Psychophysiology and adaptive automation

Byrne, Evan; Parasuraman, Raja

doi:10.1016/0301-0511(95)05161-9

Cited by 299 publications

(195 citation statements)

References 28 publications

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“…Early adaptive systems in the mid 1990's used feedback loops with physiology (Byrne et al, 1996). Research on affective computing has also developed adaptive software taking into account the user's emotions (Picard, 1995).…”

Section: Principles Of Self-adaptive Systemsmentioning

confidence: 99%

An overview of self-adaptive technologies within virtual reality training

Vaughan

Gabrys

Dubey

2016

Computer Science Review

115

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This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training.

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Section: Principles Of Self-adaptive Systemsmentioning

confidence: 99%

An overview of self-adaptive technologies within virtual reality training

Vaughan

Gabrys

Dubey

2016

Computer Science Review

115

View full text Add to dashboard Cite

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“…no overt response is required from the user [15]. In the case of physiological computing, the dynamic state of the user is inferred on the basis of spontaneous activity from the brain and the body [13,16].…”

Section: Monitoring the Usermentioning

confidence: 99%

A Closed-Loop Perspective on Symbiotic Human-Computer Interaction

Fairclough

2015

Symbiotic Interaction

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This paper is concerned with how people interact with an emergent form of technology that is capable of both monitoring and affecting the psychology and behaviour of the user. The current relationship between people and computer is characterised as asymmetrical and static. The closed-loop dynamic of physiological computing systems is used as an example of a symmetrical and symbiotic HCI, where the central nervous system of the user and an adaptive software controller are engaged in constant dialogue. This emergent technology offers several benefits such as: intelligent adaptation, a capacity to learn and an ability to personalise software to the individual. This paper argues that such benefits can only be obtained at the cost of a strategic reconfiguration of the relationship between people and technology -specifically users must cede a degree of control over their interaction with technology in order to create an interaction that is active, dynamic and capable of responding in a stochastic fashion. The capacity of the system to successfully translate human goals and values into adaptive responses that are appropriate and effective at the interface represents a particular challenge. It is concluded that technology can develop lifelike qualities (e.g. complexity, sentience, freedom) through sustained and symbiotic interaction with human beings. However, there are a number of risks associated with this strategy as interaction with this category of technology can subvert skills, self-knowledge and the autonomy of human user.

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“…In addition, other investigators have reported validation of EEG measures of workload that reflected differences in task-related cognitive resource allocation, task mastery and task overload [7][8][9][10][11]. The EEG variables employed in these models (e.g.…”

Section: Eeg Indices Of Workload In the Warship Commander Test Augmenmentioning

confidence: 99%