Abstract
As the Internet of Healthcare Things (IoHT) concept emerges today, Wireless Body Area Networks (WBAN) constitute one of the most prominent technologies for improving healthcare services. WBANs are made up of tiny devices that can effectively enhance patient quality of life by collecting and monitoring physiological data and sending it to healthcare givers to assess the criticality of a patient and act accordingly. The collected data must be reliable and correct, and represent the real context to facilitate right and prompt decisions by healthcare personnel. Anomaly detection becomes a field of interest to ensure the reliability of collected data by detecting malicious data patterns that result due to various reasons such as sensor faults, error readings and possible malicious activities. Various anomaly detection solutions have been proposed for WBAN. However, existing detection approaches, which are mostly based on statistical and machine learning techniques, become ineffective in dealing with big data streams and novel context anomalous patterns in WBAN. Therefore, this paper proposed a model that employs the correlations that exist in the different physiological data attributes with the ability of the hybrid Convolutional Long Short-Term Memory (ConvLSTM) techniques to detect both simple point anomalies as well as contextual anomalies in the big data stream of WBAN. Experimental evaluations revealed that an average of 98% of F1-measure and 99% accuracy were reported by the proposed model on different subjects of the datasets compared to 64% achieved by both CNN and LSTM separately.