Modeling and Analysis of Intrabody Communication for Biometric Identity in Wireless Body Area Networks
Abstract
:1. Introduction and Motivation
- Our research presents a study that integrates biometric identification techniques into the IBC system, utilizing channel gain influenced by individual physical characteristics (such as height, weight, and body composition) to enhance user identification and authentication from an experimental dataset containing data about real subjects. By using the KNN algorithm, we achieved an accuracy of 99.9%, demonstrating the potential for IBC to serve as reliable biometric identifiers.
- We proposed a novel IBC WBAN architecture that incorporates biometric identification functionalities, enhancing the performance and security of existing systems. This architectural advancement provides a framework for the development of future IBC-enabled devices and applications focused on personalized user experiences.
- By exploring the synergy between IBC technologies and biometric identification, our study addresses significant challenges in the realms of security and energy efficiency relevant to Industry 4.0. Our findings suggest pathways for the deployment of energy-efficient, secure WBANs within industrial settings, highlighting opportunities for health monitoring and workforce management.
- This analysis lays the groundwork for future studies assessing the applicability of IBC in diverse contexts.
2. Related Work
3. Materials and Methods
3.1. IBC
- Security: IBC provides a secure and private communication network, offering natural security and interference-resistant communication [32]. IBC requires a significantly lower operating frequency compared to RF systems. This means that signals are limited to human proximity, so data reading requires body contact [33].
- Frequency reuse: IBC forms a near-field communication network inside and around the human body. Consequently, it allows for the reuse of the same frequency band by other WBAN users.
3.2. IBC Identification
3.3. Signal Loss Measurement in IBC Channel
4. Data Analysis and IBC Channel Gain Characteristics
5. Modeling of IBC Identification
- subject_id—unique subject identifiers.
- distance—distance between the transmission and reception points.
- rx_gain_50—receiver gain using a 50 Ω load resistance.
- rx_gain_1M—receiver gain using a 1 mΩ load resistance.
Listing 1. Python code to calculate distance matrix. |
def dist_one_loop(M_random): dist = np.zeros ( (M_random.shape[0],M.shape[0]) ) for i, m_random in enumerate(M_random): m_len = M – m_random dist[i, :] = np.sqrt( (m_len ** 2).sum(axis=1) ) return dist dist = dist_one_loop(M_random) print(dist) |
6. IBC WBAN Architecture with Identification Functionality
- 1.
- Implantable Devices and Signal PropagationMiniaturized implants embedded within the body generate low-power electrical signals using galvanic coupling, where the human body acts as a conductive medium. Signal propagation is modulated by the individual’s unique biological parameters, including body composition, geometric dimensions [25], and tissue dielectric properties. These characteristics impart distinct channel gain and attenuation profiles to the transmitted signals.
- 2.
- IBC TransceiverThe IBC transceiver employs separate coupling to establish dual communication pathways: one for signal transmission through the body and another for environmental coupling. This design minimizes cross-interference between internal and external communication channels. Simultaneously, it generates coupling to stabilize signal transmission across dynamic physiological conditions (e.g., movement, hydration changes).
- 3.
- Mobile Transceiver and Biometric ExtractionA wearable or handheld mobile transceiver interface with the IBC transceiver to relay data to external networks. Integrated within this unit is a mobile identification module, which extracts real-time IBC channel characteristics, such as:
- ○
- Receiver gain.
- ○
- Frequency-dependent attenuation profiles.
- ○
- Transmitter–receiver separation distance. (The inclusion of distance as a feature mitigates false identification caused by overlapping gain values at varying transmission distances.)
- 4.
- Biometric Identification WorkflowThe extracted channel characteristics are compared against pre-registered biometric templates stored in a secure database. A machine learning classifier (e.g., K-Nearest Neighbors) analyzes the feature space to authenticate users. Matching thresholds are dynamically adjusted to accommodate minor physiological variations (e.g., weight fluctuations), ensuring robustness without compromising security.
- 5.
- Security and ScalabilityThe architecture leverages the inherent physical boundaries of the human body to restrict signal propagation, reducing eavesdropping risks. Biometric templates are encrypted and stored in a decentralized database, enabling scalable deployment across large populations. The system supports real-time updates to biometric profiles, ensuring adaptability to long-term physiological changes.
- Energy Efficiency: galvanic coupling reduces power consumption by one-to-two orders of magnitude compared to RF-based systems.
- High Accuracy: biometric identification achieves 99.9% accuracy in controlled environments, driven by unique channel gain profiles.
- Interference Resilience: separate coupling pathways isolate intra-body communication from external electromagnetic noise.
7. Discussion
8. Concluding Remarks
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Subject_id | Distance | Rx_gain_50 | Rx_gain_1M |
---|---|---|---|
1256 | 20.6 | −47.771266 | −39.482652 |
1256 | 16.3 | −44.810831 | −36.444843 |
1256 | 13.6 | −44.475018 | −35.952138 |
1256 | 16.3 | −48.714588 | −40.176981 |
1256 | 14.9 | −44.007403 | −35.684326 |
1256 | 12.9 | −42.942264 | −34.625295 |
1256 | 8.6 | −37.340486 | −29.146415 |
1256 | 8 | −38.88304 | −30.088628 |
1256 | 6.7 | −38.236727 | −29.62718 |
1256 | 10.25 | −43.56398 | −34.519372 |
1256 | 5.5 | −37.712044 | −29.149732 |
1256 | 18.6 | −44.371963 | −35.712716 |
1256 | 13 | −42.146326 | −33.774958 |
1256 | 10.3 | −41.808655 | −33.768054 |
1256 | 4.15 | −47.139468 | −39.070489 |
1256 | 11.6 | −41.295583 | −33.238293 |
1686 | 16.3 | −43.848785 | −35.944426 |
1686 | 13.6 | −43.064913 | −35.35695 |
1686 | 16.3 | −45.95929 | −37.969667 |
… | … | … | … |
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Khromov, I.; Voskov, L.; Komarov, M. Modeling and Analysis of Intrabody Communication for Biometric Identity in Wireless Body Area Networks. Appl. Sci. 2025, 15, 4126. https://doi.org/10.3390/app15084126
Khromov I, Voskov L, Komarov M. Modeling and Analysis of Intrabody Communication for Biometric Identity in Wireless Body Area Networks. Applied Sciences. 2025; 15(8):4126. https://doi.org/10.3390/app15084126
Chicago/Turabian StyleKhromov, Igor, Leonid Voskov, and Mikhail Komarov. 2025. "Modeling and Analysis of Intrabody Communication for Biometric Identity in Wireless Body Area Networks" Applied Sciences 15, no. 8: 4126. https://doi.org/10.3390/app15084126
APA StyleKhromov, I., Voskov, L., & Komarov, M. (2025). Modeling and Analysis of Intrabody Communication for Biometric Identity in Wireless Body Area Networks. Applied Sciences, 15(8), 4126. https://doi.org/10.3390/app15084126