A fusion of a deep neural network and a hidden Markov model to recognize the multiclass abnormal behavior of elderly people

Abstract

With a rapidly aging population, the health problems of older individuals have attracted increasing attention. Elderly people are exposed to more health risks, and their behavior can often indicate signs of crises and diseases. This paper proposes a method for multiclass abnormal behavior recognition based on the integration of sensors (accelerometer, gyroscope, and orientation sensor) in smartphones with positioning monitoring systems. An attention-convolutional neural network (CNN)-long short-term memory (LSTM) algorithm is introduced for human action recognition, which can handle time-dependent data with multiple features of varying importance. Furthermore, based on the long-term activity data (action and position) of the human body, a hidden Markov model (HMM) of the individual’s daily behavior activity state is constructed. Experimental results show that compared with the existing approaches, the proposed attention-CNN-LSTM algorithm performs better in recognizing different human behaviors, with 94.2% precision, 95.1% recall, and F1-score of 94.6%. The developed daily behavior HMM for an individual has been proven to be able to detect changes in human behavioral patterns and indicate the specific behaviors that have changed. The method proposed in this paper can provide a powerful technical guarantee for the health and care of elderly individuals at home.