Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

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.

Ensuring safety of people in the urban environment is an important area for improving the quality of people’s lives, and the development of modern intelligent technologies creates new opportunities to achieve this goal. The use of modern intelligent technologies can realize the potential of video surveillance systems, making possible the automatic recognition of dangerous situations in real time in order to take timely measures to handle them and provide aid to victims. Often, a dangerous situation is caused by deviant behavior of people – robbery, fight, vandalism, etc. But the existing works focused on recognizing deviant behavior are only focused on recognizing short-term distinguishing features, such as punches, falls or weapons in the hands of a person. At the same time, such features may often be absent, for instance, when a passerby is robbed but no fighting occurs, which is why the task of recognizing complex long-term scenes of deviant behavior remains unresolved. This paper proposes a method for automatic recognition of long-term human deviant behavior scenes, characterized by the integration of knowledge management and computer vision technologies for detecting and segmenting objects, estimating the three-dimensional human skeleton, tracking objects in video, estimating the ground plane normal to calculate the camera distance, and classification of short-term actions of people using three-dimensional skeleton. This method can be used in the development of a decision support system by operators of video monitoring systems used to detect and handle deviant behavior of people in real time in order to prevent escalation, provide timely aid to victims and detain suspects.