By Daniel Tze Huei Lai, Marimuthu Palaniswami, Rezaul Begg

Healthcare sensor networks (HSNs) now supply the prospect to continually computer screen human task and physiological indications in a cellular surroundings. Such sensor networks are able to decrease the tension at the current healthcare crew by way of offering new self reliant tracking providers starting from uncomplicated user-reminder structures to extra complex tracking brokers for preventive, diagnostic, and rehabilitative Read more...

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There is also a huge gap between the expectations of the users and what is provided in terms of user-friendly devices and robust operation of these devices. The lack of clear standards in the field also makes it difficult to provide cost-effective and efficient maintenance. Quite often, maintenance can be so expensive that some of these systems hover around being not competitive commercially. There is going to be a growing demand for health sensor-based ­services which use mobile communications technologies and are wirelessly networked.

The presence of these two components in acceleration signals creates some difficulties in the use of these sensors. Usually, we are interested in only one of these components; for instance, the gravitational component is the only one needed when the aim of the analysis is to estimate the inclination of a body part relative to the vertical (inclinometry). Otherwise, in applications where we need to estimate the three-dimensional (3D) position of monitored anatomical points relative to an inertial frame of reference, the component of interest is the inertial acceleration (Bouten, Koekkoek et al.

Examples of this kind of machine learning algorithms that have been specifically applied to human activity recognition are the support vector machines (SVM; Lustrek and Kaluza 2009; Ravi et al. 2005), k-nearest neighbor classifier (k-NN; Ravi et al. 2005; K. Song and Wang 2005), ANNs (Mantyjarvi, Himberg, and Seppanen 2001; Randell and Muller 2000; Van Laerhoven and Cakmakci 2000), and threshold-based algorithms (Bussmann et al. 2001; Karantonis et al. 2006; S. H. Lee et al. 2003; Najafi et al.

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