# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 1 Introduction  
Physical exercise is an important part of the daily routine that helps to overcome the risk of diseases as well as improve quality of life. Physical exercise is essential to prevent and reduce the risk of many diseases and improve physical and mental health [1]. People are quite busy in modern times and this trend is increasing day by day, therefore, this is a time to think about the appropriate technology for monitoring their activities and caring for their elderly [2]. An intelligent interface between the human body and the monitoring mechanism could solve this type of problem. In the recent years, many technologies have been implemented for extracting and collecting physiological data, such as heart rate, blood pressure, temperature, etc., using various types of sensors before, during, and after physical exercise [3, 4, 5], however, these techniques are not adequate for regular monitoring. An intelligent model using image processing techniques is required to measure physical exercise intensity during physical exercise.  
In recent years, many studies have proposed various methodologies to extract various physiological data and monitor physical exercise. Mainly, two approaches (contact sensor technology and contactless technology) are widely used in recent times to extractphysiological features whether during physical exercise or resting. Both the approaches have pros/cons, however, the proposed implementation is parallelly used. In contact sensor techniques, the sensors are attached on the body and the result is interpreted using machine learning or statistical approaches. For non-contact/contact sensor techniques, various types of cameras are used to capture video/images and processed with various image processing techniques including Infrared camera and thermal camera. Whatever the image extraction technique, they must be followed by computer vision or machine learning and deep learning techniques to interpret the result. The facial expression in different states of tiredness may depend on the age of people or may be different for male and female users. The intelligence interface should be more useful for elderly people who cannot control a physical activity machine themselves during physical activities. They do not even want to use a wearable device on the body; in this situation, this model gives a better way of monitoring.  
Exercise monitoring uses various techniques corresponding to the physiological parameter monitoring during exercise. [PERSON] [6] presented a review on sports video analysis to detect players, predicting trajectory for strategy planning. Likewise, [PERSON] [7] reviewed the video-based prediction of various parameters of the elderly patients and all the articles were focused on healthcare. A similar review article was presented by [PERSON] reviewing the articles related to vision-based approaches for physical rehabilitation related research studies and [PERSON] [8] presented review articles on school students' activity monitoring using computer vision techniques. However, a lot of review articles are published in state-of-art articles, to our knowledge, none of the review articles cover only non-contact and video-based techniques for exercise monitoring. In the review article, physical exercise monitoring using non-contact techniques is covered. The primary goal of this review article is to review the overview of the related articles. Each published paper is analyzed in terms of technology, dataset, methodology and results. The targeted subject for the experiments includes sportspeople, general people, special patients (if related) and the elderly. A wide range of human subjects are considered as the experimental subjects. We hope future researchers will have valuable guidelines for their further research direction by studying this article.