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Optimal model selection for posture recognition in home-based healthcare

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Zhang, Shumei, McCullagh, PJ, Nugent, CD, Zheng, H and Baumgarten, Matthias (2011) Optimal model selection for posture recognition in home-based healthcare. International Journal of Machine Learning and Cybernetics, 2 (1). pp. 1-14. [Journal article]

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URL: http://www.springerlink.com/content/121571/?Content+Status=Accepted

DOI: 10.1007/s13042-010-0009-5

Abstract

This paper investigates optimal model selectionfor posture recognition. Accuracy and computational timeare related to the trained model in a supervised classification.An optimal model selection is important for a reliableactivity monitoring system. Conventional guidance onmodel training uses large instances of randomly selecteddata in order to characterize the classes. A new approach tothe training of a multiclass support vector machine (SVM)model suited to limited training sets such as used in posturerecognition is provided. This approach picks a smalltraining set from misclassified data to improve an initialmodel in an iterative and incremental fashion. In addition,a two step grid-search algorithm is used for the parameterssetting. The best parameters were chosen according to thetesting accuracy rather than conventional validating accuracy.This new approach for model selection was evaluatedagainst conventional approaches in an activity classificationstudy. Nine everyday postures were classified from abelt-worn smart phone’s accelerometer data. The classificationderived from the small training set and the conventionalrandomly selected training set differed in twoaspects: classification performance to new data (85.1%Pick-out small training set vs. 70.3% conventional largetraining set) and computational efficiency (improved 28%).

Item Type:Journal article
Keywords:Optimal model; Posture recognition ;Accelerometer; Multi-class SVM
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Smart Environments
ID Code:16418
Deposited By:Dr Paul McCullagh
Deposited On:19 Nov 2010 13:55
Last Modified:09 Aug 2011 15:43

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