Developing a System for Recognizing the Emotional States Using Physiological Devices

Khan, Ali Mehmood; Lawo, Michael

doi:10.1109/ie.2016.16

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…In [17], few emotional states are detected (e.g., Sad, Dislike, Joy, Stress, Normal, No-Idea, Positive and Negative) by using a Decision Tree classifier. Four physiological sensors, BVP, EMG, GSR, and Skin Temperature are adopted.…”

Section: Background and Related Workmentioning

confidence: 99%

A user-centered approach for detecting emotions with low-cost sensors

Francese

Risi

Tortora

2020

Multimed Tools Appl

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Detecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users’ biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user’s affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance.

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Section: Background and Related Workmentioning

confidence: 99%

A user-centered approach for detecting emotions with low-cost sensors

Francese

Risi

Tortora

2020

Multimed Tools Appl

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“…Three physiological sensors were used: blood pulse volume, galvanic response of the skin, and skin temperature. The developed system (Khan & Lawo, 2016) was able to recognize the emotional states mentioned previously with high accuracy (around 95%), both in the problem of two classes (positive, negative), and in the six classes (taste, normal, sadness, disgust, stress, no-idea) using classification algorithms based on decision trees.…”

Section: Physiological Response and Decision Makingmentioning

confidence: 98%

“…Emotions have been considered as a dynamic process that affects social relationships and influences the mechanisms of rational thinking and decision-making (Khan & Lawo, 2016), a way of incorporating emotions into a computer system is through the extraction of characteristics from a variety of physiological signals, and from the analysis and identification of patterns to infer the corresponding emotion. Researchers from the University of Bremen proposed the development of a system for recognition of emotions using the e-Health platform (Khan & Lawo, 2016). The emotions were categorized into two groups, positive (Joy, normal) and negative (sadness, disgust, stress).…”

Section: Physiological Response and Decision Makingmentioning

confidence: 99%

Decision Making, Stress Assessed by Physiological Response and Virtual Reality Stimuli

Mosquera-Dussán

Guzmán-Pérez

Terán-Ortega

et al. 2020

Rev. colomb. psicol.

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Many decisions must be made under stress; therefore, stress and decision-making are intrinsically related not only at the behavioral level but also at the neural level. Additionally, virtual reality tools have been proposed as a method to induce stress in the laboratory. This review focuses on answering the following research question: Does stress assessed by physiological variables of a subject under virtual reality stimuli increase the chances of error in decision-making? The reviewed studies were consulted in the following databases: PubMed, IEEE Xplore, and Science Direct. The analysis of the consulted literature indicates that the stress induced in the laboratory using virtual reality tools and the physiological response of the central and autonomous nervous system are complementary subjects and allow the design of training and support systems for the decision-making process

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“…A few studies exist which used the abundance of information collected by wearable devices, but they were never tested in a real world, working environment. For example, Khan and Lawo [26] collected various physiological information by using a pulse sensor and an eHealth platform, including the following sensors: 2D accelerometer, blood pressure, oxygen in the blood, body temperature, airflow, electrocardiogram (ECG), electromyography and GSR. While wearing these devices, the 24 participants were shown 100 images one after another.…”

Section: Related Workmentioning

confidence: 99%

Measuring happiness increases happiness

Roessler

Gloor

2020

J Comput Soc Sc

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Happiness has been an overarching goal of mankind at least since Aristotle spoke of Eudaimonia. However measuring happiness has been elusive and until now has almost exclusively been done by asking survey questions about self-perceived happiness. We propose a novel approach, tracking happiness and stress through changes in body signals with a smartwatch, the "Happimeter". It predicts individual emotions from sensor data collected by an Android Wear smartwatch, such as acceleration, heartbeat, and activity. The Happimeter was used over three months in the innovation lab of a bank with 22 employees to measure individual happiness, activity, and stress. The participants were randomly divided into an experimental and a control group of similar size. Both groups wore the watch and entered their subjective happiness, activity and stress levels several times a day. The user-entered ratings were then used to train a machine learning system using the sensors of the smartwatch to subsequently automatically predict happiness, activity, and stress. The experimental group received ongoing feedback about their mood and which activity, sensor signals, or other people, made them happier or unhappier, while the control group did not get any feedback about their predicted and manually entered emotions. Just like in quantum physics we observed a "Heisenberg Effect", where the participants made aware of their measurements changed their behavior: Members of the experimental group that received happiness feedback were 16% happier, and 26% more active than the control group at the end of the experiment. No effect was observed for stress. Fig. 13 Website shwing who is influencing the mood of a person

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Developing a System for Recognizing the Emotional States Using Physiological Devices

Cited by 7 publications

References 13 publications

A user-centered approach for detecting emotions with low-cost sensors

A user-centered approach for detecting emotions with low-cost sensors

Decision Making, Stress Assessed by Physiological Response and Virtual Reality Stimuli

Measuring happiness increases happiness

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