On the feasibility of learning to predict minimum toe clearance under different walking speeds

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Lai, Daniel ORCID: 0000-0003-3459-7709, Shilton, A and Begg, Rezaul ORCID: 0000-0002-3195-8591 (2010) On the feasibility of learning to predict minimum toe clearance under different walking speeds. In: EMBC 2010 : Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society : "Merging Medical Humanism and Technology" : August 31 - September 4, 2010, Buenos Aires Sheraton Hotel, Buenos Aires, Argentina. IEEE, Piscataway, N.J., pp. 4890-4893.


A major concern in human movement research is preventing tripping and falling which is known to cause severe injuries and high fatalities in elderly (>65 years) populations. Current falls prevention technology consists of active interventions e.g., strength and balance exercises, preimpact fall detectors, and passive interventions e.g., shower rails, hip protectors. However it has been found that these interventions with the exception of balance exercises do not effectively reduce falls risk. Recent work has shown that the minimum toe clearance (MTC) can be successfully monitored to detect gait patterns indicative of tripping and falling risk. In this paper, we investigate the feasibility of predicting MTC values of consecutive gait cycles under different walking speeds. The objective is two-fold, first to determine if end point foot trajectories can be accurately predicted and second, if walking speed is a significant parameter which influences the prediction process. The Generalized Regression Neural Networks and the Support Vector Regressor models were trained to predict MTC time series successively over an increasing prediction horizon i.e., 1 to 10 steps. Increased walking speeds resulted in increased MTC variability but no significant increase in mean MTC height. Root mean squared prediction errors ranged between 2.2-2.6mm or 10% of the mean values of the respective test data. The SVM slightly outperformed the GRNN predictions (0.5%-2.1% better accuracy). Best prediction accuracies decreased by 0.5mm for a doubling of walking speed i.e., from 2.5 km/h to 5.5 km/h. The results are encouraging because they demonstrate that the technique could be applied to forecasting low MTC values and provide new approaches to falls prevention technologies.

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Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/9996
DOI 10.1109/IEMBS.2010.5627269
Official URL http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arn...
ISBN 9781424441235 (print), 9781424441242
Subjects Historical > Faculty/School/Research Centre/Department > Institute of Sport, Exercise and Active Living (ISEAL)
Historical > FOR Classification > 1106 Human Movement and Sports Science
Historical > SEO Classification > 970111 Expanding Knowledge in the Medical and Health Sciences
Keywords ResPubID20253, gait, artificial neural networks, foot, feet, injuries, legged locomotion, predictive models, support vector machines, time series analysis, Generalized Regression Neural Networks, generalised, MTC time series, Support Vector Regressor model , consecutive gait cycles, elderly population, end point foot trajectory, falls prevention technology, gait patterns, human movement research, minimum toe clearance, root mean squared prediction error, walking speed
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