State Estimation via Communication for Monitoring
Masoumeh Heidari Kapourchali, Bonny Banerjee · IEEE Transactions on Emerging Topics in Computational Intelligence · 2019
Monitoring using sensors is ubiquitous in our environment. In this paper, a state estimation model is proposed for continuous activity monitoring from multimodal and heterogenous sensor data. Each sensor is modeled as an independent agent in the predictive coding framework. It can sample its environment, communicate with other agents, and adapt its internal model to its environment in an unsupervised manner. Using controlled experiments, we show that limitations of each sensor, such as inference inaccuracy and delay, can be overcome through communication with the other sensors. The model is tested for human action recognition on the UTD-MHAD and MHEALTH datasets and for gait freeze recognition from wearable acceleration sensors in Parkinson's disease patients. In both cases, the proposed model yields results comparable to the state-of-the-art. To the best of our knowledge, this is the first work taking advantage of opportunistic communication by a predictive coding agent for estimating the state of its environment.