AI Processing of Wearable IMU Data for Exoskeleton Gait Analysis

Walking involves multiple gait cycles comprising stance and swing phases, crucial for stability and propulsion. Recovery of walking is a primary goal in neurological rehabilitation, with robotic exoskeletons emerging as promising tools. These devices assist individuals with lower extremity weakness,...

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Bibliographic Details
Main Authors: Vittoria Guerra, Bruna Maria, Sozzi, Stefania, Pizzocaro, Serena, De Nunzio, Alessandro Marco, Schmid, Micaela
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Data models
Deep Learning
Exoskeleton
Exoskeletons
Gait analysis
IMU signals
Legged locomotion
Protocols
Real-time systems
Robot sensing systems
Stability analysis
Technological innovation
Training
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Summary:Walking involves multiple gait cycles comprising stance and swing phases, crucial for stability and propulsion. Recovery of walking is a primary goal in neurological rehabilitation, with robotic exoskeletons emerging as promising tools. These devices assist individuals with lower extremity weakness, utilizing various signal acquisition technologies like inertial sensors (IMUs) to interpret human body movements. Accurately estimating gait phases, particularly for online exoskeleton control, presents a significant challenge. Deep Learning (DL) algorithms offer solutions by analysing IMU signals to predict gait events. A proposed DL model, based on a hybrid solution of one-dimensional Convolutional Neural Network and Bidirectional Long Short-Term Memory Neural Network, utilizes kinematic data from hip, knee and ankle to identify gait phases during exoskeleton-assisted walking, achieving 98.64% accuracy in stance and swing identification. Moreover, the study evaluates the time taken for the prediction of the gait phases, crucial for effective exoskeleton control, estimating the time spent on acquisition, elaboration, and classification procedures. The results show a processing time of 57 milliseconds for a 1-second segment of IMU signal. Evaluated on 26 healthy subjects, the model demonstrates potential for online gait phase identification. Overall, the proposed DL model shows promise for improving exoskeleton-assisted rehabilitation, offering tailored support, and enhancing the effectiveness of gait training interventions.
ISSN:2687-6817
DOI:10.1109/RTSI61910.2024.10761359