Validation of an instrumented shoe insole framework for analyzing spatiotemporal gait metrics in healthy and neurodegenerative populations

Background Many neurological conditions negatively affect a person's walking quality, which is a vital aspect of their quality of life. Gait quality, through the collection of spatiotemporal variables, can also help infer disease status; however, in-clinic access to these metrics is limited or cannot be assessed frequently enough to proactively monitor disease progression (i.e., improvement, maintenance, worsening). Methods To address these limitations, we developed a framework that analyzes spatiotemporal gait metrics using healthy and neurodegenerative walking data collected from instrumented shoe insoles. The Insole Framework (IF) identifies ambulatory activities using an artificial neural network, identifies gait events using logic, fuses the inertial measurement unit (IMU) data, standardizes the analysis to every ten seconds, and calculates spatiotemporal metrics categorized into core, pace, percentage, and asymmetry metrics. Activity classification algorithms had excellent accuracy and F1-score (≥ 93%). Results The spatiotemporal metrics obtained from the IF were validated against a gold standard motion capture system using ICCs, limits of agreement, and statistical testing. All core and pace metrics had good to excellent reliability and acceptable bias compared to the motion capture system, regardless of neurological function. Of the 19 spatiotemporal metrics assessed, system-independent statistical tests showed that similar population-level interpretations and post-hoc differences with similar levels of explained variance would be found regardless of the system used. Conclusion The IF was considered valid and can appropriately capture ambulatory activities and spatiotemporal gait metrics in healthy, multiple sclerosis, and Parkinson's disease populations.