title: On the feasibility of learning to predict minimum toe clearance under different walking speeds.
abstract: 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.
authors.affiliation: Institute of Sports, Exercise and Active Living (ISEAL), School of Sports and Exercise Science, Victoria University, Australia. daniel.lai@vu.edu.au
authors.forename: Daniel T H
authors.initials: DT
authors.lastname: Lai
authors.forename: A
authors.initials: A
authors.lastname: Shilton
authors.forename: R
authors.initials: R
authors.lastname: Begg
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D005240:Feasibility Studies; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014034:Toes / Q000502:physiology*; D016138:Walking / Q000502:physiology*