Learning based lower limb joint kinematic estimation using open source IMU data.

ABSTRACT: This study introduces a deep learning framework for estimating lower-limb joint kinematics using inertial measurement units (IMUs). While deep learning methods avoid sensor drift, extensive calibration, and complex setup procedures, they require substantial data. To meet this demand, we leveraged an open-source dataset to develop and evaluate three training approaches. The first involved training a model exclusively on data from a single user, resulting in high accuracy for that individual only. The second approach trained a model on data from multiple users to generalize across individuals; however, demonstrated lower accuracy due to variations in gait patterns across users. The third approach added transfer learning to the second, improving estimation accuracy for new users through fine-

SUBMITTER: Hur B 

PROVIDER: S-EPMC11822032 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature