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.

Highlights

  • The accelerated development of the Internet of Things (IoT) has attracted attention from stakeholders all over the world due to the combination of the physical world with the virtual world through the Internet for communication and data sharing

  • The standard deviation of the MAE acts as a dynamic threshold based on the studies in [7,42]

  • The value of the dynamic threshold is different from one sensor to another

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Summary

Introduction

The accelerated development of the Internet of Things (IoT) has attracted attention from stakeholders all over the world due to the combination of the physical world with the virtual world through the Internet for communication and data sharing. Despite their success, more research is needed to promote their improvement concerning the speed of detection, type of anomalies, the correlation that exists in the collected attributes, and dealing with big data In this light, this paper proposes an anomaly detection model that exploits the correlation that exists in measured attributes of WBAN sensors and uses the hybrid ConvLSTM deep learning technique. This paper proposes an anomaly detection model that exploits the correlation that exists in measured attributes of WBAN sensors and uses the hybrid ConvLSTM deep learning technique This model aims to detect anomalous data in WBAN and consider the requirements of the learning process to identify anomalous behavior and provide a reliable system against sensor faults and anomalous activities with an understanding of the factors that impact patients and healthcare organizations.

Related Works
Proposed Model
Data Collection and Pre-Processing Phase
Data Collection and Pre‐Processing Phase
Sensors
Detection
Long Short‐Term Memory (LSTM)
Convolutional LSTM (Conv-LSTM)
Point Anomaly Detection
Evaluation
Evaluation Phase
Model Setup
Point Anomaly Detection Results and Analysis
Contextual Anomaly Detection Result and Analysis
Comparison with Existing Deep Learning and Machine Learning Techniques
Conclusions and Future Work
Full Text
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