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Realtime BioSensing System Assessing Subconscious Responses of Engagement: An Evaluation Study

Realtime BioSensing System Assessing Subconscious Responses of Engagement: An Evaluation Study

Anthony Psaltis, Constantinos Mourlas
Copyright: © 2018 |Pages: 27
ISBN13: 9781522539407|ISBN10: 1522539409|EISBN13: 9781522539414
DOI: 10.4018/978-1-5225-3940-7.ch015
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MLA

Psaltis, Anthony, and Constantinos Mourlas. "Realtime BioSensing System Assessing Subconscious Responses of Engagement: An Evaluation Study." Digital Technologies and Instructional Design for Personalized Learning, edited by Robert Zheng, IGI Global, 2018, pp. 307-333. https://doi.org/10.4018/978-1-5225-3940-7.ch015

APA

Psaltis, A. & Mourlas, C. (2018). Realtime BioSensing System Assessing Subconscious Responses of Engagement: An Evaluation Study. In R. Zheng (Ed.), Digital Technologies and Instructional Design for Personalized Learning (pp. 307-333). IGI Global. https://doi.org/10.4018/978-1-5225-3940-7.ch015

Chicago

Psaltis, Anthony, and Constantinos Mourlas. "Realtime BioSensing System Assessing Subconscious Responses of Engagement: An Evaluation Study." In Digital Technologies and Instructional Design for Personalized Learning, edited by Robert Zheng, 307-333. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3940-7.ch015

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Abstract

Inferences of physiological responses are seen increasingly in dynamically adaptive environments, towards personalization, learning, and interactive instructional design. In search of conclusive interpretations, scientists consider bio-sensing and physiological metrics in addition to formal assessment methodologies. Devices developed for laboratory use impose limitations that yield them prohibitively unsuitable for wider use due to their strong dependence on electrodes and kinetic restrictions. Additionally, synchronisation, diverse format and frequencies of data produced by assorted equipment, contribute to precision concerns. The development cited in this chapter circumvents the above constraints by using a proprietary real-time system. An algorithm assessing coinciding excitation of two important physiological quantities is used to evaluate classifiers indicative to focused attention and engagement. Experiments and interpretations are delineated, exposing system accuracy and potential to assist in substantiating propositions towards improved learning performance and adaptive personalisation.

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