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Physiological Pattern of Emotion in Elderly Based on Pulse Rate Variability Features: A preliminary study of e-Health monitoring system
Wibawa A.D.a, Hakim L.a, Widyanti A.b, Muslim K.b, Wijayanto T.c, Trapsilawati F.c, Arini H.M.c
a Institut Teknologi Sepuluh Nopember, Department of Computer Engineering, Surabaya, Indonesia
b Institut Teknologi Bandung, Department of Industrial Engineering, Bandung, Indonesia
c Universitas Gadjah Mada, Department of Industrial Engineering, Yogyakarta, Indonesia
[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]© 2019 IEEE.e-Health monitoring systems have been developed widely in many forms of applications. Monitoring physical condition of patients from a distance is the basic definition of e-health. However, there have been only few studies that explored the implementation of e-health in monitoring human emotions. It’s been widely known that emotion is one of the fundamental aspects that affecting human health conditions, especially the negative emotions such as sad and angry. In a health monitoring system, the influence of emotion to the human health can be monitored by using physiological signals such as heartbeat data, Oxygen in blood level, Galvanic skin level, pulse rate or blood pressure. Due to that reason, this study aimed to analyze physiological patterns of emotion in elderly based on pulse rate variability features by using pulse sensor as a form of e-health monitoring system in a lab scale. In this study, 25 elderly participants were involved and were stimulated by a video that conveys happiness, sadness, and anger content. The experiment was done in three phases: the baseline phase, video stimulation phase, and recovery phase. Pulse sensor was applied to capture the physiological signal of 25 participants during those 3 phases. Statistical feature extraction was used to calculated mean, maximum, minimum and standard deviation values from pulse rate signals. Furthermore, the paired t-test was used to analyze mean value between baseline and video stimulation phase. The results showed that sad emotion increased pulse rate significantly compared to happy and angry emotions. It was reported that 9 participants reached the pulse rate level more than 100 bpm when experiencing sadness. It implies that sad emotion has a higher potential for Sinus Tachycardia to happen to elderly people.[/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]E-health monitoring systems,Health monitoring system,Human emotion,negative emotion,physiological pattern of emotion,Pulse rate,Pulse rate variability,Statistical feature extractions[/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]e-Health monitoring,human emotion stimulation,negative emotion,physiological pattern of emotion,Pulse rate sensor[/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/ICA.2019.8916684[/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]