[vc_empty_space][vc_empty_space]
A study on the stress identification using observed heart beat data
Pramanta S.P.L.A.a,b, Prihatmanto A.S.a, Park M.-G.b
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia
b Department of IT Converge and Internet Application, Pukyong National University, South Korea
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2016 IEEE.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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Cognitive appraisal,Heart beats,Heart rate variability,Machine learning methods,Physical phenomena,Random forests,Stress recognition,Stress theory[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Heart Beat,HRV,Stress[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/FIT.2016.7857555[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]