Feasibility of Gait Event Detection from Thigh Segment Kinematics in Children with Cerebral Palsy

Authors

• JE Dembsky
• NM Rubin
• TC Bulea

Abstract

Wearable robotic exoskeletons have been shown to improve walking in children with movement disorders such as cerebral palsy (CP). Many gait event detection algorithms, including the one developed in our laboratory at the NIH, use Force Sensitive Resistors (FSRs) underneath the exoskeleton’s footbed to determine when the foot contacts the ground. FSR-based gait event detection is suboptimal due to fragile hardware and delays between actual gait events and detection. Using thigh segment kinematics to detect gait events may offer more robust, precise detection than an FSR. The goals of this research were to 1) confirm viability of using highly accurate thigh segment kinematics to detect gait events during overground walking with motion capture and 2) determine feasibility of gait event detection using estimated thigh segment kinematics from an Inertial Measurement Unit (IMU) attached to the thigh of children with CP. Successful IMU-based gait event detection would provide impetus for its eventual use in real-time control of robotic exoskeletons. A peak detection algorithm was created to estimate initial ground contact from motion capture sagittal plane thigh angle. Terminal contact was not reliably predicted using sagittal plane data alone, so a weighted angular velocity using sagittal and frontal plane data was created to improve toe-off detection. After applying these algorithms to overground walking data from children with CP, we found that thigh segment kinematics-based gait event detection may have similar, if not improved, timing accuracy compared to the current FSR system used in the NIH pediatric robotic exoskeleton.

Scientific Focus Area: Clinical Research

This page was last updated on Tuesday, August 6, 2024