{"url":"/task/physiological-computing","name":"Physiological Computing","slug":"physiological-computing","description_markdown":"**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.\r\n\r\n*Main goal:*\r\nTo 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.\r\n\r\n*Key components include:*\r\n- 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.\r\n- 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.\r\n- 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.\r\n\r\n*Applications:*\r\nPhysiological 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.","categories":[{"name":"Computer Vision","url":"/area/computer-vision"},{"name":"Medical","url":"/area/medical"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":12,"papers_with_code":6,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":2,"subtasks":0,"parent_tasks":1},"benchmarks":[],"datasets":[{"url":"/dataset/v4v","name":"V4V","full_name":"Vision for Vitals","num_papers_in_archive":11},{"url":"/dataset/esports-sensors-dataset","name":"eSports Sensors Dataset","full_name":"","num_papers_in_archive":4}],"subtasks":[],"parent_tasks":[{"url":"/task/signal-processing","name":"Signal Processing"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":6,"of":6,"tagged_in_all":12,"items":[{"url":"/paper/collection-and-validation-of","title":"Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset","date":"2020-11-02","arxiv_id":"2011.00958","repositories_listed":3,"syntology":null},{"url":"/paper/deepbreath-deep-learning-of-breathing","title":"DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings","date":"2017-08-20","arxiv_id":"1708.06026","repositories_listed":2,"syntology":null},{"url":"/paper/pyvhr-a-python-framework-for-remote","title":"pyVHR: a Python framework for remote photoplethysmography","date":"2022-04-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/detecting-video-game-player-burnout-with-the","title":"Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning","date":"2020-11-29","arxiv_id":"2012.02299","repositories_listed":1,"syntology":null},{"url":"/paper/an-open-framework-for-remote-ppg-methods-and","title":"An Open Framework for Remote-PPG Methods and their Assessment","date":"2020-11-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/physiological-and-affective-computing-through","title":"Physiological and Affective Computing through Thermal Imaging: A Survey","date":"2019-08-27","arxiv_id":"1908.10307","repositories_listed":1,"syntology":null}],"syntology_records":0,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}