CNN-LSTM-Based Late Sensor Fusion for Human Activity Recognition in Big Data Networks

The technological advancement in sensor technology and pervasive computing has brought smart devices into our daily life. Due to the continuous connectivity of the internet with our everyday devices, researchers can deploy IoT sensors to health care and other applications, such as human activity recognition. Most of the state-of-the-art sensor-based human activity recognition systems can detect basic activities (such as standing, sitting, and walking), but they cannot accurately distinguish similar activities (ascending stairs or descending stairs). Such systems are not efficient for critical healthcare applications having complex activity sets. This paper proposes two sensor fusion approaches, i.e., position-based early and late sensor fusion using convolutional neural network (CNN) and convolutional long-short-term memory (CNN-LSTM). The performance of our proposed models is evaluated on two publicly available datasets. We also evaluated the effect of different normalization techniques on recognition accuracy. Our results show that the CNN-LSTM-based late sensor fusion model also improves the recognition accuracy of similar activities.