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

By Stephen H. Fairclough, Kiel Gilleade

This edited assortment will supply an outline of the sphere of physiological computing, i.e. using physiological signs as enter for laptop regulate. it is going to disguise a breadth of present learn, from brain-computer interfaces to telemedicine.

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This is logical but we believe it is also crucial to the adoption of this technology. The primary barriers to acceptance of physiological computing system are: (1) usage is perceived to be synonymous with an invasion of privacy (2) an emphasis on physiological data is seen to be potentially threatening as methods and measures are associated with medical procedures, and (3) biocybernetic adaptation represents a new mode of HCI where the computer has a greater degree of autonomy. Most nascent technologies are viewed with a mixture of apprehension and suspicion (often with good reason) and it is likely that the requirement for physiological monitoring will increase the trepidation of the user in this particular case.

Mental workload assessment in office tasks, fun assessment in physically demanding tasks), allowing application-specific sensors to be made simpler and cheaper with little loss in accuracy. Signal Processing The raw data collected from physiological sensors must generally be processed before it can be used for psychophysiological inference. g. g. band power from the EEG). The required methods are fairly well-known and for the most part not limited to physiological computing: ECG processing is based on decades of clinical ECG analysis while EEG processing uses essentially the same methods for both physiological computing and active brain-computer interfaces.

Furthermore, real-time capability needs to be ensured, as sensors such as those developed by Wilhelm et al. (2006) only allow data to be stored on a memory card and analyzed later. g. microcontrollers placed near the sensors. Validating Ambulatory Physiological Sensors Major progress has already been made in the validation of ambulatory equipment, especially dry and wireless EEG systems. g. Berka et al. 2004). g. Estepp et al. 2010). 20 D. g. Chi et al. 2012). The third option is by far the most general, as guaranteeing high-quality raw data guarantees usability of an ambulatory system in a variety of applications.

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