## 2 Related Work  
The concept of leveraging the human body as a communication medium was pioneered by [PERSON] [4], who demonstrated the feasibility of capacitive coupling for low-power data transmission through the body. Early work focused on basic signal transmission, but recent studies have explored IBC's potential for biometric identification [22]. In [23], several parameters of IBC were measured using body channel response (BCR) as a unique identifier, noting that variations in body composition (e.g., fat-to-muscle ratio, hydration levels) induce distinct signal attenuation patterns. But practical implementations were limited by simplistic feature extraction methods. Early studies like [22] modeled IBC intra-palm propagation signals as a function of frequency (10-100 MHz) and electrode placement, identifying optimal operating frequencies for minimal loss. Recent research has linked channel gain dynamics to biometric identity. Advancements in machine learning have enabled more sophisticated biometric frameworks. In [20], machine learning algorithms were introduced to identify users based on IBC channel characteristics, achieving 98,5% accuracy using the Naive Bayes algorithm. K-nearest neighbors yielded the second-best results, with identification accuracy of 97.8%. It provides good results with the simplest model and least computational power budget. Their dataset, however, relied on controlled laboratory settings, limiting generalizability. Research in the field of Intrabody Communication is often focused on using data obtained from artificial phantoms [20; 24; 25; 26; 27] rather than actual human subjects. This limits researchers' ability to account for individual biometric characteristics and geometric variations that can significantly influence the performance of IBC channels. The use of phantoms may not fully capture the real-world conditions of communication, highlighting the need for more comprehensive studies based on data from real users to ensure the reliability and accuracy of IBC technologies. Despite progress, these studies largely overlooked the impact of dynamic channel conditions (e.g., motion artifacts, environmental interference) on biometric reliability. Comparative studies have also explored modulation schemes for IBC-based biometrics. For example, in [28], the combination of Ultra-Wide-Band IBC showed that offers superior resilience to motion artifacts but at the cost of higher power consumption. These findings underscore the trade-offs between biometric accuracy and energy efficiency in WBAN design. The absence of standardized IBC datasets has hindered reproducibility in biometric research. WBAN architectures for IBC biometrics must reconcile security, latency, and energy constraints. The effectiveness and increased demand of WBAN forced the development of communication standard IEEE 802.15.6 [29] to bring low-power sensor devices and protocols under one umbrella for seamless communication and to expand the range of applications. In [30], it was proposed a tiered WBAN framework with centralized hubs for data aggregation, but their design prioritized throughput over security. However, their work did not address biometric-specific challenges like template protection. The integration of Intrabody Communication in Wireless Body Area Networks is gaining traction, particularly in the context of Industry 4.0, where the convergence of physical and digital systems is creating opportunities for innovative solutions across various domains. IBC utilizes the human body as an effective transmission medium, enabling seamless and secure communication between wearable devices utilized in industrial environments. The existing literature on IBC often neglects a comprehensive examination of how individual biometric characteristics--such as height, weight, body mass index (BMI), and body composition--impact IBC channel characteristics.