Robotic emotion monitoring for mental health applications: preliminary outcomes of a survey

Marat Rostov; Md Zakir Hossain; Jessica Sharmin Rahman

doi:10.1007/978-3-030-85607-6_62

Robotic emotion monitoring for mental health applications: preliminary outcomes of a survey

Marat Rostov, Md Zakir Hossain^*, Jessica Sharmin Rahman

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

Maintaining mental health is crucial for emotional, psychological, and social well-being. Currently, however, societal mental health is at an all-time low. Robots have already proven useful in medicine, and robot assisted mental therapies through emotional monitoring have great potential. This paper reviews 60 recent papers to determine how accurately robots can classify human emotions using the latest sensor technologies. Among 18 different signals, it was determined that EDA sensors are best for this application. Our findings also show that CNN outperforms SVM, SVR, KNN and LDA for classifying EDA data with an average of 79% accuracy. This is further improved with the addition of RGB sensor data.

Original language	English
Title of host publication	Human-Computer Interaction – INTERACT 2021
Subtitle of host publication	18th IFIP TC 13 International Conference, Proceedings, Part V
Editors	Carmelo Ardito, Rosa Lanzilotti, Alessio Malizia, Helen Petrie, Antonio Piccinno, Giuseppe Desolda, Kori Inkpen
Place of Publication	Switzerland
Publisher	Springer, Springer Nature
Pages	481-485
Number of pages	5
ISBN (Electronic)	9783030856076
ISBN (Print)	9783030856069
DOIs	https://doi.org/10.1007/978-3-030-85607-6_62
Publication status	Published - 2021
Externally published	Yes
Event	18th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2021 - Hybrid, Bari, Italy Duration: 30 Aug 2021 → 3 Sept 2021

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	12936 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2021
Country/Territory	Italy
City	Hybrid, Bari
Period	30/08/21 → 3/09/21

Keywords

Emotion recognition
Machine learning
Physiology
Robots
Sensors

Access to Document

10.1007/978-3-030-85607-6_62

Cite this

Rostov, M., Hossain, M. Z., & Rahman, J. S. (2021). Robotic emotion monitoring for mental health applications: preliminary outcomes of a survey. In C. Ardito, R. Lanzilotti, A. Malizia, H. Petrie, A. Piccinno, G. Desolda, & K. Inkpen (Eds.), Human-Computer Interaction – INTERACT 2021: 18th IFIP TC 13 International Conference, Proceedings, Part V (pp. 481-485). (Lecture Notes in Computer Science; Vol. 12936 LNCS). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-85607-6_62

Rostov, Marat ; Hossain, Md Zakir ; Rahman, Jessica Sharmin. / Robotic emotion monitoring for mental health applications : preliminary outcomes of a survey. Human-Computer Interaction – INTERACT 2021: 18th IFIP TC 13 International Conference, Proceedings, Part V. editor / Carmelo Ardito ; Rosa Lanzilotti ; Alessio Malizia ; Helen Petrie ; Antonio Piccinno ; Giuseppe Desolda ; Kori Inkpen. Switzerland : Springer, Springer Nature, 2021. pp. 481-485 (Lecture Notes in Computer Science).

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title = "Robotic emotion monitoring for mental health applications: preliminary outcomes of a survey",

abstract = "Maintaining mental health is crucial for emotional, psychological, and social well-being. Currently, however, societal mental health is at an all-time low. Robots have already proven useful in medicine, and robot assisted mental therapies through emotional monitoring have great potential. This paper reviews 60 recent papers to determine how accurately robots can classify human emotions using the latest sensor technologies. Among 18 different signals, it was determined that EDA sensors are best for this application. Our findings also show that CNN outperforms SVM, SVR, KNN and LDA for classifying EDA data with an average of 79\% accuracy. This is further improved with the addition of RGB sensor data.",

keywords = "Emotion recognition, Machine learning, Physiology, Robots, Sensors",

author = "Marat Rostov and Hossain, \{Md Zakir\} and Rahman, \{Jessica Sharmin\}",

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doi = "10.1007/978-3-030-85607-6\_62",

language = "English",

isbn = "9783030856069",

series = "Lecture Notes in Computer Science",

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booktitle = "Human-Computer Interaction – INTERACT 2021",

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Rostov, M, Hossain, MZ & Rahman, JS 2021, Robotic emotion monitoring for mental health applications: preliminary outcomes of a survey. in C Ardito, R Lanzilotti, A Malizia, H Petrie, A Piccinno, G Desolda & K Inkpen (eds), Human-Computer Interaction – INTERACT 2021: 18th IFIP TC 13 International Conference, Proceedings, Part V. Lecture Notes in Computer Science, vol. 12936 LNCS, Springer, Springer Nature, Switzerland, pp. 481-485, 18th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2021, Hybrid, Bari, Italy, 30/08/21. https://doi.org/10.1007/978-3-030-85607-6_62

Robotic emotion monitoring for mental health applications: preliminary outcomes of a survey. / Rostov, Marat; Hossain, Md Zakir; Rahman, Jessica Sharmin.
Human-Computer Interaction – INTERACT 2021: 18th IFIP TC 13 International Conference, Proceedings, Part V. ed. / Carmelo Ardito; Rosa Lanzilotti; Alessio Malizia; Helen Petrie; Antonio Piccinno; Giuseppe Desolda; Kori Inkpen. Switzerland: Springer, Springer Nature, 2021. p. 481-485 (Lecture Notes in Computer Science; Vol. 12936 LNCS).

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

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T1 - Robotic emotion monitoring for mental health applications

T2 - 18th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2021

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AU - Hossain, Md Zakir

AU - Rahman, Jessica Sharmin

PY - 2021

Y1 - 2021

N2 - Maintaining mental health is crucial for emotional, psychological, and social well-being. Currently, however, societal mental health is at an all-time low. Robots have already proven useful in medicine, and robot assisted mental therapies through emotional monitoring have great potential. This paper reviews 60 recent papers to determine how accurately robots can classify human emotions using the latest sensor technologies. Among 18 different signals, it was determined that EDA sensors are best for this application. Our findings also show that CNN outperforms SVM, SVR, KNN and LDA for classifying EDA data with an average of 79% accuracy. This is further improved with the addition of RGB sensor data.

AB - Maintaining mental health is crucial for emotional, psychological, and social well-being. Currently, however, societal mental health is at an all-time low. Robots have already proven useful in medicine, and robot assisted mental therapies through emotional monitoring have great potential. This paper reviews 60 recent papers to determine how accurately robots can classify human emotions using the latest sensor technologies. Among 18 different signals, it was determined that EDA sensors are best for this application. Our findings also show that CNN outperforms SVM, SVR, KNN and LDA for classifying EDA data with an average of 79% accuracy. This is further improved with the addition of RGB sensor data.

KW - Emotion recognition

KW - Machine learning

KW - Physiology

KW - Robots

KW - Sensors

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AN - SCOPUS:85115253626

SN - 9783030856069

T3 - Lecture Notes in Computer Science

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EP - 485

BT - Human-Computer Interaction – INTERACT 2021

A2 - Ardito, Carmelo

A2 - Lanzilotti, Rosa

A2 - Malizia, Alessio

A2 - Petrie, Helen

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A2 - Desolda, Giuseppe

A2 - Inkpen, Kori

PB - Springer, Springer Nature

CY - Switzerland

Y2 - 30 August 2021 through 3 September 2021

ER -

Rostov M, Hossain MZ, Rahman JS. Robotic emotion monitoring for mental health applications: preliminary outcomes of a survey. In Ardito C, Lanzilotti R, Malizia A, Petrie H, Piccinno A, Desolda G, Inkpen K, editors, Human-Computer Interaction – INTERACT 2021: 18th IFIP TC 13 International Conference, Proceedings, Part V. Switzerland: Springer, Springer Nature. 2021. p. 481-485. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-85607-6_62

Robotic emotion monitoring for mental health applications: preliminary outcomes of a survey

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