## 2 Related Works
### Action Recognition Datasets
**N-UCLA**[50]: the dataset includes 10 actions captured from different viewpoints (usually 3) using multiple Kinect devices, 10 actors perform following actions: pick up with one hand, pick up with two hands, drop trash, walk around, sit down, stand up, donning, doffing, throw, carry. **Sports 1M**[51]: this is the largest available dataset of human actions. It contains 1 million YouTube videos belonging to 487 classes. On average, 1000-3000 videos comprise each class. The video classes belong to the following macro-category: aquatic sports, team sports, winter sports, ball sports, combat sports, sports with animals. **UWA3D II**[52]: contains 30 actions, mainly captured from 4 different viewpoints (front, left, right and top view) with a Kinect device in the same environments. A total of 10 subjects perform the following actions: one hand waving, one hand punching, two hand waving, two hand punching, sitting down, standing up, vibrating, falling down, holding chest, holding head, holding back, walking, irregular walking, lying down, turning around, drinking, phone answering, bending, jumping jack, running, picking up, putting down, kicking, jumping, dancing, mopping floor, sneezing, sitting down (chair), squatting, coughing. **Kinetics**[25]: the dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10 s and is taken from a different YouTube video. The videos are annotated using Amazon's Mechanical Turk. The actions cover a wide range of situations including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. **DALY**[53]: the Daily Action Localization in YouTube contains high-quality temporal and spatial annotations for 3.6k instances of 10 actions in 31 h of videos (3.3 million frames). The actions belong to 10 categories: applying makeup on lips, brushing teeth, cleaning floor, cleaning, drinking, folding textile, ironing, phoning, playing harmonica, taking photos/videos. **Charades**[26]: the dataset has been created by hundreds of people recording videos in their own homes, acting out casual everyday activities. The dataset is composed of 9848 annotated videos with an average length of 30 s, showing activities of 267 people from three continents. In total, Charades provides 27,847 video descriptions, 66,500 temporally localized intervals for 157 action classes and 41,104 labels for 46 object classes. **NTU**[27]: the dataset includes 60 actions for a total of 56,880 videos acquired with a Kinect v2 device. Video sequences are recorded in different environments and captured from different viewpoints. Actions are gathered under the following categories: daily actions, medical conditions and mutual actions. As it can be seen, the datasets in the literature are different and designed with different aims in mind. There are datasets specific for a given scenario, and others which are very heterogeneous; datasets containing high-quality videos, and datasets with homemade videos; datasets with a static background and others with a dynamic background; etc. For a robust and reliable action recognition system, selecting the right dataset is crucial if we do not want to introduce any bias towards any specific acquisition condition or set of actions. Moreover, most of the datasets in the literature are not suitable for monitoring the actions of elderly people in an indoor scenario, while others contain a small selection of possible actions of interest. However, to the best of our knowledge, no public dataset contains actions specifically performed by elderly people, depicting instead either adult or young actors. This can represent a bias for elderly monitoring, as older subjects move with a different speed compared to younger people, and could perform actions in a different way.