The OmniConnect project developed a low‑power, passive identification and localisation system that turns ordinary household objects into Internet‑of‑Things (IoT) devices without the need for batteries or wired power. The core technology is a 60‑GHz secondary radar system that interrogates miniature tags measuring roughly 1 cm². These tags are embedded in textiles or attached to objects and respond by altering a controllable load resistance, allowing the radar to recover both a unique identifier and a position estimate. Experimental results show that the system can locate tagged items with an accuracy of about 5 cm in typical indoor environments, a precision sufficient for many domestic assistance scenarios. The tags’ small size permits seamless integration into clothing, furniture, or everyday objects, enabling continuous monitoring without user intervention.

The project defined a set of use cases that guided the technical design. First, the system can report the current location of a tagged object, such as a key, by providing a spoken answer (“The key is visible in the living room on the dresser”) through a Raspberry Pi‑based acoustic interface that uses speech‑synthesis software integrated into the IDEAAL platform. Second, it can track the position of a person wearing multiple tags on different garments, allowing the system to trigger context‑aware actions (e.g., turning on a reading lamp when a resident sits in a chair). Third, the system records temporal trajectories of tags, enabling questions about movement history (“Did the medication box move at 8 p.m.?”) and supporting health monitoring by detecting abnormal inactivity. Fourth, state detection is supported: tags can signal whether a door or window is open, and multiple body‑mounted tags can be combined to infer posture (standing, sitting, lying), which is useful for fall detection or emergency response. Finally, the system can aggregate these capabilities into checklists for routine tasks, such as verifying that all windows are closed before leaving the house.

To validate the technology, the consortium conducted a week‑long diary study with participants who recorded 47 instances of object relocation and 22 instances of state checks. These data informed the design of acoustic and graphical user interfaces (AP 7.1 and AP 7.2). The interfaces were refined through multidisciplinary workshops involving partners such as OFFIS and Die Netz‑Werker, and the findings were presented at internal project meetings. The project also carried out a series of workshops to identify ethical, legal, and social implications (ELSI). In total, 81 ethical aspects were catalogued and discussed, ensuring that the system’s deployment would respect privacy, data security, and user autonomy.

Collaboration was organised around a consortium of research institutions and industry partners. OFFIS contributed expertise in radar hardware and system integration, while Die Netz‑Werker supplied user‑experience design and interface development. The Living Lab at IDEAAL provided a realistic testbed for user studies and field trials. The project’s timeline spanned several years, during which the consortium planned cross‑project workshops and a location‑agnostic study; however, the COVID‑19 pandemic forced a shift to internal workshops and limited the execution of the planned external studies. Funding was provided through a European Union research programme, supporting the development of the radar‑tag technology, the creation of user interfaces, and the comprehensive ELSI assessment. The project’s outcomes include a demonstrator system that achieves 5‑cm localisation accuracy, a set of validated use‑case scenarios, and a framework for ethical deployment of passive IoT devices in domestic settings.