ADELA: attention based deep ensemble learning for activity recognition in smart collaborative environments
Abstract: Human activity recognition (HAR) is a key challenge in pervasive computing and its solutions have been presented based on various disciplines. For HAR in a smart environment without privacy and accessibility issues, data streams generated by environmental sensors are leveraged. In this paper, we propose ADELA, a novel activity recognition scheme in a smart collaborative environment where a group of users performs an activity without user identification. ADELA calculates the importance of events from sensor data streams depending on their impact on recognizing activities and finds the best activity recognition base models using attention-based ensemble learning. After the training phase, each base model obtains its weight through weighted majority calculation, and ADELA stores the information to Trained Model Storage to reuse it for inferring. We evaluate ADELA using the data collected from our testbed and CASAS dataset where users perform their tasks daily and validate the effectiveness of ADELA in a real environment. Experiment results show that the proposed scheme performs higher recognition performance than existing approaches.
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