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

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dc.contributor.author Yuwono, Mitchell en_US
dc.contributor.author Moulton, Bruce en_US
dc.contributor.author Su, Steven en_US
dc.contributor.author Celler, Branko en_US
dc.contributor.author Nguyen, Hung en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-10-12T03:33:58Z
dc.date.available 2012-10-12T03:33:58Z
dc.date.issued 2012 en_US
dc.identifier 2011002027 en_US
dc.identifier.citation Yuwono Mitchell et al. 2012, 'Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems', Biomed Central, vol. 11, pp. art9 en_US
dc.identifier.issn 1475-925X en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/18373
dc.description.abstract Background: 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. Method: 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. Results: 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 tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. Conclusion: The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall detection systems. en_US
dc.language en_US
dc.publisher Biomed Central en_US
dc.relation.isbasedon http://dx.doi.org/10.1186/1475-925X-11-9
dc.title Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems en_US
dc.parent Biomedical Engineering Online en_US
dc.journal.volume 11 en_US
dc.journal.number en_US
dc.publocation Bethesda MD USA en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage en_US
dc.identifier.endpage 11 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 090300 en_US
dc.personcode 11006881 en_US
dc.personcode 010755 en_US
dc.personcode 997723 en_US
dc.personcode 100180 en_US
dc.personcode 840115 en_US
dc.percentage 100 en_US
dc.classification.name Biomedical Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords fall detection, aged care, machien learning, Gaussian Distribution en_US
dc.staffid 840115 en_US

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