## 4 Experimental Conditions and Results  
The experiments were performed with the use of the [PERSON] dataset [30], which consists of short video sequences that last up to several seconds (144 \(\times\) 180 px, 50 fps). Foreground binary masks extracted from the database were made available by its authors and are here used as input data. There are masks for 93 sequences, however three of them are removed--one actor doubled three actions by moving with two different directions. In result, the database has 10 action classes and each action type is performed by nine different actors. During the experiments we follow the data processing steps presented in the previous section. Firstly, each frame is preprocessed individually and then all sequences are divided into two subsets--actions performed in place and actions with changing location of a silhouette. After preprocessing the number of images in a sequence varies from 28 to 146. The group of actions performed in place consists of five action classes: 'bend', 'jump in place', 'jumping jack', 'wave one hand' and 'wave two hands', whereas actions with changing location of a silhouette are: 'jump forward on two legs', 'run','skip', 'walk' and 'gallop sideways'. Figure 4 depicts some selected masks after preprocessing step (for two different actions). The next steps, including shape description, action representation and action classification, are performed separately in each subgroup. Ultimately, the classification accuracy values of both subgroups are averaged, which gives the final effectiveness of the approach. The main part of the experiments refers to the assumed application scenario, which is related to the Ambient Assisted Living and the concept of active ageing. In this scenario, a human activity analysis is performed to identify types of exercises. Physical activity is indicated as one of the methods of preventing the risk of developing non-communicable diseases. Based on the social-cognitive behavioural theory it is advised to promote self-care and incorporate self-monitoring of progress. Nowadays, video content analysis techniques and the popularity of cameras (in laptops, smartphones) facilitate the implementation of exercise monitoring solutions. In order to carry out the experiment concerning the recognition of types of exercises, we composed a database using selected classes from the [PERSON] database. Action classes were compared with the recommended exercises presented in [15]. In addition, it was taken into account that exercises are supposed to be performed in a home environment. Due to that, the 'run' class is excluded. Moreover, there are two classes with waving action, therefore the 'wave with one hand' class is excluded as well. The remaining action classes may be associated with the following exercises (based on [15]):  
* Aerobic/endurance exercise, in which the body's large muscles move in a rhythmic manner (e.g., walking, skipping, jumping jack);
* Balance training, which includes various activities that increase lower body strength (e.g., galloping sideways, jumping in place, jumping forward on two legs);
* Flexibility exercise, which preserves or extends motion range around joints (e.g., waving, bending). Several experiments were carried out to investigate the best combination of methods and parameters for the approach. Twenty simple shape descriptors were tested in combination with three matching measures and the use of up to 256 Fast Fourier Transform coefficients. In order to focus only on the highest results and be able to appropriately present them, for each matching measure an  
Figure 4: Exemplary preprocessed masks for ‘walk’ (**top row**) and ‘jumping jack’ (**bottom row**) actions. experiment is performed with several tests, in which a selected simple shape descriptor and different number of coefficients are used. The results of exercise recognition are provided in Table 1. The best result is considered as the highest accuracy and the smallest action representation.