A dynamic Bayesian network approach for device free radio vision: Modeling, learning and inference for body motion recognition
In this paper, a time varying dynamic Bayesian network model is shown to describe human induced RF fluctuations for the purpose of non cooperative and device free radiobased body motion recognition (radio vision) The technology relies on pre existing wireless communication network infrastructures and processes channel quality information (CQI) for human scale sensing. Body movements leave a characteristic footprint on the CQI sequences collected during consecutive radio transmissions over multiple co located links. Body induced RF footprints are proved to be effectively characterized by temporarily coupled hidden Markov chains: abrupt changes of body postures make CQIs observed over co located links temporarily coupled while being uncoupled for slow body movements. Learning and classification/inference problems are discussed based on experimental measurements. Device free radio vision performances are evaluated for arm gesture and fall detection applications.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)