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Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders.

Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders.
Author Information (click to view)

Tunca C, Pehlivan N, Ak N, Arnrich B, Salur G, Ersoy C,


Tunca C, Pehlivan N, Ak N, Arnrich B, Salur G, Ersoy C, (click to view)

Tunca C, Pehlivan N, Ak N, Arnrich B, Salur G, Ersoy C,

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Sensors (Basel, Switzerland) 2017 04 1117(4) pii E825
Abstract

The gold standards for gait analysis are instrumented walkways and marker-based motion capture systems, which require costly infrastructure and are only available in hospitals and specialized gait clinics. Even though the completeness and the accuracy of these systems are unquestionable, a mobile and pervasive gait analysis alternative suitable for non-hospital settings is a clinical necessity. Using inertial sensors for gait analysis has been well explored in the literature with promising results. However, the majority of the existing work does not consider realistic conditions where data collection and sensor placement imperfections are imminent. Moreover, some of the underlying assumptions of the existing work are not compatible with pathological gait, decreasing the accuracy. To overcome these challenges, we propose a foot-mounted inertial sensor-based gait analysis system that extends the well-established zero-velocity update and Kalman filtering methodology. Our system copes with various cases of data collection difficulties and relaxes some of the assumptions invalid for pathological gait (e.g., the assumption of observing a heel strike during a gait cycle). The system is able to extract a rich set of standard gait metrics, including stride length, cadence, cycle time, stance time, swing time, stance ratio, speed, maximum/minimum clearance and turning rate. We validated the spatio-temporal accuracy of the proposed system by comparing the stride length and swing time output with an IR depth-camera-based reference system on a dataset comprised of 22 subjects. Furthermore, to highlight the clinical applicability of the system, we present a clinical discussion of the extracted metrics on a disjoint dataset of 17 subjects with various neurological conditions.

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