Using Latent Knowledge to Improve Real-Time Activity Recognition for Smart IoT

Real-time/online activity recognition (AR) is an important technology in smart Internet of Things (IoT) systems where users are assisted by smart devices in their daily activities. How to generate appropriate feature representation from sensor event streaming is a challenging issue for accurate and efficient real-time AR. Previous AR models that rely on explicit domain knowledge are not appropriate for online recognition of complex human activities. We propose to use unsupervised learning to learn about the latent knowledge and embed the activity probability distribution prediction as high-level features to boost real-time AR performance. The proposed approach first learns the latent knowledge from explicit-activity window sequences using unsupervised learning, and derives the probability distribution prediction of activity classes for a given sliding window. Our approach then feeds the prediction with other basic features of the sliding window into a classifier to produce the final class result on each event-count sliding window. Experiments on five smart home datasets show that the proposed method achieves a higher accuracy by at least 20 percent improvement on F1_score than previous traditional algorithms, while maintaining a lower time cost than deep learning based methods. An analysis on the feature importance shows that the addition of probability distribution prediction about activity classes leads to a promising direction for real-time AR.