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Sensors 2017, 17(6), 1321; doi:10.3390/s17061321

Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features

1
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
2
Centre for Rehabilitation Research and Development, Ottawa Hospital Research Institute, Ottawa, ON K1H 8M2, Canada
3
Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Stephane Evoy and Baris Fidan
Received: 10 March 2017 / Revised: 31 May 2017 / Accepted: 2 June 2017 / Published: 7 June 2017
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Canada 2017)
View Full-Text   |   Download PDF [1652 KB, uploaded 9 June 2017]   |  

Abstract

Faller classification in elderly populations can facilitate preventative care before a fall occurs. A novel wearable-sensor based faller classification method for the elderly was developed using accelerometer-based features from straight walking and turns. Seventy-six older individuals (74.15 ± 7.0 years), categorized as prospective fallers and non-fallers, completed a six-minute walk test with accelerometers attached to their lower legs and pelvis. After segmenting straight and turn sections, cross validation tests were conducted on straight and turn walking features to assess classification performance. The best “classifier model—feature selector” combination used turn data, random forest classifier, and select-5-best feature selector (73.4% accuracy, 60.5% sensitivity, 82.0% specificity, and 0.44 Matthew’s Correlation Coefficient (MCC)). Using only the most frequently occurring features, a feature subset (minimum of anterior-posterior ratio of even/odd harmonics for right shank, standard deviation (SD) of anterior left shank acceleration SD, SD of mean anterior left shank acceleration, maximum of medial-lateral first quartile of Fourier transform (FQFFT) for lower back, maximum of anterior-posterior FQFFT for lower back) achieved better classification results, with 77.3% accuracy, 66.1% sensitivity, 84.7% specificity, and 0.52 MCC score. All classification performance metrics improved when turn data was used for faller classification, compared to straight walking data. Combining turn and straight walking features decreased performance metrics compared to turn features for similar classifier model—feature selector combinations. View Full-Text
Keywords: wearable sensors; machine learning; accelerometer; faller classification; faller prediction; feature selection; elderly; falls; prospective fallers wearable sensors; machine learning; accelerometer; faller classification; faller prediction; feature selection; elderly; falls; prospective fallers
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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

Drover, D.; Howcroft, J.; Kofman, J.; Lemaire, E.D. Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features. Sensors 2017, 17, 1321.

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