### Wireless Body Area Networks  
One approach to acquire the data necessary for the AI models is the use of so-called Wireless Body Area Networks (WBANs), a near-body, low-power, and short-range wireless communication device to aggregate health, sport, and well-being information. These have already been defined in 2012 in the IEEE Standard for Local and metropolitan area networks Part 15.6: Wireless Body Area Networks [6]. However, the standard is set to be deactivated at the end of March 2023, but the general approach is being further developed and enhanced through several standardization approaches, such as the SmartBAN Technical Committee established by the European Telecommunications Standards Institute (ETSI) in 2013 [7, 8], which not only considers wireless communication on but also in the body, with the associated range of medical and health improvements, personal safety, and wellbeing in sport as leisure applications. Furthermore, initiatives such as the one2M2 standard approach, with the visions 'A world of interoperable and secure IoT services...', are addressing issues of health-related Internet of Things (IoT) structures and security [9]. The work described here is based on the WBAN approach, but in contrast to the common star network topology of the deprecated 802.15.6 standard, or that considered in SmartBAN [7] approaches, it proposes the concept of a ring topology that concatenates information and thus distributes computational load while increasing the data transmission rate. Furthermore, adding an additional Human Body Communication (HBC) channel to the system is proposed. This technology utilizes the conductivity of human skin as a complementary communication medium, thereby opening up additional options in terms of security and privacy of the system. The HBC approach plays a crucial role in introducing new sensors into the network and exchanging security information, such as public keys. The HBC also becomes relevant whereas adding additional sensors, as described in Section 2. Based on this described protocol, the security aspect of device authentication is especially evaluated, and therefore ordinary attack vectors such as spoofing or man-in-the-middle attacks can be excluded from the outlet because of the ground-based integrity checking of health-related systems. Authentication and AI-related attack vectors are shown, and countermeasures are presented. By focusing on encryption techniques and threat-mitigation strategies, an analysis of challenges and potential solutions in securing medical data in WBANs is provided. Similar to the AI Act and the high-risk classification of human information, specific requirements also apply to this type of data concerning privacy and security. To take this into account, in addition to standard encryption techniques such as symmetric and asymmetric methods, homomorphic encryption algorithms are considered, particularly, the ability of distributed- and federated learning and operating on encrypted data. The healthcare sector is a critical infrastructure, so special attention must be paid to protecting the systems. As part of (cyber) resilience strategies, methods for detecting, mitigating, and preventing attacks are particularly relevant here, as are active protective measures to make it as difficult as possible for potential attackers [10].