# Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors
## 6 Conclusions  
Recognizing multi-user activities requires complexity and diversity. User actions can be individual or collaborative. The targeted activities can be simple and involve little action, including reading or recreational activities, but also complex, including cooking or cleaning. Recognizing activities in residential spaces not only helps to identify activities but also improves everyday life. These intelligent residential spaces contribute to energy efficiency, optimizing the environment to meet the user's needs. It can even help identify negative habits and behaviors that can affect health. Any intelligent activity recognition system or intelligent residential space must respect data confidentiality and respect the users' personal space without interfering with their lives or trying to control them. One of the problems with the smart environment is that a user feels under surveillance and that he no longer has the freedom to carry out his activity without reasoning for each action he takes. That is why this type of system should use noninvasive electronic data recording systems and compensate for the missing information by using even more intelligent recognition methods. This study aimed to find a graphical representation method, which is as efficient as possible with the data obtained from a PIR sensor-based system. From the graphical representation methods tested, we conclude that the most efficient method was using generated windows based on coordinates. The length of the windows with a good result was 50 s. The tests in which this value was used obtained a recognition rate of 79%. We improved the recognition rate by 4% by finding parameter values that help in this regard. This was achieved by completing the initial architecture of the sequential neural network with four more convolutional layers and using the AdamW and Adadelta optimizer. With an accuracy result of 83%, this study met its objective of finding an efficient method of activity recognition. At the same time, the use of data windows proved to have a positive effect on the representation of chronological data. From the tests performed, it was proven that the window overlap did not have a positive result in the case of this method; moreover, it led to the creation of too much information data, which led to a decrease in recognition rate. Recognizing activities in a multi-user residential space is a complex process that involves, first and foremost, an understanding of the activities performed and the need for space adaptability and flexibility. Secondly, it also involves the integration of technologies for improving the housing experience. It also requires a careful approach to monitoring the users and protecting their privacy rights. Taking into account the good results obtained with this method, we will, in a future study, create a real-time system that recognizes activities and anticipates, based on future behaviors, the possible health problems that the users of the smart residential space could experience. The study method successfully identified multi-user activities at specific moments. Users cannot be identified, and activities are detected only as they occur individually or collaboratively. The dataset's existing data make solving this problem challenging. In addition, for user identification, we can improve this method by combining the data taken by the PIR sensors with data taken from a user's gadgets such as smartphones. These data can bring about new information about the user's location and behavior. Using a wearable device that the user is likely to have, such as a smartphone or a smart watch, the user does not feel invaded by technology. This new modal dataset will represent the input from which the algorithm will be able to extract new features, resulting in a better recognition rate. We have managed to obtain a better recognition rate, as compared with other studies, due to the chosen graphical image representation.