A Five-Tissue-Layer Human Body Communication Circuit Model Tunable to Individual Characteristics

Mao, Jingna; Yang, Huazhong; Lian, Yong; Zhao, Bo

doi:10.1109/tbcas.2018.2798410

Cited by 28 publications

(18 citation statements)

References 26 publications

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“…In [22], the arm is modeled as cuboid. However, in most of the cases, the arm is modeled as cylinder [9,28]. Since the shape of the arm is more similar to a cylinder than a cuboid in reality, we use cylinder to model the arm.…”

Section: S-parameter Model In Cstmentioning

confidence: 99%

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Investigation and Modeling of Multi-Node Body Channel Wireless Power Transfer

Huang

Zhao

Sun

et al. 2019

Sensors

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Insufficient power supply is a huge challenge for wireless body area network (WBAN). Body channel wireless power transfer (BC-WPT) is promising to realize multi-node high-efficiency power transmission for miniaturized WBAN nodes. However, the behavior of BC-WPT, especially in the multi-node scenario, is still lacking in research. In this paper, the inter-degeneration mechanism of a multi-node BC-WPT is investigated based on the intuitive analysis of the existing circuit model. Co-simulation in the Computer Simulation Technology (CST) and Cadence platform and experiments in a general indoor environment verify this mechanism. Three key factors, including the distance between the source and the harvester, frequency of the source, and area of the ground electrodes, are taken into consideration, resulting in 15 representative cases for simulation and experiments studies. Based on the simulation parameters, an empirical circuit model to accurately predict the received power of multiple harvesters is established, which fits well with the measurement results, and can further provide guidelines for designs and research on multi-node BC-WPT systems.

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Section: S-parameter Model In Cstmentioning

confidence: 99%

“…Since the shape of the arm is more similar to a cylinder than a cuboid in reality, we use cylinder to model the arm. The thickness of each tissue layer is chosen in Table 1 according to [28]. The S-parameter of the body channel can be obtained by simulation in CST and construct the S-parameter model.…”

Section: S-parameter Model In Cstmentioning

confidence: 99%

Investigation and Modeling of Multi-Node Body Channel Wireless Power Transfer

Huang

Zhao

Sun

et al. 2019

Sensors

Self Cite

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“…The galvanic coupling technique has better adaptability and stability than other types of IBC because the information transmission process occurs completely on the human body and is not easy affected by external influences [8]. This coupling type has a body surface in vivo communication ability, making it an important branch of IBC [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Design of Galvanic Coupling Intra-Body Communication Transceiver Using Direct Sequence Spread Spectrum Technology

et al. 2020

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Intra-body communication (IBC) uses the human body as the transmission medium for electrical signals, and it features the following advantages: low power consumption, strong anti-interference ability, high data security, and broad application scenarios. However, some technical issues still need to be addresses, such as the choice of the best modulation and demodulation scheme in different application scenarios, influence of human activity on IBC performance, variable signal-to-noise ratio (SNR), and influence of transmission distance change on different modulation and demodulation methods. This paper adopts direct sequence spread spectrum (DSSS) communication and phase modulation to realize DSSS-differential phase shift keying (DPSK) and DPSK modulation transmission of baseband data. Moreover, the Costas loop method is employed to achieve reliable symbol recovery. Under the same conditions, in vivo experiments were conducted to compare the performance of DSSS-DPSK and DPSK galvanic coupling IBC transceivers. Notably, these transceivers are affected by the changes in SNR, transmission distance, and human activities. Results show that the bit error rate (BER) of the DPSK scheme is 40 times larger than the DSSS-DPSK scheme in a 30 cm channel length and different SNR experiments. When the BER performance changes from extremely poor (1.40 × 10 −1) to excellent (1.51 × 10 −6), the SNR of DSSS-DPSK scheme only needs to be improved by 16 dB. In contrast, when the BER performance changes from extremely poor (1.54×10 −1) to good (1.65 × 10 −5), the SNR of DPSK scheme needs to be improved by 25 dB. With a SNR of −5 dB, the BER ratios of the DPSK scheme is 7 times larger than the DSSS-DPSK scheme. Also, DSSS-DPSK scheme is more sensitive to changes in motion status than DPSK.

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“…The first challenge facing IBC systems is understanding and modeling the human body channel. Early research [2][3][4][5][6][7][8][9][10] focused on measuring the gain/attenuation profile for IBC, over the range of frequencies where the new application is believed to have the most efficient channel performance, which is from 100 kHz to 100 MHz, below which the periodic exposure to EM signals at certain power levels might have harmful health effects, and above 100 MHz the body antenna effects kicks in, leading to more transmitted power being leaked into the surrounding environment, rather than confined to the body.…”

Section: Introductionmentioning

confidence: 99%

Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning

Khorshid

Alquaydheb

Kurdahi

et al. 2020

Sensors

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In this paper, we propose and validate using the Intra-body communications channel as a biometric identity. Combining experimental measurements collected from five subjects and two multi-layer tissue mimicking materials’ phantoms, different machine learning algorithms were used and compared to test and validate using the channel characteristics and features as a biometric identity for subject identification. An accuracy of 98.5% was achieved, together with a precision and recall of 0.984 and 0.984, respectively, when testing the models against subject identification over results collected from the total samples. Using a simple and portable setup, this work shows the feasibility, reliability, and accuracy of the proposed biometric identity, which allows for continuous identification and verification.

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A Five-Tissue-Layer Human Body Communication Circuit Model Tunable to Individual Characteristics

Cited by 28 publications

References 26 publications

Investigation and Modeling of Multi-Node Body Channel Wireless Power Transfer

Investigation and Modeling of Multi-Node Body Channel Wireless Power Transfer

Design of Galvanic Coupling Intra-Body Communication Transceiver Using Direct Sequence Spread Spectrum Technology

Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning

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