A Survey of Emotion Recognition using Physiological Signal in Wearable Devices

2021 ◽  
Author(s):  
Hamidan Z. Wijasena ◽  
Ridi Ferdiana ◽  
Sunu Wibirama
Keyword(s):  
Emotion Recognition ◽  
Wearable Devices ◽  
Physiological Signal

scholarly journals EEG-based human emotion recognition using entropy as a feature extraction measure

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pragati Patel ◽  
Raghunandan R ◽  
Ramesh Naidu Annavarapu
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Computer Interface ◽  
Eeg Signal ◽  
Emotion Classification ◽  
Emotion Identification ◽  
Physiological Signal ◽  
Wide Range ◽  
Electrical Activities ◽  
The Brain

AbstractMany studies on brain–computer interface (BCI) have sought to understand the emotional state of the user to provide a reliable link between humans and machines. Advanced neuroimaging methods like electroencephalography (EEG) have enabled us to replicate and understand a wide range of human emotions more precisely. This physiological signal, i.e., EEG-based method is in stark comparison to traditional non-physiological signal-based methods and has been shown to perform better. EEG closely measures the electrical activities of the brain (a nonlinear system) and hence entropy proves to be an efficient feature in extracting meaningful information from raw brain waves. This review aims to give a brief summary of various entropy-based methods used for emotion classification hence providing insights into EEG-based emotion recognition. This study also reviews the current and future trends and discusses how emotion identification using entropy as a measure to extract features, can accomplish enhanced identification when using EEG signal.

Physiological signal-based emotion recognition system

2017 ◽  
Author(s):  
Saif Hassani ◽  
Ibrahim Bafadel ◽  
Abdelrahman Bekhatro ◽  
Ebraheim Al Blooshi ◽  
Soha Ahmed ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Recognition System ◽  
Physiological Signal

Automatical Emotion Recognition Based on Daily Gait

2019 ◽  
pp. 61-74
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Mental Health ◽  
Emotion Recognition ◽  
Health Monitoring ◽  
Wearable Devices ◽  
Psychology Research ◽  
Acceleration Sensor ◽  
Emotion Identification ◽  
Technology Application ◽  
Sensing Technology ◽  
Acceleration Data

In this chapter, a kind of emotion recognition method based on gait using a customized smart bracelet with a built-in acceleration sensor was introduced in detail. The results showed that the classification accuracies of angry-neutral, happy-neutral, angry-happy, and angry-happy-neutral using the acceleration data of wrist are 91.3%, 88.5%, 88.5%, and 81.2%, respectively. Besides, the wearable devices and motion-sensing technology application in psychology research have been further discussed, and non-contact emotion identification and mental health monitoring based on offline behaviors were reviewed summarily.

scholarly journals A Robust Beat-to-Beat Artifact Detection Algorithm for Pulse Wave

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Qihan Hu ◽  
Xintao Deng ◽  
Xin Liu ◽  
Aiguo Wang ◽  
Cuiwei Yang
Keyword(s):  
Pulse Wave ◽  
Smart Cities ◽  
Detection Algorithm ◽  
Wearable Devices ◽  
Segmentation Algorithm ◽  
Artifact Detection ◽  
Physiological Conditions ◽  
Physiological Signal ◽  
Pulse Waves ◽  
Complex Algorithm

With the rise of the concept of smart cities and healthcare, artificial intelligence helps people pay increasing attention to the health of themselves. People can wear a variety of wearable devices to monitor their physiological conditions. The pulse wave is a kind of physiological signal which is widely applied in the physiological monitoring system. However, the pulse wave is susceptible to artifacts, which prevents its popularization. In this work, we propose a novel beat-to-beat artifact detection algorithm, which performs pulse wave segmentation based on wavelet transform and then detects artifacts beat by beat based on the decision list. We verified our method on data acquired from different databases and compared with experts’ annotations. The segmentation algorithm achieved an accuracy of 96.13%. When it is applied to detect main peaks, the performance achieved an accuracy of 99.11%. After the previous segmentation algorithm, the artifact detection algorithm can detect beat-to-beat pulse waves and artifacts with an accuracy of 98.11%. The result indicated that the proposed method is robust for pulse waves of different patterns and could effectively detect the artifact without the complex algorithm. In summary, our proposed algorithm is capable of annotating pulse waves of various patterns and determining pulse wave quality. Since our method is developed and evaluated on the transmission-mode PPG data, it is more suitable for the devices and applications inside the hospitals instead of reflectance-mode PPG.

scholarly journals An Ear Wearable Device System for Facial Emotion Recognition Disorders

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhengxu Lian ◽  
Yingjie Guo ◽  
Xinyu Cao ◽  
Wendi Li
Keyword(s):  
Emotion Recognition ◽  
Data Storage ◽  
Mobile Terminal ◽  
Recognition Algorithm ◽  
Facial Emotion Recognition ◽  
Wearable Device ◽  
Facial Emotion ◽  
Data Sets ◽  
Physiological Signal ◽  
Surrounding Environment

A wearable device system was proposed in the present work to address the problem of facial emotion recognition disorders. The proposed system could comprehensively analyze the user’s own stress status, emotions of people around, and the surrounding environment. The system consists of a multi-dimensional physiological signals acquisition module, an image acquisition and transmission module, a user interface of the user mobile terminal, and a cloud database for data storage. Moreover, a deep learning based multi-model physiological signal pressure recognition algorithm and a facial emotion recognition algorithm were designed and implemented in the system. Some publicly available data sets were used to test the two algorithms, and the experiment results showed that the two algorithms could well realize the expected functions of the system.

scholarly journals Emotion recognition based on customized smart bracelet with built-in accelerometer

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2258 ◽  
Author(s):  
Zhan Zhang ◽  
Yufei Song ◽  
Liqing Cui ◽  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Human Computer Interaction ◽  
Emotion Recognition ◽  
Wearable Devices ◽  
Time Interval ◽  
Classification Models ◽  
Raw Data ◽  
Film Clips ◽  
Novel Method ◽  
Human Emotions ◽  
Computer Interaction

Background:Recently, emotion recognition has become a hot topic in human-computer interaction. If computers could understand human emotions, they could interact better with their users. This paper proposes a novel method to recognize human emotions (neutral, happy, and angry) using a smart bracelet with built-in accelerometer.Methods:In this study, a total of 123 participants were instructed to wear a customized smart bracelet with built-in accelerometer that can track and record their movements. Firstly, participants walked two minutes as normal, which served as walking behaviors in a neutral emotion condition. Participants then watched emotional film clips to elicit emotions (happy and angry). The time interval between watching two clips was more than four hours. After watching film clips, they walked for one minute, which served as walking behaviors in a happy or angry emotion condition. We collected raw data from the bracelet and extracted a few features from raw data. Based on these features, we built classification models for classifying three types of emotions (neutral, happy, and angry).Results and Discussion:For two-category classification, the classification accuracy can reach 91.3% (neutral vs. angry), 88.5% (neutral vs. happy), and 88.5% (happy vs. angry), respectively; while, for the differentiation among three types of emotions (neutral, happy, and angry), the accuracy can reach 81.2%.Conclusions:Using wearable devices, we found it is possible to recognize human emotions (neutral, happy, and angry) with fair accuracy. Results of this study may be useful to improve the performance of human-computer interaction.

scholarly journals Emotion Recognition from Physiological Signal Analysis: A Review

2019 ◽  
Vol 343 ◽  
pp. 35-55 ◽  
Author(s):  
Maria Egger ◽  
Matthias Ley ◽  
Sten Hanke
Keyword(s):  
Emotion Recognition ◽  
Signal Analysis ◽  
Physiological Signal

scholarly journals Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 319 ◽  
Author(s):  
Evangelos Kafantaris ◽  
Ian Piper ◽  
Tsz-Yan Milly Lo ◽  
Javier Escudero
Keyword(s):  
Time Series ◽  
Wearable Devices ◽  
Physiological Monitoring ◽  
Disruptive Effect ◽  
Respiratory Impedance ◽  
Common Occurrence ◽  
Original Algorithm ◽  
Physiological Signal ◽  
Monitoring Applications ◽  
Improved Performance

Entropy quantification algorithms are becoming a prominent tool for the physiological monitoring of individuals through the effective measurement of irregularity in biological signals. However, to ensure their effective adaptation in monitoring applications, the performance of these algorithms needs to be robust when analysing time-series containing missing and outlier samples, which are common occurrence in physiological monitoring setups such as wearable devices and intensive care units. This paper focuses on augmenting Dispersion Entropy (DisEn) by introducing novel variations of the algorithm for improved performance in such applications. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate interval, electroencephalogram, and respiratory impedance time-series. Our results indicate that the algorithmic variations of DisEn achieve considerable improvements in performance while our analysis signifies that, in consensus with previous research, outlier samples can have a major impact in the performance of entropy quantification algorithms. Consequently, the presented variations can aid the implementation of DisEn to physiological monitoring applications through the mitigation of the disruptive effect of missing and outlier samples.

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