  This work proposes a way to detect the 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, especially 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
