Is heart rate variability (HRV) an adequate tool for evaluating human emotions? – A focus on the use of the International Affective Picture System (IAPS)

Choi, Kwang Ho; Kim, Junbeom; Kwon, O Sang; Kim, Min Ji; Ryu, Yeon Hee; Park, Ji Eun

doi:10.1016/j.psychres.2017.02.025

Cited by 134 publications

(88 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is already documented that RRI is associated with the condition of one's automatic nervous system, representing emotional status. Choi et al [52] provided evidence that heart rate variability could be used to adequately evaluate human emotion. Valenza, Antonio, and Enzo [53] and Lane et al [54] also confirmed that heart rate variability could be an objective tool to assess emotional responses.…”

Section: Sample Selection Variable Definitions and Measurement Issuesmentioning

confidence: 99%

Emotional Status and Productivity: Evidence from the Special Economic Zone in Laos

et al. 2020

View full text Add to dashboard Cite

Employee productivity is a well-studied area, which has been explained in various dimensions. However, there is insufficient research on how workers' on-job emotional status relates to productivity. This study examined the relationship between workers' emotional states and productivity by assessing on-job emotionality recorded using a specially designed wearable biometric device. The experiment was conducted at KP Beau Lao Co. Ltd., a Japanese plastic toys and cosmetic products company in Savannakhet province in Southwestern Laos. Participants were 15 plastic toy painters. Mental status, daily output, and other issues were recorded for three consecutive working days. Using random effects panel regression models, we examined how productivity, operationalized as the log of daily output, was related to workers' emotional states, including the amount of time workers reported being happy, angry, relaxed, and sad. We controlled for conversation time, heart rate, and other demographic features. The results revealed that happiness, and no other emotional state, was significantly and positively related to productivity. Such findings suggested that workers' emotional states must be addressed as part of an organization's operational strategy to ensure higher productivity.

show abstract

Section: Sample Selection Variable Definitions and Measurement Issuesmentioning

confidence: 99%

Emotional Status and Productivity: Evidence from the Special Economic Zone in Laos

et al. 2020

View full text Add to dashboard Cite

show abstract

“…HRV (heart rate variability) is a method that has been widely used in the academic literature. It reflects the state of ANS (autonomic nervous system) [28]. It is therefore a good objective tool for assessing emotional responses [29].…”

Section: Heart Ratementioning

confidence: 99%

Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data

Shen

Wang

2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

In order to improve the driver's physiological and psychological state, the driver's mental load which is caused by sight distance, lighting, and other factors in the tunnel environment should be quantified via modeling the spatiotemporal data. e experimental schemes have been scientifically designed based on methods of traffic engineering and human factor engineering, which aims to test the driver's spatiotemporal data of eye movement and ECG (electrocardiogram) index in the tunnel environment. Firstly, the changes in the driver's spatiotemporal data are analyzed to judge the changing trend of the driver's workload in the tunnel environment. e results show that the cubic spline interpolation function model can fit the dynamic changes of average pupil diameter and heart rate (HR) growth rate well, and the goodness of fit for the model group is above 0.95. So, tunnel environment makes the driver's typical physiological indicators fluctuate in the coordinates of time and space, which can be modeled and quantified. Secondly, in order to analyze the classification of tunnel risk level, a fusion model has been built based on the functions of average pupil diameter and HR growth rate. e tunnel environmental risk level has been divided into four levels via the fusion model, which can provide a guidance for the classification of tunnel risk level. Furthermore, the fusion model allows tunnel design and construction personnel to adopt different safety design measures for different risk levels, and this method can effectively improve the economy of tunnel operating safety design.

show abstract

“…For some research based on contexts like emotion recognition based on physiological data, it is not recommended to use only one data e.g., heart rate variability, to accurately detect emotions, because it can reflect emotions only in strong or intense emotional situations [25]. According to some studies, when an extended number of physiological data are considered, better results can be reached.…”

Section: Proposed Multimodal Sensing Systemmentioning

confidence: 99%

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

Roza

Postolache

2019

Sensors

View full text Add to dashboard Cite

The present work tries to fill part of the gap regarding the pilots' emotions and their bio-reactions during some flight procedures such as, takeoff, climbing, cruising, descent, initial approach, final approach and landing. A sensing architecture and a set of experiments were developed, associating it to several simulated flights (N f lights = 13) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator (N pilots = 8). It is shown that it is possible to recognize emotions from different pilots in flight, combining their present and previous emotions. The cardiac system based on Heart Rate (HR), Galvanic Skin Response (GSR) and Electroencephalography (EEG), were used to extract emotions, as well as the intensities of emotions detected from the pilot face. We also considered five main emotions: happy, sad, angry, surprise and scared. The emotion recognition is based on Artificial Neural Networks and Deep Learning techniques. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were the main methods used to measure the quality of the regression output models. The tests of the produced output models showed that the lowest recognition errors were reached when all data were considered or when the GSR datasets were omitted from the model training. It also showed that the emotion surprised was the easiest to recognize, having a mean RMSE of 0.13 and mean MAE of 0.01; while the emotion sad was the hardest to recognize, having a mean RMSE of 0.82 and mean MAE of 0.08. When we considered only the higher emotion intensities by time, the most matches accuracies were between 55% and 100%.

show abstract

Is heart rate variability (HRV) an adequate tool for evaluating human emotions? – A focus on the use of the International Affective Picture System (IAPS)

Cited by 134 publications

References 22 publications

Emotional Status and Productivity: Evidence from the Special Economic Zone in Laos

Emotional Status and Productivity: Evidence from the Special Economic Zone in Laos

Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

Contact Info

Product

Resources

About