Communication system design for magnetic induction-based Wireless Body Area Network

Golestani, Negar; Moghaddam, Mahta

doi:10.1109/usnc-ursi.2017.8074891

“…The MI-based communication system is a short-range wireless physical layer that transmits signals by coupling non-propagating magnetic field between the wire coils rather than radiating as conventional methods. The main component of each node is a coil, which is lightweight, portable, inexpensive, simple, and can be worn as accessories such as belts, wristbands, and jewelry 33,40 . The manufacturing cost of an MI module is approximately less than $20, while a Bluetooth IMU costs more than $100 (refs.…”

Section: Resultsmentioning

confidence: 99%

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

Golestani

¹

,

Moghaddam

²

2020

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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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“…The MI-based communication system is a short-range wireless physical layer that transmits signals by inductive coupling between the wire coils rather than radiating as is done in conventional methods [27,28]. The transmitter node uses a coil to produce an oscillating magnetic field at a specific frequency.…”

Section: Operating Principlementioning

confidence: 99%

Wearable Magnetic Induction-based Approach Toward 3D Motion Tracking

Golestani

¹

,

Moghaddam

²

2021

Preprint

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0

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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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Wearable magnetic induction-based approach toward 3D motion tracking

Golestani

¹

,

Moghaddam

²

2021

Sci Rep

3

0

View full text Add to dashboard Cite

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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Communication system design for magnetic induction-based Wireless Body Area Network

Cited by 6 publications

References 2 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

Wearable Magnetic Induction-based Approach Toward 3D Motion Tracking

Wearable magnetic induction-based approach toward 3D motion tracking

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