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A machine learning approach to k-step look-ahead prediction of gait variables from acceleration data

Version 2 2024-06-04, 06:00
Version 1 2019-07-10, 14:20
conference contribution
posted on 2009-12-01, 00:00 authored by D T H Lai, Alistair ShiltonAlistair Shilton, E Charry, R Begg, M Palaniswami
This 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.

History

Event

Engineering in Medicine and Biology Society. International Conference (31st : 2009 : Minneapolis, Minnesota)

Pagination

384 - 387

Publisher

IEEE

Location

Minneapolis, Minnesota

Place of publication

Piscataway, N.J.

Start date

2009-09-03

End date

2009-09-06

ISSN

1094-687X

eISSN

1558-4615

ISBN-13

9781424432967

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2009, IEEE

Title of proceedings

Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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