# Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors
## 1 Introduction
Geospatial location data are also needed for outdoor activities, not just human action-specific data [12]. This research aims to develop an efficient way of recognizing human activity for daily tasks based on a continuous activities dataset. The main objectives are the following:  
* Validating the proposed method by testing with plot images, which were generated on the basis of the Aruba CASAS dataset from our previous study, and comparing the results with our research described in [13]. * Generating new images based on the proposed method from our study [13] but using the Kyoto Multiresident ADL Activities dataset provided by the Center for Advanced Studies in Adaptive Systems (CASAS). * Using a new data representation method and comparing the results with the previous method. * Optimizing the activity recognition neural network and testing several parameters to increase the activity recognition accuracy. * Recognizing daily activities through a method capable of interpreting continuous data flows and creating a method compatible with real-time systems. The improved results of the neural network were validated using the study's single-user dataset [13] to assess the recognition rate. The recognition rate obtained was 84.54%. Our method achieved a 99% result on the same dataset. In this way, we have the confirmation that the network structure is a performant one for recognizing images that contain information about the user's activity. Having confirmed the operation of the method chosen in this study, we generated images for the multi-user dataset, using two methods of graphic data representation from the datasets made available by the CASAS project. The graphic method was chosen for the following reasons:  
* Image recognition algorithms are very efficient. * The graphic representation can take into account the spatial aspect of the activity by depicting it in the form of a map. * New features can be introduced to improve the graphic representation, including the temporal spectrum and not the actual activity execution time. A fixed-size data window was chosen for each image (graphical representation). These data are represented by two methods. The first is to represent data using density maps in which, for each sensor activation, the representation color will intensify. Regarding the location where the state of a sensor is represented in the image, it corresponds to the location on the map of the residential space. The second representation option is the graphical representation option of the data from a data window. Data representation involved plotting coordinates and connecting consecutive points to observe temporal changes in sensor activations by comparing shifted coordinates. These generated images were uploaded to a cloud location and then fed to a sequential convolutional neural network, using the Google Colab platform. Details related to the dataset, the window generation process in Section 3, and the structure of the neural network can be found in Section 4. Given this way of representing data using fixed-size windows, the method can also be used in real-time recognition systems. For each window, a time-related dimension is used, and all sensor activations at that moment are taken into account. For the real-time method, these windows can retrieve the data (sensor activation status) directly from the active CASAS system. The problem of recognizing multi-user activities is very different from those of single users. A single-user activity is easier to represent both graphically and using a set of numerical data. When we talk about the recognition of multi-user activities, the problem of collaborative activities and the interleaving of data from sensors come into play. Another issue is represented by user identification in the context of multi-user activities. In this study, we analyze the recognition of activities at a given time, without trying to recognize the identity of the users involved.