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

By Stephen H. Fairclough, Kiel Gilleade

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

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With active brain-computer interfaces, which are closely related to physiological computing, users were found to expect and accept approximately 75 % accuracy in recognition of four possible desired movements (Ware et al. 2010), but the finding is very application-specific. In critical situations such as driver fatigue monitoring, physiological computing systems should be very accurate, as any mistake would either cause harm (potential problem not detected) or annoy the user (alarm or automated assistance engaged inappropriately).

Even if the subject is perfectly still, artefacts can occur due to movement of the cables between the electrodes and the analog-digital converter. This can partially be compensated for by signal processing (Signal Processing), but not always. The motion artefacts in the skin conductance signal, for instance, can be very difficult to distinguish from actual skin conductance changes. Motion artefacts in the electrocardiogram (ECG) can be noticed easily, but are still difficult to remove since their frequency range partially overlaps the frequency range of the ECG.

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.

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