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Article

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models †

1
Department of Computer Science, USTO-MB University, Oran 31000, Algeria
2
LICEF Research Institute, Department of Science and Technology, TELUQ University, Montreal, QC 11290, Canada
*
Authors to whom correspondence should be addressed.
This paper is an extended version Long Short Term Memory Based Model for Abnormal Behavior Prediction in Elderly Persons, In Proceeding of the 17th International Conference on Smart Living and Public Health (ICOST), New York, NY, USA, 14–16 October 2019.
Sensors 2020, 20(8), 2359; https://doi.org/10.3390/s20082359
Received: 15 February 2020 / Revised: 13 April 2020 / Accepted: 16 April 2020 / Published: 21 April 2020
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. View Full-Text
Keywords: smart home; activity daily life (ADL); LSTM; CNN; autoencoder; abnormality detection smart home; activity daily life (ADL); LSTM; CNN; autoencoder; abnormality detection
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MDPI and ACS Style

Zerkouk, M.; Chikhaoui, B. Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models. Sensors 2020, 20, 2359. https://doi.org/10.3390/s20082359

AMA Style

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

Chicago/Turabian Style

Zerkouk, Meriem, and Belkacem Chikhaoui. 2020. "Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models" Sensors 20, no. 8: 2359. https://doi.org/10.3390/s20082359

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