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Optimal model selection for posture recognition in home-based healthcare

Optimal model selection for posture recognition in home-based healthcare,10.1007/s13042-010-0009-5,Shumei ZhangPaul,Paul McCullagh,Chris Nugent,Huiru

Optimal model selection for posture recognition in home-based healthcare   (Citations: 9)
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This paper investigates optimal model selection for posture recognition. Accuracy and computational time are related to the trained model in a supervised classification. An optimal model selection is important for a reliable activity monitoring system. Conventional guidance on model training uses large instances of randomly selected data in order to characterize the classes. A new approach to the training of a multiclass support vector machine (SVM) model suited to limited training sets such as used in posture recognition is provided. This approach picks a small training set from misclassified data to improve an initial model in an iterative and incremental fashion. In addition, a two step grid-search algorithm is used for the parameters setting. The best parameters were chosen according to the testing accuracy rather than conventional validating accuracy. This new approach for model selection was evaluated against conventional approaches in an activity classification study. Nine everyday postures were classified from a belt-worn smart phone’s accelerometer data. The classification derived from the small training set and the conventional randomly selected training set differed in two aspects: classification performance to new data (85.1% Pick-out small training set vs. 70.3% conventional large training set) and computational efficiency (improved 28%).
Published in 2011.
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    • ...267 tion is a very stringent condition that may limit applications of rough sets, various rough set theories based on the non­ equivalence relations and the fuzzy relations were therefore developed[4, 5, 8,10, 11, 12, 13, 15, 17]...

    Shao-Pu Zhanget al. Reduction of rough approximation space based on matroid

    • ...Applications in the area of pervasive healthcare range from small scale, highly specific body area sensor networks such as discussed in [1], which reflect specialized micro-environments; over well known macro-environments used for example, in activity recognition as discussed in [2] and [3]; to large scale and often fully unsupervised pervasive environments in which a multitude of sensors are deployed...

    Matthias Baumgartenet al. Cognitive sensor networks: Towards self-adapting ambient intelligence ...

    • ...The details relating to the activity classification algorithms have previously been described in Zhang et al. (2009, 2010, 2011a)...
    • ...Zhang et al. (2011a) illustrated that the phone’s position was selected as belt-worn, horizontal and on the left waist, based on the experimental results for comparison of two common positions between belt-worn and pocket...
    • ...Finally, the three motionless activities were further classified as nine postures (Zhang et al. 2011a), four sitting postures: sitting normal (Sit-N), sitting back (Sit-B), sitting leaning left and right (Sit-L and Sit-R); two standing postures: standing upright (Sta-U) and standing forward (Sta-F); three lying postures: lying right (Lyi-R), lying back (Lyi-B) and lying face down (Lyi-Fd)...

    Shumei ZhangPaulet al. An ontological framework for activity monitoring and reminder reasonin...

    • ...Fd [28]. The eventReminder is established by the relationships among temporal interval,...

    Shumei Zhanget al. An Ontology-Based Context-aware Approach for Behaviour Analysis

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