This proposal consists of adding a new language module named Users to provide multi-user support in NCL, proposing three new NCL elements: userBase, userAgent and userProfile. user-Base is used for declaring user and their profiles. userAgent identifies a user and userProfile represents a description of the user’s profile. Each userAgent is associated with only one userProfile, and together, they allow representing a unique user. The user description with its specific characteristics can be loaded from a profile specified with the Resource Description Framework (RDF) standard, for example.
This work proposes a way to detect the wandering movement of Alzheimer’s patients from path data collected from non-intrusive indoor sensors around the house. Due to the lack of adequate data, we have manually generated a dataset of 220 paths using our developed application. Wandering patterns in the literature are normally identified by visual features (such as loops or random movement), thus our dataset was transformed into images and augmented. Convolutional layers were used on the neural network model since they tend to have good results in finding patterns mainly on images. The Convolutional Neural Network model was trained with the generated data representing the hourly analysis and achieved an F1 score (relation between precision and recall) of 75%, recall of 60%, and precision of 100% on the validation slice. For comparative purposes, we have also trained the model with a 30-min interval of analysis and achieved an F1 score of 57.14%, a recall of 80% and a precision of 44.44%.
This proposal consists of adding one more type of variable storage settings node, called “x-ncl-user-settings,” which stores user information. This information may come from sensors attached to the user, that must update this data periodically. These variables can be used by NCL links or procedural code to drive application behavior based on the perceived user information.
This work to propose a way to detect wandering activity of Alzheimer's patients from path data collected from non-intrusive indoor sensors around the house. Due to the lack of adequate data, we've manually generated a dataset of 220 paths using our own developed application. Wandering patterns in the literature are normally identified by visual features (such as loops or random movement), thus our dataset was transformed into images and augmented. Convolutional layers were used on the neural network model since they tend to have good results finding patterns specially on images. The Convolutional Neural Network model was trained with the generated data and achieved an f1 score (relation between precision and recall) of 75%, recall of 60%, and precision of 100% on our 10 sample validation slice.
This proposal consists of adding one more type of variable storage setting node, called “x-ncl-ambient-settings,” which stores environment information. This information may come from sensors in the physical ambient that must update this data periodically or as defined by the author of the NCL document. These variables can be used by NCL links or procedural code to drive application behavior based on the perceived environment information.
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