Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby

Yunus Palaska; Huseyin Erdogan; Hazim Kemal Ekenel; Engin Masazade; Duygun Erol Barkana

doi:10.1109/CCECE.2017.7946691

Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby

Yunus Palaska, Huseyin Erdogan, Hazim Kemal Ekenel, Engin Masazade, Duygun Erol Barkana

Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Investigation into robot-assisted rehabilitation systems, and robot-assisted systems that are capable of detecting patient's emotions and then modifying the rehabilitation task to better suit the patients' abilities by taking account their emotions have gained momentum in recent years. In this paper, our aim is to distinguish whether the subject is under-challenged or over-challenged using psychophysiological signal data collected from biofeedback sensors while executing the tasks with RehabRoby. Initially, features are extracted from the physiological signals (Blood Volume Pulse (BVP), Skin Conductance (SC), and Skin Temperature (ST)). The extracted features are examined in terms of their contribution to the classification of the overstressed/over-challenged, boredom/under-challenged using variance analysis (ANOVA). The most significant features are selected, and various classification methods are used to classify overstressed/over-challenged, boredom/under-challenged.

Original language	English (US)
Title of host publication	2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering, CCECE 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509055388
DOIs	https://doi.org/10.1109/CCECE.2017.7946691
State	Published - Jun 12 2017
Event	30th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2017 - Windsor, Canada Duration: Apr 30 2017 → May 3 2017

Publication series

Name	Canadian Conference on Electrical and Computer Engineering
ISSN (Print)	0840-7789

Conference

Conference	30th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2017
Country/Territory	Canada
City	Windsor
Period	4/30/17 → 5/3/17

Keywords

Biofeedback sensor
Classification
Emotion recognition
Robot-assisted rehabilitation

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/CCECE.2017.7946691

Cite this

Palaska, Y., Erdogan, H., Ekenel, H. K., Masazade, E., & Barkana, D. E. (2017). Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby. In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering, CCECE 2017 Article 7946691 (Canadian Conference on Electrical and Computer Engineering). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCECE.2017.7946691

Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby. / Palaska, Yunus; Erdogan, Huseyin; Ekenel, Hazim Kemal et al.
2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering, CCECE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7946691 (Canadian Conference on Electrical and Computer Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Palaska, Y, Erdogan, H, Ekenel, HK, Masazade, E & Barkana, DE 2017, Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby. in 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering, CCECE 2017., 7946691, Canadian Conference on Electrical and Computer Engineering, Institute of Electrical and Electronics Engineers Inc., 30th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2017, Windsor, Canada, 4/30/17. https://doi.org/10.1109/CCECE.2017.7946691

Palaska Y, Erdogan H, Ekenel HK, Masazade E, Barkana DE. Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby. In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering, CCECE 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 7946691. (Canadian Conference on Electrical and Computer Engineering). doi: 10.1109/CCECE.2017.7946691

Palaska, Yunus ; Erdogan, Huseyin ; Ekenel, Hazim Kemal et al. / Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby. 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering, CCECE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (Canadian Conference on Electrical and Computer Engineering).

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abstract = "Investigation into robot-assisted rehabilitation systems, and robot-assisted systems that are capable of detecting patient's emotions and then modifying the rehabilitation task to better suit the patients' abilities by taking account their emotions have gained momentum in recent years. In this paper, our aim is to distinguish whether the subject is under-challenged or over-challenged using psychophysiological signal data collected from biofeedback sensors while executing the tasks with RehabRoby. Initially, features are extracted from the physiological signals (Blood Volume Pulse (BVP), Skin Conductance (SC), and Skin Temperature (ST)). The extracted features are examined in terms of their contribution to the classification of the overstressed/over-challenged, boredom/under-challenged using variance analysis (ANOVA). The most significant features are selected, and various classification methods are used to classify overstressed/over-challenged, boredom/under-challenged.",

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AB - Investigation into robot-assisted rehabilitation systems, and robot-assisted systems that are capable of detecting patient's emotions and then modifying the rehabilitation task to better suit the patients' abilities by taking account their emotions have gained momentum in recent years. In this paper, our aim is to distinguish whether the subject is under-challenged or over-challenged using psychophysiological signal data collected from biofeedback sensors while executing the tasks with RehabRoby. Initially, features are extracted from the physiological signals (Blood Volume Pulse (BVP), Skin Conductance (SC), and Skin Temperature (ST)). The extracted features are examined in terms of their contribution to the classification of the overstressed/over-challenged, boredom/under-challenged using variance analysis (ANOVA). The most significant features are selected, and various classification methods are used to classify overstressed/over-challenged, boredom/under-challenged.

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Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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