Abstract
Human Activity Recognition (HAR) based on sensor networks is of paramount importance in the fields of body area networks, ubiquitous and pervasive computing. HAR is widely used in applications such as health monitoring, medical care, smart homes etc. With the advent of sensor networks and the fast-growing waveform data in the technologically developing modern world, the traditional feature engineering methods are becoming more obsolete. Deep Learning methods are very beneficial as they are efficient in feature extraction, helps in modelling the sensor data systematically and improving the performance of complex human activity recognition. Taking advantage of deep learning techniques, we propose a model based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). To this we integrate a special feature recalibration framework based on attention mechanism to perform human activity recognition. The model uses Inception Neural Network architecture with various kernel-based convolution layers to extract spatial features and Gated Recurrent Units (GRU) to model temporal / time-series features. Space and Channel based Squeeze and Excitation blocks (SCbSE) framework is used to recalibrate features to complete classification tasks of complex human activities. The proposed model is experimentally verified on two publicly available benchmark HAR datasets namely: Smartphone Dataset and Opportunity dataset. The performance of the model is analysed while comparing to that of the state-of-the-arts.
