%0 Journal Article
%T Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems
%A Mitchell Yuwono
%A Bruce D Moulton
%A Steven W Su
%A Branko G Celler
%A Hung T Nguyen
%J BioMedical Engineering OnLine
%D 2012
%I BioMed Central
%R 10.1186/1475-925x-11-9
%X 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 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.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.Falls are recognised by the World Health Organization as a major cause of hospitalization of older people [1]. If no preventative measures are undertaken, it is estimated that costs associated with fall-related trauma will double over the next 20 years [1].Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment, and these devices are also potentially useful for assessing gait and tremor in older people with Parkinson's disease [2,3]. Research regarding accelerometer-based fall detection typically uses thresholding algorith
%U http://www.biomedical-engineering-online.com/content/11/1/9