# Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors
## 2 Related Works
### Image Recognition Methods  
A method for determining multi-user human activities using wireless signals. The data were collected for 16 different activities, including individual and group activities. Three automatic activity recognition algorithmic methods were tested. The input data provided to the neural networks were matrices that contained the values of the wireless signals transmitted and received by the antenna, as well as the response frequency. The method based on the Extra Trees (ET) algorithm obtained an average accuracy of 98%, which is the best performing among the evaluated models. The Random Forest (RFo) method had an average accuracy of 97%. The lowest recognition rate method was the one based on the Decision Tree (DT) algorithm with an accuracy result of 90% [25]. The hybrid data representation method combines existing data with future data, which are represented in the form of monochrome thermal maps. The recognition algorithm is based on a convolutional neural network (CNN) combined with Gated Recurrent Unit (GRU) technology. The optimal approach for data graphic representation, which gave the best results, was to use a mix of a 30 min data window containing data already collected(past) and 15 min of future data (assumed). The recognition rate varies from 98% to 100% depending on the complexity of the activities. The study included eight static and dynamic activities: toileting, resting, exit, cooking, shower, PC, sleep, and kitchen [18]. The dataset provided by the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University is described in the article [13]. It uses both single-user and multi-user data. The tested model is based on the Markov model using the recorded data. For single-user data, the recognition rate was 83%. When it comes to the presence of pets in the residential space, the recognition rate drops between 3 and 6%. For data taken from the multi-user residential space, the recognition rate with this method is 11%. An innovative method that can be used to improve the HAR rate is a multi-agent model, called Hydra-TS, used for data augmentation. It proposes a multidimensional representation and the relationships between events. Even if this method is still at the beginning stages, the results are quite promising. With a recognition rate of about 67%, this method can determine the relationships between individuals, which can improve multi-user recognition [26]. The multi-user Kyoto dataset from CASAS was processed by adding features based on the most frequent activity. Two algorithms are tested, namely long-short-term memory (LSTM) and BiLSTM. The accuracy results for multi-users were 39% in the case of LSTM and 37% in the case of BiLSTM [27]. An efficient method of recognizing multi-user activities uses a dataset collected with data taken from two users. The users had 16 independent and collaborative activities. The recognition method is in two stages; the first stage involves the recognition of individual activities, and then, in the second stage, the generated data are provided to a network that predicts the following activities. A DNN network was used, and a recognition rate of 66.7% was obtained [28]. In Table 1, the results obtained by other studies in the field are presented. The results from studies using the CASAS multi-user dataset serve as references for this study. Although the results for other datasets are better, they use a dataset collected specifically for that study.