Mobile Spatiotemporal Gait Segmentation Using an Ear-Worn Motion Sensor and Deep Learning.

ABSTRACT: Mobile health technologies enable continuous, quantitative assessment of mobility and gait in real-world environments, facilitating early diagnoses of gait disorders, disease progression monitoring, and prediction of adverse events like falls. Traditionally, mobile gait assessment predominantly relied on body-fixed sensors positioned at the feet or lower trunk. Here, we investigate the potential of an algorithm utilizing an ear-worn motion sensor for spatiotemporal segmentation of gait patterns. We collected 3D acceleration profiles from the ear-worn sensor during varied walking speeds in 53 healthy adults. Temporal convolutional networks were trained to detect stepping sequences and predict spatial relations between steps. The resulting algorithm, mEar, accurately detects initial and fina

SUBMITTER: Decker J 

PROVIDER: S-EPMC11479375 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature