Wireless Body Area Network Monitoring Research Articles

Real-time health monitoring in WBANs using hybrid Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP)

Wireless body area networks (WBANs) are helpful for remote health monitoring, especially during the COVID-19 pandemic. Due to the limited batteries of bio-sensors, energy-efficient routing is vital to achieve load-balancing and prolong the network's lifetime. Although many routing techniques have been presented for WBANs, they were designed for an application, and their performance may be degraded in other applications. In this paper, an ensemble Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP) is introduced as an adaptive real-time remote health monitoring in WBANs. The motivation behind this technique is to utilize the superior route optimization solutions offered by metaheuristics and to integrate them with the real-time routing capability of machine learning. The proposed method involves two phases: offline model tuning and online routing. During the offline pre-processing step, a metaheuristic algorithm based on the whale optimization algorithm (WOA) is used to optimize routes across various WBAN configurations. By applying WOA for multiple WBANs, a comprehensive dataset is generated. This dataset is then used to train and test a machine learning regressor that is based on support vector regression (SVR). Next, the optimized MDML-RP model is applied as an adaptive real-time protocol, which can efficiently respond to just-in-time requests in new, previously unseen WBANs. Simulation results in various WBANs demonstrate the superiority of the MDML-RP model in terms of application-specific performance measures when compared with the existing heuristic, metaheuristic, and machine learning protocols. The findings indicate that the proposed MDML-RP model achieves noteworthy improvement rates across various performance metrics when compared to the existing techniques, with an average improvement of 42.3% for the network lifetime, 15.4% for reliability, 31.3% for path loss, and 31.7% for hot-spot temperature.

Wireless body-area network monitoring with ZigBee, 5G and 5G with MIMO for outdoor environments

Wireless body area network (WBAN) works near or inside the human body and is characterized by its accuracy in sending and receiving data. It works for long hours and must be low in energy consumption. One of its problems is that the transmission and reception distance is few and does not exceed a few meters. We worked on simulations based on a network consisting of ZigBee or fifth-generation (5G) and 5G with multiple-input and multiple-output (MIMO) nodes to deliver information to the center (hospital) at a 2.4 to 2.8 or 5 GHz frequency to solve this problem. Suppose the sensors are connected to the Arduino, which in turn is connected to the transmitter connection. The proposed method transmits data obtained from the sensor that touched the patient by multi-node to the hospital. The suggested method shows the best scenario to reduce energy consumption based on the number of active nodes. Based on the results obtained, we have noticed that ZigBee devices reduce energy use, perform better, and significantly extend the life of the nodes. While 5G devices increased the response speed in transferring data. In addition, MIMO antennas have the advantage of adding more stability in the connection between nodes.

An Experimental Performance Evaluation and Compatibility Study of the Bluetooth Low Energy Based Platform for ECG Monitoring in WBANs

A long term healthcare monitoring system requires battery operated devices with low-power technologies. Researchers tried to adapt various short-range technologies for Wireless Body Area Networks (WBANs) in ubiquitous health monitoring. The classical Bluetooth is known for its greedy power consumption, IrDA and NFC require line-of-sight conditions, and ANT has weak coexistence features and interference issues. A typical choice remains ZigBee/6LoWPAN over IEEE 802.15.4 based solutions in WBANs because of their low-power consumption. However, the recently proposed Bluetooth Low Energy (BLE) announced more compelling features in various aspects. Only few studies have been published supporting these claims on BLE. In this paper, we present a BLE based remote healthcare monitoring platform and we study its compatibility for ECG monitoring. ECG data requires continuous and real-time transmissions, making it particularly challenging for resource constrained devices. In our system, a BLE112 module from Bluegiga and a BLE USB dongle are used for WBAN. The performance of the system is evaluated experimentally and the results showed good potential of this proposed BLE platform in meeting the main QoS requirements of medical applications in terms of throughput, end-to-end delay, and packet error rate, while staying energy efficient.

Model based compressed sensing reconstruction algorithms for ECG telemonitoring in WBANs

Wireless Body area networks (WBANs) consist of sensors that continuously monitor and transmit real time vital signals to a nearby coordinator and then to a remote terminal via the Internet. One of the most important signals for monitoring in WBANs is the electrocardiography (ECG) signal. The design of an accurate and energy efficient ECG telemonitoring system can be achieved by: i) reducing the amount of data that should be transmitted ii) minimizing the computational operations executed at any transmitter/receiver in a WBAN. To this end, compressed sensing (CS) approaches can offer a viable solution. In this paper, we propose two novel CS based ECG reconstruction algorithms that minimize the samples that are required to be transmitted for an accurate reconstruction, by exploiting the block structure of the ECG in the time domain (TD) and in an uncorrelated domain (UD). The proposed schemes require the solutions of second-order cone programming (SOCP) problems that are usually tackled by computational demanding interior point (IP) methods. To solve these problems efficiently, we develop a path-wise coordinate descent based scheme. The reconstruction accuracy is evaluated by the percentage root-mean-square difference (PRD) metric. A reconstructed signal is acceptable if and only if PRD<9%. Simulation studies carried out with real electrocardiographic (ECG) data, show that the proposed schemes, operating in both the TD and in the UD as compared to the conventional CS techniques, reduce the Compression Ratio (CR) by 20% and 44% respectively, offering at the same time significantly low computational complexity.