A large harmonized upper and lower limb accelerometry dataset: A resource for rehabilitation scientists.

ABSTRACT: Wearable sensors can measure movement in daily life, an outcome that is salient to patients, and have been critical to accelerating progress in rehabilitation research and practice. However, collecting and processing sensor data is burdensome, leaving many scientists with limited access to such data. To address these challenges, we present a harmonized, wearable sensor dataset that combines 2,885 recording days of sensor data from the upper and lower limbs from eight studies. The dataset includes 790 individuals ages 0 - 90, nearly equal sex proportions (53% male, 47% female), and representation from a range of demographic backgrounds (69.4% White, 24.9% Black, 1.8% Asian) and clinical conditions (46% neurotypical, 31% stroke, 7% Parkinson's disease, 6% orthopaedic conditions, and others).

SUBMITTER: Miller AE 

PROVIDER: S-EPMC12681975 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature