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Regression Models for Estimating Gait Parameters Using Inertial Sensors

Santhiranayagam, Braveena K, Lai, Daniel ORCID: 0000-0003-3459-7709, Shilton, A, Begg, Rezaul ORCID: 0000-0002-3195-8591 and Palaniswami, M (2011) Regression Models for Estimating Gait Parameters Using Inertial Sensors. In: 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2011). IEEE, United States, pp. 46-51.

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Abstract

Advanced mathematical models are now widely used in medical applications for diagnosis, prognosis, and prevention of diseases. This work looks at the application of advanced regression models for estimating key foot parameters in falls prevention research. Falls is a serious issue for the rapidly increasing elderly demographic. We propose to investigate the notion of falls prediction through the use of portable, light weight, easy to use and inexpensive sensors along with advanced computational intelligence estimation models. This study compares two mathematical models namely the Generalized Regression Neural Networks (GRNN), and the Support Vector Machine (SVM) to estimate the key gait parameters. The study deployed Inertial Measurement Units (IMU) consisting of accelerometers and gyroscopes sensors to measure the foot kinematics and an optoelectronic motion capture system to validate the results. Our results demonstrated that both mathematical models estimate the key end point foot trajectory parameters (1) mx1 - first maximum after toe-off (root mean square error (rmse) range of 2.0 mm to 12.5 mm) (2) normalized time to mx1 (rmse range of 0.4% to 3.7%) and (3) Minimum Toe Clearance (rmse range of 2.0 mm to 10.2 mm) and (4) normalized time to MTC (rmse range of 0.7% to 5.4%) using IMU features. The SVM regressor showed better estimation rmse 56 times out of the 70 comparison estimations. In all cases the best model respectively from the GRNN and SVM family of models was compared.

Item Type: Book Section
ISBN: 9781457706752
Additional Information:

Proceedings of a meeting held 6-9 December 2011, Adelaide, Australia

Uncontrolled Keywords: ResPubID22965, ResPubID23600, accelerometers, computerised instrumentation, gait analysis, gyroscopes, inertial systems, kinematics, neural nets, regression analysis, support vector machines
Subjects: FOR Classification > 0903 Biomedical Engineering
Faculty/School/Research Centre/Department > School of Sport and Exercise Science
SEO Classification > 9202 Health and Support Services
Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: VUIR
Date Deposited: 18 Jan 2014 11:41
Last Modified: 01 Aug 2019 07:05
URI: http://vuir.vu.edu.au/id/eprint/9482
DOI: https://doi.org/10.1109/ISSNIP.2011.6146605
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Citations in Scopus: 8 - View on Scopus

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