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

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 indications as enter for machine keep an eye on. it's going to disguise a breadth of present study, from brain-computer interfaces to telemedicine.

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This challenge was mentioned as early as Picard et al. (2001). The same problem is well-known in gesture and speech recognition, where the majority of recordings consist of ‘garbage’ and actual events occur only briefly. Hidden Markov models, for instance, can try to deal with the problem using a ‘garbage’ model where one possible class is dedicated specifically to various types of meaningless measurements (Bernardin et al. 2005; Wilpon et al. 1990). For physiology, Kreibig (2010) suggested tackling the problem by first using unspecific physiological responses (which distinguish between neutral and nonneutral conditions) to detect periods of interest, then using specific physiological responses to determine the exact psychological state experienced—a type of ensemble classification.

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. It can, however, be problematic for physiological computing developers, as it can be very difficult to ensure that the ambulatory and reference system are measuring the same data. For instance, as electrodes from two sensors cannot be placed in the exactly same spots at the same time, it is impossible to measure the exactly same signals even using identical sensors (Chi et al.

Unfortunately, feedback in existing applications is generally limited to a handful of predefined rules independent of either user or situation (see Novak et al. 2012, for a review of feedback loops using autonomic nervous system responses). Exceptions do, however, already exist. One promising example is the work of Liu et al. (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.

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


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