A Review of Emotion Recognition Using Physiological Signals

Shu, Lin; Xie, Jinyan; Yang, Mingyue; Li, Ziyi; Li, Zhenqi; Liao, Dan; Xu, Xiangmin; Xinyi, Yang

doi:10.3390/s18072074

Cited by 580 publications

(328 citation statements)

References 134 publications

Supporting

Mentioning

324

Contrasting

Unclassified

Order By: Relevance

“…In this section, we compare the performance of our method with widely-used traditional machine learning (ML) and deep learning (DL) methods. For the ML methods, we select the mean, standard variance, average root mean square, maximum amplitude, average amplitude and mean of the absolute values for SCR, SCL, SC, PD, first and second differential of SC and PD according to [25]. We also test these methods through the CorrFeat generated from our method.…”

Section: Comparison With ML and Dl Methodsmentioning

confidence: 99%

CorrFeat: Correlation-based Feature Extraction Algorithm using Skin Conductance and Pupil Diameter for Emotion Recognition

Zhang¹,

Ali

Wang³

et al. 2019

2019 International Conference on Multimodal Interaction

View full text Add to dashboard Cite

To recognize emotions using less obtrusive wearable sensors, we present a novel emotion recognition method that uses only pupil diameter (PD) and skin conductance (SC). Psychological studies show that these two signals are related to the attention level of humans exposed to visual stimuli. Based on this, we propose a feature extraction algorithm that extract correlation-based features for participants watching the same video clip. To boost performance given limited data, we implement a learning system without a deep architecture to classify arousal and valence. Our method outperforms not only state-of-art approaches, but also widely-used traditional and deep learning methods.

show abstract

Section: Comparison With ML and Dl Methodsmentioning

confidence: 99%

CorrFeat: Correlation-based Feature Extraction Algorithm using Skin Conductance and Pupil Diameter for Emotion Recognition

Zhang¹,

Ali

Wang³

et al. 2019

2019 International Conference on Multimodal Interaction

View full text Add to dashboard Cite

show abstract

“…The recognition of emotions based on physiological signals covers different aspects: emotional models, methods for generating emotions, common emotional data sets, characteristics used and choices of classifiers. The whole framework of emotion recognition based on physiological signals has recently been described by [55].…”

Section: Physiological Signalsmentioning

confidence: 99%

Survey on AI-Based Multimodal Methods for Emotion Detection

Maréchal

Mikołajewski

Tyburek

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Automatic emotion recognition constitutes one of the great challenges providing new tools for more objective and quicker diagnosis, communication and research. Quick and accurate emotion recognition may increase possibilities of computers, robots, and integrated environments to recognize human emotions, and response accordingly to them a social rules. The purpose of this paper is to investigate the possibility of automated emotion representation, recognition and prediction its state-of-the-art and main directions for further research. We focus on the impact of emotion analysis and state of the arts of multimodal emotion detection. We present existing works, possibilities and existing methods to analyze emotion in text, sound, image, video and physiological signals. We also emphasize the most important features for all available emotion recognition modes. Finally, we present the available platform and outlines the existing projects, which deal with multimodal emotion analysis.

show abstract

“…In this physiological-sensing realm, wearables are unparallelled. Previous reviews have carefully considered wearable sensors for driving safety [3], wearable sensors for emotion recognition [4,5], and combinations of wearable and in-car sensors for detecting driver drowsiness [6,7] and distraction [7,8]. The current review covers the recent (since 2000) state of the literature on sensors that monitor driving behaviors, including emotions experienced while driving, and sensors designed for nondriving contexts that can detect emotional and physiological states applicable to transportation safety.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior

Welch

Harnett

Lee

2019

Safety

View full text Add to dashboard Cite

Using sensors to monitor signals produced by drivers is a way to help better understand how emotions contribute to unsafe driving habits. The need for intuitive machines that can interpret intentional and unintentional signals is imperative for our modern world. However, in complex human-machine work environments, many sensors will not work due to compatibility issues, noise, or practical constraints. This review focuses on practical sensors that have the potential to provide reliable monitoring and meaningful feedback to vehicle operators-such as drivers, train operators, pilots, astronauts-as well as being feasible for implementation and integration with existing work infrastructure. Such an affect-sensitive intelligent vehicle might sound an alarm if signals indicate the driver has become angry or stressed, take control of the vehicle if needed, and collaborate with other vehicles to build a stress map that improves roadway safety. Toward such vehicles, this paper provides a review of emerging sensor technologies for driver monitoring. In our research, we look at sensors used in affect detection. This insight is especially helpful for anyone challenged with accurately understanding affective information, like the autistic population. This paper also includes material on sensors and feedback for drivers from populations that may have special needs.Safety 2019, 5, 72 2 of 18 would be challenging to apply in vehicles because of noise sensitivity and data-processing requirements. Beyond engineering considerations, it would be unrealistic to expect daily drivers to apply the EEG sensor before each trip because it would be a new behavior and a time-consuming departure from the driver's routine in a world where it is already difficult to get people to wear seatbelts. Wearable sensing devices such as watches and eyeglasses that fit into a driver's established routine are more practical. Video cameras, reviewed in 2016 by Fernández et al. [1], are practical in the sense that users do not need to activate or touch them, but cameras and microphones also introduce privacy concerns and produce high bandwidth data that requires processing. New soft and textile-embedded sensor formats are promising because they can be fitted to vehicle interiors, and because they can detect safety-relevant activity using body contact data that is not as personally identifiable as video and audio streams. Figure 1 illustrates a pressure sensor for tracking a driver's grip pressure in (a) a body-worn format, and (b) a vehicle surface format. Wearable sensors such as the glove in (a) are more practical for daily use than, for example, blood sampling to measure glucose [2] or cortisol levels, or neural implants to detect brain activity in animal studies. Such invasive sampling can validate conclusions drawn from proxy signals available at the body surface, but a sensor glove is better for daily wear from the user's viewpoint. However, for this grip-tracking application, the driver would have to modify their behavior to put gloves on, would need ...

show abstract

A Review of Emotion Recognition Using Physiological Signals

Cited by 580 publications

References 134 publications

CorrFeat: Correlation-based Feature Extraction Algorithm using Skin Conductance and Pupil Diameter for Emotion Recognition

CorrFeat: Correlation-based Feature Extraction Algorithm using Skin Conductance and Pupil Diameter for Emotion Recognition

Survey on AI-Based Multimodal Methods for Emotion Detection

A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior

Contact Info

Product

Resources

About