Open access research
publication repository

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models [r-libre/1965]

Zerkouk, Meriem, & Chikhaoui, Belkacem (2020). Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models. Sensors, 20 (8), 2359. https://doi.org/10.3390/s20082359

File(s) available for this item:
[img]  PDF - sensors-20-02359-v2.pdf
Content : Published Version
Item Type: Journal Articles
Refereed: Yes
Status: Published
Abstract: The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people. The temporal information and spatial sequences collected over time are used to generate models, which can be fitted to the training data and the fitted model can be used to make a prediction. We present an experimental evaluation of these models performance in identifying and predicting elderly persons abnormal behaviors in smart homes, via extensive testing on two public data sets, taking into account different models architectures and tuning the hyperparameters for each model. The performance evaluation is focused on accuracy measure.
Official URL: https://www.mdpi.com/1424-8220/20/8/2359
Depositor: Chikhaoui, Belkacem
Owner / Manager: Belkacem Chikhaoui
Deposited: 06 May 2020 20:33
Last Modified: 06 May 2020 20:33

Actions (login required)