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

Abstract 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). 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%). 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 (i.e., one disagreement) and post-hoc differences (i.e., three disagreements) with similar levels of explained variance (absolute η 2 difference between systems across all tests was 0.046) would be found regardless of the system used. The IF was considered valid and can appropriately capture ambulatory activities and spatiotemporal gait metrics in healthy, multiple sclerosis, and Parkinson’s disease populations. Author Summary Gait assessments are used by clinicians to infer the severity and progression of neurological diseases. These assessments aim to quantify gross walking quality (i.e., patient perception, visual observations, speed, and distance) rather than the spatiotemporal metrics (e.g., double support time, stride length, cadence, etc.) that differentiate people from controls, conditions, and severity levels. Although spatiotemporal metrics can be powerful digital biomarkers to assess disease severity and monitor progression, traditional motion capture methods are limited due to high costs, the need for specialized expertise, time-consuming analysis/operations and infrequent patient collections. To overcome these limitations, we propose a framework that uses instrumented shoe insoles (inertial measurement unit + pressure) to identify activities and analyze gait. With our framework, gait assessments can be done several times a month in free-living conditions instead of infrequent clinical gait assessments, reducing healthcare barriers and promoting objective decision-making. This work describes our activity recognition, gait detection, and fusion methods and demonstrates our framework’s ability to produce results comparable to a gold-standard motion capture system in participants with multiple sclerosis, Parkinson’s disease, and healthy individuals. Our Insole Framework is deemed valid due to high reliability, similar between-group interpretations across systems, and the activity recognition algorithm’s performance.

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