Neuroplex: Learning to detect complex events in sensor networks through knowledge injection

Abstract

Despite the remarkable success in a broad set of sensing applications, state-of-the-art deep learning techniques struggle with complex reasoning tasks across a distributed set of sensors. Unlike recognizing transient complex activities (e.g., human activities such as walking or running) from a single sensor, detecting more complex events with larger spatial and temporal dependencies across multiple sensors is extremely difficult, e.g., utilizing a hospital's sensor network to detect whether a nurse is following a sanitary protocol as they traverse from patient to patient. Training a more complicated model requires a larger amount of data-which is unrealistic considering complex events rarely happen in nature. Moreover, neural networks struggle with reasoning about serial, aperiodic events separated by large quantities in the spatial-temporal dimensions. We propose Neuroplex, a neural-symbolic framework that learns to perform complex reasoning on raw sensory data with the help of high-level, injected human knowledge. Neuroplex decomposes the entire complex learning space into explicit perception and reasoning layers, i.e., by maintaining neural networks to perform low-level perception tasks and neurally reconstructed reasoning models to perform high-level, explainable reasoning. After training the neurally reconstructed reasoning model using human knowledge, Neuroplex allows effective end-to-end training of perception models with an additional semantic loss using only sparse, high-level annotations. Our experiments and evaluation show that Neuroplex is capable of learning to efficiently and effectively detect complex events-which cannot be handled by state-of-the-art neural network models. During the training, Neuroplex not only reduces data annotation requirements by 100x, but also significantly speeds up the learning process for complex event detection by 4x.