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A machine learning approach to k-step look-ahead prediction of gait variables from acceleration data
conference contribution
posted on 2009-12-01, 00:00 authored by D T H Lai, Alistair ShiltonAlistair Shilton, E Charry, R Begg, M PalaniswamiThis paper investigates the use of machine learning to predict a sensitive gait parameter based on acceleration information from previous gait cycles. We investigate a k-step look-ahead prediction which attempts to predict gait variable values based on acceleration information in the current gait cycle. The variable is the minimum toe clearance which has been demonstrated to be a sensitive falls risk predictor. Toe clearance data was collected under normal walking conditions and 9 features consisting of peak acceleration and their normalized occurrences times were extracted. A standard least squares estimator, a generalized regression neural network (GRNN) and a support vector regressor (SVR) were trained using 60% of the data to estimate the minimum toe clearance and the remaining 40% was used to validate the model. It was found that when the training data contained data from all subjects (inter-subject) the best GRNN model provided a root mean square error (RMSE) of 2.8 mm, the best SVR had RMSE of 2.7 mm while the standard least squares linear regression method obtained 3.3 mm. When the training and test data consisted of different subject examples (inter-subject) data, the linear SVR demonstrated superior generalization capability (RMSE=3.3 mm) compared to other competing models. Validation accuracies up to 5-step look-ahead predictions revealed robust performances for both GRNN and SVR models with no clear degradation in prediction accuracy.
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Event
Engineering in Medicine and Biology Society. International Conference (31st : 2009 : Minneapolis, Minnesota)Pagination
384 - 387Publisher
IEEELocation
Minneapolis, MinnesotaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2009-09-03End date
2009-09-06ISSN
1094-687XeISSN
1558-4615ISBN-13
9781424432967Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2009, IEEETitle of proceedings
Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyUsage metrics
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