A New Deep Hierarchical Neural Network Applied in Human Activity Recognition (HAR) Using Wearable Sensors

Abstract

Human Activity Recognition (HAR) using wearable sensors is becoming more practical in the field of security and health care monitoring. Deep and Machine learning techniques have been widely used in this area. Since smartphones and their applications are the parts of daily life, they can be very helpful in data gathering and online learning in HAR problems. In this paper, a new hierarchical neural network structure with the hierarchical learning method is presented and applied to the HAR. The proposed model is a deep learner neural network without using heavy computations that CNN-based deep learners usually suffer from. This makes the suggested model suitable for being embedded in agent and multi-agent based solutions and online learning, especially when they are implemented in small devices such as smartphones. In addition the hidden layer of the first section of the proposed model benefits automatic nonlinear feature extraction. The extracted features are proper for classifications. Handling the dimension of data is one of the challenges in HAR problem. In our model, data dimension reduction is automatically performed in the hidden layers of the different network sections. According to the empirical results, our proposed model yields better performance on the Opportunity data sets, compared to the similar ML algorithms.