Developing and exploring a methodology for multi-modal indoor and outdoor gait assessment

Area of Science:

• Biomechanics
• Neuroscience
• Rehabilitation Engineering

Background:

• Gait assessment is crucial for understanding mobility impairments and neurological deficits.
• Wearable sensors offer a practical and cost-effective solution for gait analysis in various environments.
• Current gait models often rely on uni-modal data, limiting a comprehensive understanding of impaired gait.

Purpose of the Study:

• To develop and evaluate a multi-modal gait assessment approach.
• To synchronize inertial measurement unit (IMU) data with electromyography (EMG) signals.
• To investigate differences in gait characteristics between indoor and outdoor walking using this integrated approach.

Main Methods:

• Utilized synchronized IMU and EMG data for gait analysis.
• Identified initial contact (IC) and final contact (FC) moments from IMU data to derive temporal gait parameters.
• Segmented EMG data based on IC/FC timings to determine muscle activity onset/offset and amplitude within the gait cycle.

Main Results:

• Observed distinct differences in temporal gait characteristics and muscle activation patterns between indoor and outdoor walking in stroke survivors.
• Preliminary analysis revealed variations in muscle onset/offset timings and amplitudes.
• Demonstrated the feasibility of synchronizing IMU and EMG data for detailed gait analysis.

Conclusions:

• A multi-modal approach integrating IMU and EMG data offers a more comprehensive assessment of gait impairments.
• This method can augment existing uni-modal gait models by incorporating quantitative muscle activity data.
• Findings suggest potential for improved understanding and management of mobility deficits in neurological conditions.