# Intelligent Monitoring Platform to Evaluate the Overall State of People with Neurological Disorders
### Creation of a People Concentration Model in the Workstation  
The system presented in this paper can be used for multiple pathologies and applications. The acquisition system and the database allow to easily adapt or integrate new sensors. The signal processing, the feature analysis, and the machine learning algorithms used to generate the model will also be defined according to the objective. To show the workflow of the platform, an experimentation to validate the system is performed. The test consist of generating a personalized model of the concentration of a person in two different workstations within the same research laboratory. One of the places is located at the entrance of the laboratory, next to the door. Any person who must access or leave the laboratory has to pass by this position, which we will call Position A. The second position (Position B) is located in an isolated room within the same laboratory. The final goal of the model is to predict at which times the person is focused on the tasks being performed and at which times the concentration is reduced, for example, due to an interruption or stimuli around the workstation. This will provide information on which of the positions is the most suitable to work at. Although this application is not directly related to assisting people with neurological disorders, it is an example of the versatility that the system can offer as well as to show the working methodology of the platform. In addition, a similar model could also be proposed for a person with a neurological disorder so that it would be possible to analyze how the user adapts to a specific work  
Figure 6: Workflow of dataset split and cross validation process. environment. The two possible situations are labeled as _\"Focused\"_ and _\"Distracted\"_, and the output of the predictive model will be one of these two options. First, it is necessary to define what will be considered as _\"Focused\"_ and _\"Distracted\"_. For this example, a first supervised part has been proposed where data are collected from the person and the environment in both workstations performing a task that allows to quantify the person's concentration on the task. This task consists of reading an entertainment book. The reading of technical or scientific documents, which a priori could be more related to a task to be developed in the person's work, has been discarded, because the reading pace may not be constant due to the complexity of certain parts throughout the document. During this supervised period, the number of lines per minute read by the user is counted. In this way, a range is established in which the person is considered focused on the task. Below that range, the person is considered distracted. The supervised task is necessary to establish the user's concentration level in a quantitative way. The model needs this information to learn in which situations the user is focused on the task. Once the supervised stage is completed, the necessary information is available to generate the new model, always based on the previously established definition of concentration. The second part of the experimentation corresponds to the unsupervised collection of new data in both workstations. The user performs the usual tasks of his job. The new information collected will be used to estimate the concentration levels of the person in both workstations using the generated model with the supervised data. For the supervised stage, a data acquisition period of 180 min has been established for each of the established positions (A and B), i.e., a total of 6 h. To make the supervised experimentation more user-friendly, the time has been divided into three one-hour periods, taken on consecutive days. Subsequently, for the unsupervised stage, a total of 8 h of data have been collected. For each of the positions, 4 h have been taken. In turn, these 4 h have been captured in 2-h periods on consecutive days. The next step is to analyze the available signals and decide which data can provide relevant information to the model.