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

By Stephen H. Fairclough, Kiel Gilleade

This edited assortment will supply an summary of the sector of physiological computing, i.e. using physiological signs as enter for desktop regulate. it's going to disguise a breadth of present study, from brain-computer interfaces to telemedicine.

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Extra resources for Advances in Physiological Computing

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00568 Nijholt A, Plass-Oude Bos D, Reuderink B (2009) Turning shortcomings into challenges: brain– computer interfaces for games. Entertain Comput 1:85–94 Peck E, Lalooses F, Chauncey K (2011) Framing meaningful adaption in a social context. com. Accessed 12 Jan 2011 Picard RW (1997) Affective computing. The MIT Press, Cambridge Pope AT, Bogart EH, Bartolome DS (1995) Biocybernetic system evaluates indices of operator engagement in automated task. Biol Psychol 40:187–195 Pope AT, Palsson OS (2001) Helping video games ‘‘rewire our minds’’.

2008), where players need to throw baskets through a basketball hoop controlled by a robotic arm. The hoop is constantly moved in different directions according to the measured psychophysiological state. The movement rules themselves are gradually adapted as the robot tries different patterns and discovers the effect each pattern has on the current player’s enjoyment of the game. This application shows the promise of context-awareness in physiological computing. Since context-awareness is a major field of study in other fields, it should be possible to adapt many lessons on context-aware feedback for physiological computing.

Features are extracted from all the sensors, and machine learning algorithms are commonly used to determine which features are the most important (Novak et al. 2012). However, as the field moves toward downscaled and cheaper measurement solutions, it becomes necessary to know just which sensors are important in a given situation and which can be ignored. Researchers should thus, if possible, report which signals most contributed to psychophysiological inference. This can be done using the same machine learning methods used for psychophysiological inference.

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