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Physiological Computing

6 papers with code · 0 benchmarks · 2 datasets archive 2025-07-28

Physiological computing is an interdisciplinary field that focuses on the development of computational systems and technologies that interact with and respond to the physiological signals of the human body. These systems use sensors and algorithms to detect, analyze, and interpret physiological signals in real-time, allowing for a more natural and intuitive interaction between humans and computers.

Main goal: To create intelligent systems that can adapt to the user's physiological state, enhancing user experience, performance, and well-being. This field draws on knowledge from various disciplines, including computer science, engineering, psychology, neuroscience, and human-computer interaction.

Key components include: - Physiological Sensors: To capture physiological signals from the human body. Examples include electrocardiogram (ECG) sensors, electroencephalogram (EEG) sensors, electromyogram (EMG) sensors, and galvanic skin response (GSR) sensors. - Signal Processing and Analysis: Physiological signals are processed and analyzed using computational techniques to extract meaningful information about the user's physiological state. This may involve filtering, feature extraction, pattern recognition, and machine learning algorithms. - Adaptive Systems: Physiological computing systems use the information obtained from physiological signals to adapt their behavior in real-time. For example, a computer interface may adjust its presentation based on the user's level of attention, stress, or cognitive workload.

Applications: Physiological computing has applications in various domains, including healthcare, education, entertainment, gaming, virtual reality, and human-computer interaction. For example, physiological computing technologies can be used to develop biofeedback systems for stress management, adaptive learning environments, and immersive gaming experiences.

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

V4V eSports Sensors Dataset

Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Signal Processing

Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (12 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

2 Nov 2020 3 repositories listed

An important feature of the dataset is simultaneous data collection from five players, which facilitates the analysis of sensor data on a team level.
DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings

20 Aug 2017 2 repositories listed

Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements (e.
pyVHR: a Python framework for remote photoplethysmography

15 Apr 2022 1 repository listed

A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades.
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

29 Nov 2020 1 repository listed

In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter.
An Open Framework for Remote-PPG Methods and their Assessment

26 Nov 2020 1 repository listed

This paper presents a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG).
Physiological and Affective Computing through Thermal Imaging: A Survey

27 Aug 2019 1 repository listed

Thermal imaging-based physiological and affective computing is an emerging research area enabling technologies to monitor our bodily functions and understand psychological and affective needs in a contactless manner.

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