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Sensors 2015, 15(11), 29393-29407; doi:10.3390/s151129393

Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms

1
Shenzhen Key Laboratory for Low-cost Healthcare, and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Road, Shenzhen 518055, China
2
Chengdu University of Technology, No.1, Third East Road, Erxianqiao, Chengdu 610059, China
3
Beijing Research Center of Urban System Engineering, Beijing 100035, China
4
College of mathematics and statistics, Shenzhen University, Shenzhen 518055, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Panicos Kyriacou
Received: 25 September 2015 / Revised: 4 November 2015 / Accepted: 17 November 2015 / Published: 20 November 2015
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
View Full-Text   |   Download PDF [806 KB, uploaded 20 November 2015]   |  

Abstract

The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population. View Full-Text
Keywords: feature selection; fall prediction; lower limb extremity; gait and balance; ground reaction force; sample entropy; KNN-based classifier feature selection; fall prediction; lower limb extremity; gait and balance; ground reaction force; sample entropy; KNN-based classifier
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Liang, S.; Ning, Y.; Li, H.; Wang, L.; Mei, Z.; Ma, Y.; Zhao, G. Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms. Sensors 2015, 15, 29393-29407.

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