Estimation of end point foot clearance points from inertial sensor data

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Santhiranayagam, Braveena K, Lai, Daniel ORCID: 0000-0003-3459-7709, Begg, Rezaul ORCID: 0000-0002-3195-8591 and Palaniswami, M (2011) Estimation of end point foot clearance points from inertial sensor data. In: 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, Piscataway, N.J., pp. 6503-6506.


Foot clearance parameters provide useful insight into tripping risks during walking. This paper proposes a technique for the estimate of key foot clearance parameters using inertial sensor (accelerometers and gyroscopes) data. Fifteen features were extracted from raw inertial sensor measurements, and a regression model was used to estimate two key foot clearance parameters: First maximum vertical clearance (mx1) after toe-off and the Minimum Toe Clearance (MTC) of the swing foot. Comparisons are made against measurements obtained using an optoelectronic motion capture system (Optotrak), at 4 different walking speeds. General Regression Neural Networks (GRNN) were used to estimate the desired parameters from the sensor features. Eight subjects foot clearance data were examined and a Leave-one-subject-out (LOSO) method was used to select the best model. The best average Root Mean Square Errors (RMSE) across all subjects obtained using all sensor features at the maximum speed for mx1 was 5.32 mm and for MTC was 4.04 mm. Further application of a hillclimbing feature selection technique resulted in 0.54-21.93% improvement in RMSE and required fewer input features. The results demonstrated that using raw inertial sensor data with regression models and feature selection could accurately estimate key foot clearance parameters.

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Conference held: Boston, Massachusetts USA, August 30 - September 3, 2011

Item type Book Section
DOI 10.1109/IEMBS.2011.6091604
Official URL
ISBN 9781424441211 (print), 9781424441228 (online)
Subjects Historical > FOR Classification > 1106 Human Movement and Sports Science
Historical > SEO Classification > 9202 Health and Support Services
Historical > SEO Classification > 9204 Public Health (excl. Specific Population Health)
Historical > Faculty/School/Research Centre/Department > Institute of Sport, Exercise and Active Living (ISEAL)
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID22874, ResPubID23601, accelerometers, average root mean square errors, end point foot clearance point estimation, feature extraction, foot clearance parameters, general regression neural networks, gyroscopes, hill- climbing feature selection technique, leave-one-subject-out method, maximum vertical clearance, minimum toe clearance, optoelectronic motion capture system, raw inertial sensor data, raw inertial sensor measurements, regression model, sensor features, swing foot, walking speeds, trajectory, biomedical equipment, feature extraction, gait analysis, gyroscopes, neural nets, patient monitoring, physiological models, regression analysis, feet, shoes
Citations in Scopus 8 - View on Scopus
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