TY - GEN
T1 - Gaussian Process Regression for COP Trajectory Estimation in Healthy and Pathological Gait Using Instrumented Insoles
AU - Duong, Ton T.H.
AU - Uher, David
AU - Young, Sally Dunaway
AU - Duong, Tina
AU - Sangco, Monica
AU - Cornett, Kayla
AU - Montes, Jacqueline
AU - Zanotto, Damiano
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Research in powered prostheses and orthoses has relied on COP measurements to inform a device's controller about the body's progression through the gait cycle, and to provide sensory substitution for prosthesis users, thereby helping them maintain balance during locomotion. Obtaining accurate COP measurements in out-of-the-lab contexts currently requires pressure sensitive insoles with dense arrays of sensing elements, which are expensive and bulky, limiting the accessibility and scalability of this technology. In this paper, we present a new method to reconstruct COP trajectories in over-ground walking tasks, using an affordable sensor array with eight sensing elements embedded in shoe insoles. The method leverages Gaussian Process Regression (GPR) to perform predictions from raw sensor data using Bayesian inference. A preliminary validation was carried out with a convenience sample of healthy individuals and patients with neuromuscular disorders. Combined mediolateral (ML) and anteroposterior (AP) errors where 2% and 3% for healthy individuals and patients, respectively. The analysis evidenced larger stride-to-stride variability in the ML COP excursion for the patient group, suggesting higher levels of motor noise associated with selective muscle weakness. These promising results indicate the potential of the proposed method to accurately estimate COP trajectories for future applications in wearable robotics and out-of-the-lab clinical gait assessments.
AB - Research in powered prostheses and orthoses has relied on COP measurements to inform a device's controller about the body's progression through the gait cycle, and to provide sensory substitution for prosthesis users, thereby helping them maintain balance during locomotion. Obtaining accurate COP measurements in out-of-the-lab contexts currently requires pressure sensitive insoles with dense arrays of sensing elements, which are expensive and bulky, limiting the accessibility and scalability of this technology. In this paper, we present a new method to reconstruct COP trajectories in over-ground walking tasks, using an affordable sensor array with eight sensing elements embedded in shoe insoles. The method leverages Gaussian Process Regression (GPR) to perform predictions from raw sensor data using Bayesian inference. A preliminary validation was carried out with a convenience sample of healthy individuals and patients with neuromuscular disorders. Combined mediolateral (ML) and anteroposterior (AP) errors where 2% and 3% for healthy individuals and patients, respectively. The analysis evidenced larger stride-to-stride variability in the ML COP excursion for the patient group, suggesting higher levels of motor noise associated with selective muscle weakness. These promising results indicate the potential of the proposed method to accurately estimate COP trajectories for future applications in wearable robotics and out-of-the-lab clinical gait assessments.
KW - Ambulatory Gait Analysis
KW - Instrumented Footwear
KW - Machine Learning Inference Models
KW - Wearable Technology
UR - http://www.scopus.com/inward/record.url?scp=85124343440&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124343440&partnerID=8YFLogxK
U2 - 10.1109/IROS51168.2021.9636562
DO - 10.1109/IROS51168.2021.9636562
M3 - Conference contribution
AN - SCOPUS:85124343440
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 9548
EP - 9553
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Y2 - 27 September 2021 through 1 October 2021
ER -