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

Abstract

The present study investigates the inertial sensor kinematics obtained at a critical toe-control event, Minimum Toe Clearance (MTC), to classify dierent 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 triaxial 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.