Inertial sensors-based lower-limb rehabilitation assessment: A comprehensive evaluation of gait, kinematic and statistical metrics

Tedesco, Salvatore; Urru, Andrea; Peckitt, James; O'Flynn, Brendan

Inertial sensors-based lower-limb rehabilitation assessment: A comprehensive evaluation of gait, kinematic and statistical metrics

Files

lifsci_v9_n12_2017_4_(10).pdf(5.51 MB)

Published version

Date

2017

Authors

Tedesco, Salvatore

Urru, Andrea

Peckitt, James

O'Flynn, Brendan

Publisher

IARIA

Abstract

Analysis of biomechanics is frequently used in both clinical and sporting practice in order to assess human motion and their performance of defined tasks. Whilst camera-based motion capture systems have long been regarded as the ‘Gold-standard’ for quantitative movement-based analysis, their application is not without limitations as regards potential sources of variability in measurements, high cost, and practicality of use for larger patient/subject groups. Another more practical approach, which presents itself as a viable solution to biomechanical motion capture and monitoring in sporting and patient groups, is through the use of small-size low-cost wearable Micro-ElectroMechanical Systems (MEMs)-based inertial sensors. The clinical aim of the present work is to evaluate rehabilitation progress following knee injuries, identifying a number of metrics measured via a wireless inertial sensing system. Several metrics in the time-domain have been considered to be reliable for measuring and quantifying patient progress across multiple exercises in different activities. This system was developed at the Tyndall National Institute and is able to provide a complete and accurate biomechanics assessment without the constraints of a motion capture laboratory. The results show that inertial sensors can be used for a quantitative assessment of knee joint mobility, providing valuable information to clinical experts as regards the trend of patient progress over the course of rehabilitation.

Keywords

Inertial Sensors , Wearable Microsystems , Signal Processing , Data Analytics , Lower-Limb Rehabilitation , Motor Performance

Citation

Tedesco, S., Urru, A., Peckitt, J. and O'Flynn, B. (2017) 'Inertial Sensors-based Lower-Limb Rehabilitation Assessment: A Comprehensive Evaluation of Gait, Kinematic and Statistical Metrics', International Journal on Advances in Life Sciences, 9 (1 & 2), pp. 33-49.