Advances in Physiological Computing - download pdf or read online

By Stephen H. Fairclough, Kiel Gilleade

ISBN-10: 1447163915

ISBN-13: 9781447163916

ISBN-10: 1447163923

ISBN-13: 9781447163923

This edited assortment will supply an summary of the sphere of physiological computing, i.e. using physiological signs as enter for machine keep an eye on. it is going to hide a breadth of present examine, from brain-computer interfaces to telemedicine.

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Novak end-users, but do not yet have reliable solutions. They can be roughly divided into four major categories corresponding to the main components of a physiological computing system. In such a system, physiological data is first recorded by a sensor or range of sensors in next section. The raw data is then processed using algorithms that remove artefacts and extract potentially relevant features (Signal processing). A set of inference rules is used to convert the processed physiological data into an estimate of the user’s psychological state (Inferring psychological states).

Although most features are well-defined, some definitions seem somewhat arbitrary and stem from literature published before physiological computing ever got started. Consider, for example, the skin conductance signal. 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.

It is first added exactly halfway between the two R-peaks, then 20 % of the distance from the first peak to the second. Standardized measures of heart rate variability are computed according to recommendations of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996): standard deviation of NN intervals (SDNN), square root of the mean squared differences of successive NN intervals (RMSSD), percentage of differences of successive NN intervals greater than 50 ms (pNN50), total power in the high-frequency heart rate band (HF power), total power in the low-frequency heart rate band (LF power), and the ratio of LF and HF power.

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

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