# Human Activity Recognition for Assisted Living Based on Scene Understanding
## 2 Related Work  
An approach to recognizing daily activities based on imaging and neural networks is presented by [PERSON], [PERSON] and [PERSON] in [20]. In the first part, the training and testing subsets are defined and the activity groups are decided based on three properties: duration, type and position. Each group is divided into two: by duration of activities, long or short; by type, dangerous or common; and by position, static or mobile. Based on the above classifications, there is a grouping by status, alarming situations and daily activities. The approach has two steps: detect the person using 'Faster R-CNN' [21] and then recognize the action by using two neural networks: I3D [22] and DeepHAR [23]. The obtained accuracy is 97% on status activities, 83% on alarming situations and 71% on daily activities.  
Another image-based approach is described in [24] and uses depth images and thermal images to maintain confidentiality, as these images do not retain details. The dataset was taken from an elderly person's home for one month. The defined classes are: the person sleeps, sits on a bed/chair, stands, walks, uses the nightstand and needs assistance from a caretaker. In addition to the six activities, the background was also noted: when the person is not in the room. Having two streams of images, the authors trained two models and decided to merge them into one [25; 26]. The deployed models use the ResNet-34 architecture [27], the average accuracy on the six activities and the background is: 94% on thermal images, 93.2% on depth images, 91.8% using early fusion and 95.8% using delayed fusion.  
[PERSON] et al. [28] presents an activity monitoring system using RGB images or data from an RGB-D, which provides 3D information about the objects in the scene. This system can recognize some basic activities: when a person is standing, sitting on a chair/sofa, walking or falling. The system uses RGB images, and the person's outline is extracted by a background removal algorithm. The system uses a model based on a set of key positions [29] and the most representative body position for each activity class is learned. When the RGB-D sensor data are used, the silhouette of the human body is easier to obtain. The body position is extracted and a generic algorithm is used to select the optimal joints in recognizing activity [30].  
In paper [31], the activity monitoring is carried out by means of depth sensors. The images are processed to extract the silhouette and then the human skeleton and its joints. Based on the joints, they compute three features in order to train a hidden Markov model: centroid points, joint distances, and joint magnitude. For each activity, there is a hidden Markov model trained, the obtained results have an accuracy of 84.33%.  
[PERSON], [PERSON] and [PERSON] in [32] use a method based on human detection by adaptive background removal and then extract the characteristics by which it will be detected whether a person has fallen. The extracted features are the aspect ratio between the height and the width of the bounding box that fits the person, gradients on X and Y, the fall angle. If the action takes place outside and there are more people, the accuracy is 79%, the sensitivity is 54%, and the specificity is 97%. However, if there is only one person involved, both in outdoor and indoor environments, accuracy, sensitivity, and specificity are 100%.  
A different approach in detecting human actions based on videos is presented by [PERSON] (Google) in [33; 34]. A 700 classes video dataset is used to train the I3D neural network. The model was trained and deployed on a machine with 32 P100 GPUs. The top-1 accuracy on the test set is 57.3%.  
A team of researchers from MIT-IBM AI Lab propose a dataset [35; 36] of one million short videos for dynamic events which take place over a period of time no longer than three seconds. 339 verbs are associated with over 1000 videos each. Results from three different models are combined resulting in a 31.16% top-1 recognition score.