Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems.

Biomedical Engineering Online
Mitchell YuwonoHung T Nguyen

Abstract

Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF) neural networks. Preliminary testing with 8 healthy individuals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL) data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary ...Continue Reading

References

Nov 16, 2007·Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society·Maarit KangasTimo Jämsä
Nov 26, 2010·Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society·Alan K BourkeJohn Nelson
Sep 23, 2011·Biomedical Engineering Online·Steven T MooreHamish G Macdougall
Jan 19, 2012·Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society·Mitchell YuwonoH T Nguyen

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Citations

Nov 26, 2013·Zeitschrift für Gerontologie und Geriatrie·L SchwickertUNKNOWN FARSEEING Consortium and the FARSEEING Meta Database Consensus Group
Jul 9, 2013·Biomedical Engineering Online·Raul IgualInmaculada Plaza
Apr 20, 2014·International Journal of Environmental Research and Public Health·Chih-Ning Huang, Chia-Tai Chan
May 27, 2015·Sensors·Luca PalmeriniAngelo Cappello
Aug 4, 2015·Medical Engineering & Physics·Raul IgualInmaculada Plaza
Jun 7, 2015·Biomedical Engineering Online·Prabitha UrwylerTobias Nef
Jan 11, 2014·Journal of Geriatric Physical Therapy·Shomir ChaudhuriGeorge Demiris
Feb 18, 2017·Sensors·Chia-Yeh HsiehChia-Tai Chan
Oct 9, 2017·Journal of Rehabilitation and Assistive Technologies Engineering·Emma Stack
Jul 10, 2019·Disability and Rehabilitation. Assistive Technology·Emily A KringleLauren Terhorst
Aug 23, 2021·The Spine Journal : Official Journal of the North American Spine Society·Nader FallahMarcel F Dvorak

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Methods Mentioned

BETA
feature extraction
feature-extraction

Software Mentioned

Matlab
Java2SE
RegPSO

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