TY - GEN
T1 - Regression Models for Estimating Kinematic Gait Parameters with Instrumented Footwear
AU - Zhang, Huanghe
AU - Tay, Mey Olivares
AU - Suar, Zeynep
AU - Kurt, Mehmet
AU - Zanotto, Damiano
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/9
Y1 - 2018/10/9
N2 - Quantitative gait assessment typically involves optical motion capture systems and force plates, which result in high operating costs. Footwear-based motion tracking systems can provide a portable and affordable solution for real-time gait analysis in unconstrained environments. However, the relatively low accuracy of these systems still represents a barrier to their widespread use. In this paper, we show that linear and learning-based regression models can substantially improve the raw estimates of a set of kinematic gait parameters obtained with instrumented insoles (SportSole) from a group of N=9 healthy subjects who walked at different speeds. Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Regression (SVR) models are compared in terms of accuracy, precision, and robustness to change in gait speed, using gold-standard equipment to generate reference data. Results indicate that SVR is superior to LASSO. Indeed, the mean absolute errors (MAE) in stride length, velocity and foot-ground clearance were 1.28± 0.19%. 1.62±0.42% and 3.72±0.87% for LASSO, 1.06±0.08%. 1.13±0.08% and 3.00±0.87% for SVR, respectively. These findings provide further evidence that footwear-based systems may represent valid alternatives to laboratory equipment for assessing a basic set of gait parameters in unconstrained environments.
AB - Quantitative gait assessment typically involves optical motion capture systems and force plates, which result in high operating costs. Footwear-based motion tracking systems can provide a portable and affordable solution for real-time gait analysis in unconstrained environments. However, the relatively low accuracy of these systems still represents a barrier to their widespread use. In this paper, we show that linear and learning-based regression models can substantially improve the raw estimates of a set of kinematic gait parameters obtained with instrumented insoles (SportSole) from a group of N=9 healthy subjects who walked at different speeds. Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Regression (SVR) models are compared in terms of accuracy, precision, and robustness to change in gait speed, using gold-standard equipment to generate reference data. Results indicate that SVR is superior to LASSO. Indeed, the mean absolute errors (MAE) in stride length, velocity and foot-ground clearance were 1.28± 0.19%. 1.62±0.42% and 3.72±0.87% for LASSO, 1.06±0.08%. 1.13±0.08% and 3.00±0.87% for SVR, respectively. These findings provide further evidence that footwear-based systems may represent valid alternatives to laboratory equipment for assessing a basic set of gait parameters in unconstrained environments.
KW - Gait Assessment
KW - Learning-based Regression
KW - Sport Sole
KW - Wearable Sensor Networks
KW - Wearable Technology
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U2 - 10.1109/BIOROB.2018.8487972
DO - 10.1109/BIOROB.2018.8487972
M3 - Conference contribution
AN - SCOPUS:85056570258
T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
SP - 1169
EP - 1174
BT - BIOROB 2018 - 7th IEEE International Conference on Biomedical Robotics and Biomechatronics
T2 - 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BIOROB 2018
Y2 - 26 August 2018 through 29 August 2018
ER -