Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

ABSTRACT: Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants

SUBMITTER: Wipperman MF 

PROVIDER: S-EPMC11152572 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature