2016 International Conference on Frontiers of Information Technology (FIT) (2016)
Dec. 19, 2016 to Dec. 21, 2016
S.P.L. Aditya Pramanta , School of Electrical Engineering and Informatics, Bandung Institute of Technology, Ganesha Street 10, Bandung 40132, Indonesia
Ary Setijadi Prihatmanto , School of Electrical Engineering and Informatics, Bandung Institute of Technology, Ganesha Street 10, Bandung 40132, Indonesia
Man-Gon Park , Department of IT Converge and Internet Application, Pukyong National University, Rep. of Korea
Scientist since early 19th has discussed a lot about stress theories such as “Theory of Emotion”, “General Adaptation Syndrome Model”, and “Theory of Cognitive Appraisal”. Lately there are several studies about stress recognition by analyzing physical phenomenon. Beside the physically seen phenomenon, there is also multiple reliable markers to indicate stress used in medical environment for example HRV (Heart Rate Variability) which use heart beat data as input. There are three type of HRV analysis, which results in several parameters. In order to classify the subject condition based on HRV parameters, machine learning method such as SVM, decision tree, and random forest are being implemented. Right before doing the machine learning, several studies conduct feature selection phase by using several correlation method. Various researches about stress had been discussed, however as their research methods and environments differ, the result of the best parameter for stress identification also differ.
Heart Beat, Stress, HRV,
S.P.L. Aditya Pramanta, Ary Setijadi Prihatmanto, Man-Gon Park, "A study on the stress identification using observed heart beat data", 2016 International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 149-152, 2016, doi:10.1109/FIT.2016.7857555