Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Model

Smart devices, such as smartphones, smartwatches, etc., are examples of promising platforms for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities on these platforms due to interclass pattern similarities, which occur when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on traditional sensors, such as accelerometers and gyroscopes, which are built-in in these devices. Therefore, apart from using information from the traditional sensors, these systems lack the contextual information to support automatic activity recognition. In this article, we explore environmental contexts, such as illumination (light conditions) and noise level, to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) learning models. The models performed sensor fusion by augmenting low-level sensor signals with rich contextual data to improve the models’ recognition accuracy and generalization. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used triaxial inertial sensing signals to train baseline models, while the second set of experiments combined the inertial signals with contextual information from environmental sensors. The obtained results demonstrate that contextual information, such as environmental noise level and light conditions using hybrid deep learning models, achieved better recognition accuracy than the traditional baseline activity recognition models without contextual information.