%0 Conference Proceedings
%T Robotic Emotion Monitoring for Mental Health Applications: Preliminary Outcomes of a Survey
%+ Australian National University (ANU)
%+ Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO)
%A Rostov, Marat
%A Hossain, Md, Zakir
%A Rahman, Jessica, Sharmin
%Z Part 7: Posters
%< avec comité de lecture
%@ 978-3-030-85606-9
%( Lecture Notes in Computer Science
%B 18th IFIP Conference on Human-Computer Interaction (INTERACT)
%C Bari, Italy
%Y Carmelo Ardito
%Y Rosa Lanzilotti
%Y Alessio Malizia
%Y Helen Petrie
%Y Antonio Piccinno
%Y Giuseppe Desolda
%Y Kori Inkpen
%I Springer International Publishing
%3 Human-Computer-Interaction – INTERACT 2021
%V LNCS-12936
%N Part V
%P 481-485
%8 2021-08-30
%D 2021
%R 10.1007/978-3-030-85607-6_62
%K Emotion recognition
%K Machine learning
%K Physiology
%K Robots
%K Sensors
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 13
%2 https://inria.hal.science/hal-04291213/document
%2 https://inria.hal.science/hal-04291213/file/520519_1_En_62_Chapter.pdf
%L hal-04291213
%U https://inria.hal.science/hal-04291213
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC13
%~ IFIP-INTERACT
%~ IFIP-LNCS-12936