Support Vector Machines for Young and Older Gait Classification using Inertial Sensor Kinematics at Minimum Toe Clearance

Braveena Santhiranayagam,Rezaul Begg,Daniel Lai

doi:10.4108/eai.28-9-2015.2261579

Abstract

The present study investigates the inertial sensor kinematics obtained at a critical toe-control event, Minimum Toe Clearance (MTC), to classify different age groups. Fourteen young and fourteen older adults performed treadmill walking at their preferred walking speed, wearing a shoe-mount inertial sensor unit measuring tri-axial acceleration and tri-axial angular velocities. Three dimensional (3D) position-time data was obtained using high accurate motion capture system. MTC timing within a gait cycle (MTCTime), calculated using 3D motion capture data, was used to extract inertial sensor kinematics at MTC event. Mean and standard deviation of three inertial sensor acceleration features and three angular velocity features were compared between young and older individuals using t-tests. Young adults' mean anterior-posterior acceleration was greater than older adults (p=0.002). Further, standard deviations (SD) of all three accelerations and angular velocity about medio-lateral axis were greater in Older adults. The inertial sensor kinematics obtained at MTCTime were able to classify young and older adults gait with 91.2% accuracy using a Support Vector Machine (SVM) classifier. The findings of the present study suggest that by employing SVM techniques, a portable inertial sensor system could be used to identify gait degeneration due to ageing and has the potential for wider applications in gait identification for falls-risk minimization.

Highlights

As people age, their risk of falling increases and the consequences of a fall are more serious
When only the features that showed statistical differences were fed into the Support Vector Machine (SVM) classifiers, the best classification accuracy was obtained with linear kernel was 52.65% and with RBF 83.6%
When all six inertial sensor kinematics were used the accuracy improved to 92.1% with the RBF kernel

Summary

Introduction

Their risk of falling increases and the consequences of a fall are more serious. Falls during locomotion result from different destabilizing events such as tripping, slipping and loss of balance. Failure to adequately compensate surface height variability by adjusting clearance at MTC, increases tripping risk Older adults maintained their mean M T CHeight similar to young individuals’ [2, 8] but their M T CHeight dispersion, characterized by either the standard deviation (SD) or inter quartile range (IQR) are greater than the young. This greater stride-to-stride variability in M T CHeight observed in older adults, increases the chances of toe-ground contact while walking [1, 2, 8]

Methods

Results

Discussion

Conclusion