Next Article in Journal
User Association and Power Control for Energy Efficiency Maximization in M2M-Enabled Uplink Heterogeneous Networks with NOMA
Previous Article in Journal
Permafrost Deformation Monitoring Along the Qinghai-Tibet Plateau Engineering Corridor Using InSAR Observations with Multi-Sensor SAR Datasets from 1997–2018
Open AccessArticle

Embodied Emotion Recognition Based on Life-Logging

1
Department of Emotion Engineering, University of Sangmyung, Seoul 03016, Korea
2
Team of Technology Development, Emotion Science Center, Seoul 03044, Korea
3
Department of Intelligence Informatics Engineering, University of Sangmyung, Seoul 03016, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5308; https://doi.org/10.3390/s19235308
Received: 23 October 2019 / Revised: 22 November 2019 / Accepted: 30 November 2019 / Published: 2 December 2019
(This article belongs to the Section Intelligent Sensors)
Embodied emotion is associated with interaction among a person’s physiological responses, behavioral patterns, and environmental factors. However, most methods for determining embodied emotion has been considered on only fragmentary independent variables and not their inter-connectivity. This study suggests a method for determining the embodied emotion considering interactions among three factors: the physiological response, behavioral patterns, and an environmental factor based on life-logging. The physiological response was analyzed as heart rate variability (HRV) variables. The behavioral pattern was calculated from features of Global Positioning System (GPS) locations that indicate spatiotemporal property. The environmental factor was analyzed as the ambient noise, which is an external stimulus. These data were mapped with the emotion of that time. The emotion was evaluated on a seven-point scale for arousal level and valence level according to Russell’s model of emotion. These data were collected from 79 participants in daily life for two weeks. Their relationships among data were analyzed by the multiple regression analysis, after pre-processing the respective data. As a result, significant differences between the arousal level and valence level of emotion were observed based on their relations. The contributions of this study can be summarized as follows: (1) The emotion was recognized in real-life for a more practical application; (2) distinguishing the interactions that determine the levels of arousal and positive emotion by analyzing relationships of individuals’ life-log data. Through this, it was verified that emotion can be changed according to the interaction among the three factors, which was overlooked in previous emotion recognition. View Full-Text
Keywords: embodied emotion; causality; life-logging; photoplethysmogram (PPG); global positioning system (GPS); ambient noise embodied emotion; causality; life-logging; photoplethysmogram (PPG); global positioning system (GPS); ambient noise
Show Figures

Figure 1

MDPI and ACS Style

Cho, A.; Lee, H.; Jo, Y.; Whang, M. Embodied Emotion Recognition Based on Life-Logging. Sensors 2019, 19, 5308.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop