# Human Psychophysiological Activity Estimation Based on Smartphone Camera and Wearable Electronics
###### Abstract  
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'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. meditation estimation; neural networks; human behavior patterns +
Footnote †: journal: Pattern Recognition  
0000-0001-5000-5000]M. A. A.  
area of the brain responsible for memory and learning increases. It is worth noting that meditation practices, in fact, are aimed at focusing a person's mind on a particular object and using internal means of regulation. Within this paper, we propose to solve a fundamental problem and aim at confirming the hypothesis that the external behavior of a person depends on his/her internal (mental) state. The paper is based on our previous work [6], where we considered dynamic motivational strategies related to the human-oriented daily life activities. State-of-the-art methods of artificial intelligence allow us to solve the problem of classifying human behavior based on the analysis of external signs obtained as a result of analyzing images from a smartphone's camera and making conclusions about his/her internal state. We propose to classify the meditation process using image recognition techniques, as well as using information from wearable electronic sensors to estimate the quality of the practice. The relevance of the problem is confirmed by the recently growing demand for knowledge regarding the meditation practice and the widespread dissemination of this practice among the masses (for example, using the Headspace mobile application, whose annual income is USD 100 million), which emphasizes the social significance of the research. The scientific significance of the work is confirmed by the lack of models and methods today that allow a person to get an objective assessment of the quality of the meditation process based on an analysis of psychophysiological indicators. The methodology of the automated assessment of the human meditation practice, which is the fundamental objective of the project, is represented by three key processes. (1) First, there is the preliminary training of the system and classification of the meditation practice, which is based on a comparison of the data array formed from images obtained from the video camera and indicators read from motion sensors (accelerometer) and heart rate sensors within wearable electronics devices. Readings of electroencephalograms are obtained using specialized equipment and data from a questionnaire filled out by the person at the end of the practice of meditation in order to make a subjective assessment of its quality. (2) Next, there is a software-based automatic assessment of a person's meditation practice, using a smartphone's camera (aimed at the person during the practice) and wearable device sensors.