hand function is a central determinant of independence after stroke. Measuring hand use in the home environment is necessary to evaluate the impact of new interventions, and calls for novel wearable technologies. Egocentric video can capture hand-object interactions in context, as well as show how more-affected hands are used during bilateral tasks (for stabilization or manipulation). Automated methods are required to extract this information. The objective of this study was to use artificial intelligence-based computer vision to classify hand use and hand role from egocentric videos recorded at home after stroke. Twenty-one stroke survivors participated in the study. A random forest classifier, a SlowFast neural network, and the Hand Object Detector neural network were applied to identify

SUBMITTER: Tsai MF 

PROVIDER: S-EPMC10566743 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature