Download Advances in Physiological Computing by Stephen H. Fairclough, Kiel Gilleade PDF

By Stephen H. Fairclough, Kiel Gilleade

This edited assortment will offer an outline of the sphere of physiological computing, i.e. using physiological signs as enter for machine keep watch over. it's going to conceal a breadth of present examine, from brain-computer interfaces to telemedicine.

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Kalman filters, which are commonly used in general sensor fusion and have been shown to improve psychophysiological inference with autonomic nervous system responses by ‘learning’ about a subject over time (Koenig et al. 2011; Novak et al. 2011), are theoretically a simple dynamic Bayesian network, though not wellvalidated in physiological computing. More advanced dynamic Bayesian networks have been tested, some of them incorporating context-awareness (Ji et al. 2006; Lee and Chung 2012; Yang et al.

A common skin conductance feature is the number of skin conductance responses, which are defined as sufficiently large and rapid changes from the baseline value. 05 microsiemens. But why this specific value? As Boucsein (2011) explains, this threshold originally largely depended on the skin conductance signal’s expected range and amplification. 01 microsiemens have been suggested for modern sensors (Boucsein 2011). 05 microsiemens value seems to be used today mainly because it is popular. However, given the myriad of possibilities regarding sensor placement, use of gel, sensor amplification, and filtering, all of which affect the range of the signal, it makes little sense to always use the same threshold.

G. 30 % 24 D. Novak increase), which may be incorrectly interpreted as a change in psychological state and cause inappropriate reactions by the computer. Such small errors require more complex approaches to detect online. The first popular approach uses a secondary reference sensor that gauges the quality of the primary sensor’s output. With EEG, for instance, it is common to detect noise due to eye movements by measuring the EOG. Signal processing algorithms can then remove noise from the EEG by using the EOG as a reference (Croft and Barry 2000).

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