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.
Highlights
Gait abnormalities are crucial indicators of the state and the progression of disorders that have motor symptoms
We propose a robust initial contact/foot-off (IC/FO) detection method that is able to operate under imperfect foot-relative sensor placement and orientation conditions
The stance phase begins with initial contact (IC), which marks the beginning of the load transfer to the ground-contacting foot
Summary
Gait abnormalities are crucial indicators of the state and the progression of disorders that have motor symptoms. Unlike temporary/treatable or static orthopedic diseases, some neurological diseases are associated with disabling, progressive gait disorders and increased risk of falls for the remainder of the patient’s life. These patients show fluctuations and variable response to treatment options. We discuss the challenges of working with wearable IMUs with the aim of accurate gait analysis, both in terms of the error characteristics of the underlying hardware and the difficulties in deployment and data collection These challenges serve as the rationale behind the aims of this study, explicitly, to come up with a robust gait analysis solution that is minimally affected by the typical errors associated with IMUs, either arising from the human factor or the hardware. The stance phase constitutes approximately 60% of the whole gait cycle, and the swing phase constitutes the remaining 40% [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
