Abnormality Detection and Energy Conservation in Wireless Body Area Networks using Hidden Markov Models: A Review

Recent developments in the wireless body area networking technology helps to obviate some of the issues related to healthcare sector. With continuous health monitoring being necessary for many chronic patients, wearable biomedical sensors and their affiliated technologies are in vogue recently. Since the sensor nodes are energy constrain device, the energy conservation is critical in wireless body area networks (WBANs).The energy conservation through data reduction approaches in WBANs are comparatively less explored area in which probabilistic models anomaly detection are becoming inevitable for the prevention of serious health problems. In this paper hidden markov models (HMMs) used in wireless body area networks for anomaly detection in different applications are reviewed and how the energy conservation of WBAN devices can be done using the HMM models are discussed. Brief explanation for hidden markov models and the fundamental problems that characterize the model are also explained.