Publication: Stress classification system for intelligent wheelchair
1
0
Issued Date
2019
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-85074876588
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
2019 IEEE 1st Global Conference on Life Sciences and Technologies, LifeTech 2019. (2019), p.127-130
Suggested Citation
Sueaseenak D., Apichontivong P., Sripitak P., Sukplang S. Stress classification system for intelligent wheelchair. 2019 IEEE 1st Global Conference on Life Sciences and Technologies, LifeTech 2019. (2019), p.127-130. doi:10.1109/LifeTech.2019.8883963 Retrieved from: https://hdl.handle.net/20.500.14740/5417
Author(s)
Abstract
This research has developed a stress detection system for the disabled person who uses a wheelchair based on physiological signals. Physiological signals that we used to detect stress are galvanic skins response (GSR) and heart rate (HR), these parameters were collected from BIOPAC. The GSR was detected by 2 sensors on fingertips at the left forefinger and middle finger. Heart rate was detected by 3 sensors one on the left wrist and two on the ankles. The video game was selected to stimulate stress from 7 subjects during 19-21 years old. The proposed method consists of several parts which are (i) the feature extraction by MAV, (ii) Classification by Support Vector Machine (SVM). The experiment results of our proposed method show that the system has stress detection rate 97.1 percent and non-stress detection rate 100 percent. The result is very promising. © 2019 IEEE.