Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH.

Joana Figueiredo,Cristina P Santos,João Paulo Vilas-Boas,Simão P Carvalho,Luís M Gonçalves,Juan C Moreno

doi:10.3390/s20082185

Joana Figueiredo, Cristina P Santos + Show 4 more

Open Access

https://doi.org/10.3390/s20082185

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Abstract

This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems.

Highlights

Human gait analysis, namely the kinematic data, has manifold applications, as follows
Research contributions related to the ambulatory human kinematic gait analysis may involve inertial sensor-based systems with inertial measurement units (IMUs) [1]
The InertialLAB software routines executed with a mean computation time of 2.4 ± 0.47 ms, with 95% of the samples being computed within 3.1 ms for a central processing unit (CPU) running at 168 MHz

Summary

Introduction

Namely the kinematic data, has manifold applications, as follows. It has the potential to be applied as an automatic assessment tool for motor disorders to foster better treatment decisions. To recognize walking risk situations, and to support the clinical motor diagnosis [1,2]. Current challenges include the development of wearable motion labs with unobtrusive, low-cost, and effective wearable sensor systems for all-day and any-place gait monitoring without interfering with the user’s movement [1,3]. Research contributions related to the ambulatory human kinematic gait analysis may involve inertial sensor-based systems with inertial measurement units (IMUs) [1]

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