Improving the Efficiency of Magnetic Induction-Based Wireless Body Area Network (WBAN)

Golestani, Negar; Moghaddam, Mahta

doi:10.1109/imbioc.2018.8428842

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…MI system setup. The MI transceivers adopted in the experiments consist of a coil and L-reversed impedance matching network 52 . The matching network is used to maximize the transmission efficiency of the overall system 52 .…”

Section: Resultsmentioning

confidence: 99%

“…The MI transceivers adopted in the experiments consist of a coil and L-reversed impedance matching network 52 . The matching network is used to maximize the transmission efficiency of the overall system 52 . The coils are identical, air-cored, single layer copper with 5 cm radius, 10 AWG wire diameter, and the user can wear them as accessories.…”

Section: Resultsmentioning

confidence: 99%

“…6. Coils are attached to impedance matching networks, called input and output matching networks, to maximize the transmission efficiency of the overall system 52 . The closed-form expressions of these circuit parameters are reported in ref.…”

Section: Methodsmentioning

confidence: 99%

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Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks

Golestani

Moghaddam

2020

Nat Commun

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Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challenges inherent in designing a sensor-based activity recognition system operating in and around a lossy medium such as the human body to gain a trade-off among power consumption, cost, computational complexity, and accuracy. We introduce an innovative wireless system based on magnetic induction for human activity recognition to tackle these challenges and constraints. The magnetic induction system is integrated with machine learning techniques to detect a wide range of human motions. This approach is successfully evaluated using synthesized datasets, laboratory measurements, and deep recurrent neural networks.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks

Golestani

Moghaddam

2020

Nat Commun

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“…In [35], the authors demonstrated muscle strain sensor data measurement in a WBAN through prototype hardware development. The authors in [36] illustrated the deployment of magnetic induction based wireless communication systems for WBAN instead of typical electromagnetic wave communication technologies and showed impedance matching can significantly improve the efficiency of magnetic induction based WBAN.…”

Section: Related Workmentioning

confidence: 99%

CognitiveIoT‐Based Health Monitoring Scheme Using Non‐Orthogonal Multiple Access

Rahul

Sabuj

Haider

et al. 2022

Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks

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It has become very essential to address the limited spectrum capacity and their efficient utilization to support the increasing number of Internet of Things devices. When it comes to medical infrastructure, it becomes very imperative for medical devices to communicate with the base station. In such situations, communication over the wireless medium must provide optimized throughput (data rate) with effectual energy usage, which will ensure precise medical feedback by the responsible staff. Taking into account, it is necessary to operate wireless communication precisely at a higher frequency with more substantial bandwidth and low latency. Cognitive Radio (CR) is traditionally a viable choice, where it identifies and utilizes the vacant spectrum, thus maximizing the primary user's capacity and achieving spectral efficiency. To ensure such outcomes, the Non-Orthogonal Multiple Access (NOMA) techniques have proven to deliver an effective solution to the increasing number of devices with unimpaired performance, especially when the communication shifts towards a higher frequency band such as the mmWave band. In this chapter, IoT based CR network in uplink communication is proposed alongside employing NOMA techniques for optimal throughput, and energy efficiency for a medical infrastructure. Numerical results show that effectual throughput and energy efficiency for a High Reliable Communication (HRC) device and Moderate Reliable Communication (MRC) device improve over 83.13% and 73.95%, respectively and their corresponding energy efficacy values show vast improvement (83.11% and 73.96% respectively). Likewise, for interference case both the throughput and the energy efficiency improve approximately over 93% for all devices. physiological data. Thus, alerting the person to take necessary precautions and assist the medical professionals to make sound decisions regarding the appropriate treatment to cure diseases in the right time [3 -5].Due to the huge prospects of WBAN bringing revolutionary changes in telemedicine systems with real deployment of IoT, some different wireless standards have been proposed. IEEE wireless standard 802.15 defined as wireless personal area network (WPAN) is considered as suitable technology to fulfill the purpose of e-health systems [4]. IEEE 802.15 standard has several versions such as 802.15.1 and 802.15.4. These are commonly known as Bluetooth and ZigBee respectively, which are widely used as low power consumed, short ranged and low data transfer enabled wireless technology for infotainment and health care services [6]. Later on, IEEE originated 802.15.6 named as WBAN particularly developed for telemedicine systems with improvement in data reliability, sensor life time, latency and interference [7].In the near future, a large number of sensors will be deployed on the body of the patients through WBANs to manage healthcare services remotely and provide intuitive decisions of medical professionals more dynamically. Therefore, handling big amounts of situationalawareness data in healthcare se...

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“…To improve the system efficiency, we have employed resonant inductive coupling attached to the coil. The tuning circuit can be as simple as a capacitor to tune the frequency or be a Π or T matching circuit to tune the frequency, control Q-factor, and match input and output impedances for higher power transfer [40]. Here, we used a 560 pF capacitor parallel to a trimmable capacitor with the adjustable range of 3-10 pF to tune the circuit to resonance accurately.…”

Section: Hardware Designmentioning

confidence: 99%

Wearable Magnetic Induction-based Approach Toward 3D Motion Tracking

Golestani

Moghaddam

2021

Preprint

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Activity recognition using wearable sensors has gained popularity due to its wide range of applications, including healthcare, rehabilitation, sports, and senior monitoring. Tracking the body movement in 3D space facilitates behavior recognition in different scenarios. Wearable systems have limited battery capacity, and many critical challenges have to be addressed to gain a trade-off among power consumption, computational complexity, minimizing the effects of environmental interference, and achieving higher tracking accuracy. This work presents a motion tracking system based on magnetic induction (MI) to tackle the challenges and limitations inherent in designing a wireless monitoring system. We integrated a realistic prototype of an MI sensor with machine learning techniques and investigated one-sensor and two-sensor configuration setups for motion reconstruction. This approach is successfully evaluated using measured and synthesized datasets generated by the analytical model of the MI system. The system has an average distance root-mean-squared error (RMSE) error of 3 cm compared to the ground-truth real-world measured data with Kinect.

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Improving the Efficiency of Magnetic Induction-Based Wireless Body Area Network (WBAN)

Cited by 9 publications

References 6 publications

Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks

Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks

CognitiveIoT‐Based Health Monitoring Scheme Using Non‐Orthogonal Multiple Access

Wearable Magnetic Induction-based Approach Toward 3D Motion Tracking

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