Human Psychophysiological Activity Estimation Based on Smartphone Camera and Wearable Electronics
| Autor: | Igor Lashkov, Igor Ryabchikov, Vladislav Malutin, Polina Mikhailova, Mikhail Kruglov, Alexey Kashevnik, Nikolay Teslya, Evgeny Ripachev, Nikita Saveliev |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
lcsh:T58.5-58.64 Computer Networks and Communications Computer science business.industry lcsh:Information technology media_common.quotation_subject 02 engineering and technology Human body 030204 cardiovascular system & hematology neural networks Objective assessment 03 medical and health sciences 0302 clinical medicine Human–computer interaction human behavior patterns 0202 electrical engineering electronic engineering information engineering meditation estimation 020201 artificial intelligence & image processing Objective information Meditation business Competence (human resources) Wearable technology media_common |
| Zdroj: | Future Internet, Vol 12, Iss 111, p 111 (2020) Future Internet Volume 12 Issue 7 |
| ISSN: | 1999-5903 |
| Popis: | This paper presents a study related to human psychophysiological activity estimation based on a smartphone camera and sensors. In recent years, awareness of the human body, as well as human mental states, has become more and more popular. Yoga and meditation practices have moved from the east to Europe, the USA, Russia, and other countries, and there are a lot of people who are interested in them. However, recently, people have tried the practice but would prefer an objective assessment. We propose to apply the modern methods of computer vision, pattern recognition, competence management, and dynamic motivation to estimate the quality of the meditation process and provide the users with objective information about their practice. We propose an approach that covers the possibility of recognizing pictures of humans from a smartphone and utilizes wearable electronics to measure the user&rsquo s heart rate and motions. We propose a model that allows building meditation estimation scores based on these parameters. Moreover, we propose a meditation expert network through which users can find the coach that is most appropriate for him/her. Finally, we propose the dynamic motivation model, which encourages people to perform the practice every day. |
| Databáze: | OpenAIRE |
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