Long short term memory based model for abnormal behavior prediction in elderly persons

Abstract

Smart home refers to the independency and comfort that are ensured by remote monitoring and assistive services. Assisting an elderly person requires identifying and accurately predicting his/her normal and abnormal behaviors. Abnormal behaviors observed during the completion of 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 propose a method, based on long short-term memory recurrent neural networks (LSTM), to automatically predicting an elderly person’s abnormal behaviors. Our method allows to model the temporal information expressed in the long sequences collected over time. Our study aims to evaluate the performance of LSTM on identifying and predicting elderly persons abnormal behaviors in smart homes. We experimentally demonstrated, through extensive experiments using a dataset, the suitability and performance of the proposed method in predicting abnormal behaviors with high accuracy. We also demonstrated the superiority of the proposed method compared to the existing state-of-the-art methods.