# Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors
## 3 Dataset and Data Representation  
### Dataset Information  
This study utilized the Kyoto Multiresident ADL Activities dataset created by the Center for Advanced Studies in Adaptive Systems at Washington State University. The dataset is freely available and can be downloaded from [30]. The Aruba single-user dataset was also used to prove the concept and is freely available and can be downloaded from [31]. For this study, two methods of data graphical representation were used. For the first method, the data were represented as density maps, and for the second method, the data were represented as a temporal perspective graph. The raw dataset includes the activation and deactivation status of the PIR sensors that are part of the main data acquisition system. In order to be able to represent the data, the dataset was first cleaned of redundant data such as sensor inactivity status states. The main dataset used for this study is the Kyoto Multiresident ADL Activities set, provided by the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University. In order to generate this dataset, 40 people participated and followed a list of activities they had to perform. Table 2 presents the list of activities, which is also mentioned in the article [32], the number of persons and the activity type. The activities were carried out in sets of two people and the activities were both individual and collaborative. This means that at one point in time, one or two people carried out activities, some of whom intersected with each other. The study that describes the data acquisition system, activities, and raw data representation is described in the article [32]. In addition, the dataset is annotated from the point of view of the activities and the people who actually performed the activities. The data are stored in text type files, where the states of the sensors recorded during the data collection are saved and structured as one record per row. A single recording contains the recording date, time, sensor name, sensor status, the label with the people performing the activity, and the activity number as presented in Table 2. The reason is that the \"OFF\" state of the sensor does not represent the end of the activity in the sensor's action area but rather its state of inactivity. This state is inserted automatically after a period of time specific to each type of sensor. In the dataset, there are data that were incorrectly recorded or did not appear labeled, and they were removed so as not to negatively influence the recognition of the activities. For the current study, only the data from the motion sensors (marked with M) and those placed on the doors of the rooms (marked with D) were taken into account. In the pre-processing phase, only the chosen sensor type data were retrieved and used further. Although not used, the dataset also contains raw and labeled data from other sensor types, but since they are more uncommon than the PIR-based sensors, they were discarded. Figure 1 shows the steps that were followed to clean the raw data.  
Figure 1: Initial data cleaning from redundant data.