Dual Sensor based Wearable Sensor Fault Detection for Reliable Medical Monitoring - DM , Pavithra; Rao, P. Ramchandar (International Journal of Computer Sciences and Engineering, 2019)
As the medical body sensor network (BSN) is usually resource limited and vulnerable to environmental effects and malicious attacks, faulty sensor data arise inevitably which may resultin false alarms, faulty medical diagnosis, and even serious misjudgment. Thus, faulty sensor data should be detected and removed as much as possible before being utilized for medical diagnosis making. Most available works directly employed fault detection schemes developed in traditional wireless sensor network (WSN) for body sensor fault detection. However, BSNs adopt a very limited number of sensors for vital information collection, lacking the information redundancy provided by densely deployed sensor nodesin traditional WSNs. In light of this, a Dual sensor network model based sensor fault detection scheme is proposed in this project, which relies on double sensor data for establishing the conditional probability distribution of body sensor readings, rather than the redundant information collected from a large number of sensors. Furthermore, the Dual sensor network-based scheme enables us to minimize the inaccuracy rate by optimally tuning the threshold for fault detection. Extensive online dataset has been adopted to evaluate the performance of our fault detection scheme, which shows that our scheme possesses a good fault detection accuracy and allow false alarm rate.