Why the study?
Faulty sensor data in wireless body area network remote health monitoring can be critical, making faulty sensor detection necessary.
An ANN-based framework can accurately detect faulty sensor data in wearable cardiac monitoring systems, potentially improving the reliability of remote health monitoring.
ANN framework for sensor fault detection in cardiac monitoring warrants caution before use; leaves open prospective validation.
Remote health monitoring framework using wireless body area network with ubiquitous support is gaining popularity. However, faulty sensor data may prove to be critical. Hence, faulty sensor detection is necessary in sensor-based health monitoring. In this paper, an artificial neural network (ANN)-based framework for learning about health condition of patients as well as fault detection in the sensors is proposed. This experiment is done based on human cardiac condition monitoring setup. Related physiological parameters have been collected using wearable sensors from different people. These data are then analyzed using ANN for health condition identification and faulty node detection. Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2 sensors such as ECG sensor, pulse oximeter sensor, and body temperature sensor have been used for data collection and ARDINO UNO R3 as microcontroller device. ANN method detects faulty sensor data with classification accuracy of 98%. Experimental results and analyses are given to prove the claim.
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Karmakar et al. (2021) studied this question.
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