Convolutional Neural Network for Elderly Wandering Prediction in Indoor Scenarios

TL;DR

This paper presents a CNN-based approach to predict wandering behavior of Alzheimer's patients indoors, using a manually created and augmented image dataset from sensor path data, achieving promising classification metrics.

Contribution

It introduces a novel method of transforming indoor sensor path data into images for CNN-based wandering activity detection in elderly care.

Findings

F1 score of 75% on validation data

Achieved 100% precision in predictions

Developed a new dataset of 220 paths for this task

Abstract

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