## 1 Introduction  
Human Activity Recognition (HAR) based on the video content analysis approaches is gaining more and more interest thanks to the wide variety of possible applications, such as video surveillance, human-computer interfaces or monitoring of patients and elderly people in their living environments. An exemplary structure of the HAR system may consist of the following general modules: motion segmentation, object classification, human tracking, action recognition and semantic description [1]. If a focus is put to action recognition (exercise classification), it can be assumed that input data type, localised objects and their positions are known. Based on the taxonomy presented in [2] the techniques for action recognition are divided into holistic and local representations. Holistic solutions use global representations of human shape and movement, accumulating several features. The most popular solutions include Motion History Image and Motion Energy Image templates proposed by [PERSON] and [PERSON] [3] or Space-Time Volume representation introduced by [PERSON] and [PERSON] [4]. Local representations usually are based on interest points which are used to extract a set of local descriptors, e.g., Space-Time Interest Points by [PERSON] [5]. Instead of aggregating features from all frames, some researchers propose to extract only several foreground silhouettes, so called key poses (e.g., [6;7]). If binary silhouettes are used as input data, various shape features can be extracted and combined, such as shape and contour [8], orientation [9] or skeleton [10]. Apart from traditional approaches, more challenging tasks can benefit from the application of deep learning techniques, such as Convolutional Neural Networks [11]. Ultimately, the choice of methods is dependant, among others, on the application scenario and data complexity. A task of exercise classification can be related to the Ambient Assisted Living (AAL), which refers to concepts, products and services introducing new technologies for people in all phases of life, allowing them to stay healthy, independent, safe and well-functioning at their living environment. In the era of an ageing society and a significant proportion of older people living alone or unattended, expanding the range of care support options is becoming more and more important. Another major focus of AAL is prolonging the time people can live on their own, being in good health and in good physical shape. This is related to the increasing life expectancy and successful ageing. The World Health Organisation policy in Active Ageing applies to physical, mental and social well-being, and is defined in [12] as \"the process of optimizing opportunities for health, participation and security in order to enhance quality of life as people age\". Among people aged 45 and over, non-communicable diseases (NCDs) are the most frequent causes of mortality and disability all over the world. The risk of NCDs morbidity is higher in this age group; however, risk factors may originate in younger years. NCDs include, among others, cardiovascular diseases, hypertension, diabetes, chronic obstructive pulmonary disease, musculoskeletal conditions and mental health conditions. One of the risk factors is a sedentary lifestyle [12]. Low level of physical activity and lack of exercises can directly lead to obesity which increases the risk of NCDs as well. The study presented in [13] shows that greater physical fitness is associated with reduced risk of developing many NCDs. The authors of [14] advise promoting positive health behaviour rather than reducing negative ones, such as above-mentioned sedentary lifestyle. It is recommended to focus on the benefits of physical activity, provide motivation and promote self-care. Models based on social-cognitive behavioural theory are indicated as self-regulatory strategies that can contribute to increasing physical activity based on skills such as goal setting and self-monitoring of progress.