Convolutional Neural Network for Elderly Wandering Prediction in Indoor Scenarios
This work aims to provide an early warning system for caregivers of Alzheimer's patients, which is an incremental improvement to existing monitoring methods.
This paper addresses the problem of predicting wandering activity in Alzheimer's patients using indoor sensor path data. The authors developed a Convolutional Neural Network model that achieved an f1 score of 75%, recall of 60%, and precision of 100% on a 10-sample validation set.
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