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Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby

Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby

Duygun Erol Barkana, Engin Masazade
Copyright: © 2015 |Pages: 28
ISBN13: 9781466673878|ISBN10: 1466673877|EISBN13: 9781466673885
DOI: 10.4018/978-1-4666-7387-8.ch004
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MLA

Barkana, Duygun Erol, and Engin Masazade. "Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby." Handbook of Research on Advancements in Robotics and Mechatronics, edited by Maki K. Habib, IGI Global, 2015, pp. 53-80. https://doi.org/10.4018/978-1-4666-7387-8.ch004

APA

Barkana, D. E. & Masazade, E. (2015). Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby. In M. Habib (Ed.), Handbook of Research on Advancements in Robotics and Mechatronics (pp. 53-80). IGI Global. https://doi.org/10.4018/978-1-4666-7387-8.ch004

Chicago

Barkana, Duygun Erol, and Engin Masazade. "Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby." In Handbook of Research on Advancements in Robotics and Mechatronics, edited by Maki K. Habib, 53-80. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7387-8.ch004

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Abstract

Robot-assisted rehabilitation systems have shown to be helpful in neuromotor rehabilitation because it is possible to deliver interactive and repeatable sensorimotor exercise and monitor the actual performance continuously. Note that it is also essential to distinguish if subject finds the rehabilitation task difficult or easy, since the difficulty level of a task can yield different emotional state, such as excited, bored, over-stressed, etc., at each subject. It is important to adjust the difficulty level of the task to encourage the non-motivated subjects during the therapy. The physiological measurements, which can be obtained from the biofeedback sensors, can be used to estimate the subject's emotional state during the execution of the rehabilitation task. Machine learning methods can be used to classify the emotional state using the features of the biofeedback sensory data. This is explored in this chapter.

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