The pAvIs consortium will develop intelligent sensor systems for professional healthcare applications. With the aid of embedded AI algorithms and innovative electronics, pAvIs will realize a paradigm shift from “one size fits all” to real-time adaptability of sensor-based systems to the individual patient. In the state-of-the-art the average healthy person is used to as reference for the development of sensor-based diagnostic and therapy systems. The same setup is expected to perform equally well on a 15 year old girl of 1,60m and 50kg, her 25 year old rugby playing brother twice her size, and her 80 year old grandfather with a deformed spine. Obviously, this is not realistic and does not deliver the best possible result. The project will deliver a new architecture for intelligent sensor systems with at its core a sensor module embedding a mixed-signal processing chain, which throughout will consist of new adjustable components. The adjustment settings will be determined in real-time based on the sensor signal itself or by exploiting integrated auxiliary detectors. To increase computational power-efficiency and miniaturize the system, a dedicated neuromorphic engine will be used for AI acceleration. Embedded AI algorithms will be developed with a specific focus on resource efficiency. Given the low-power requirements for sensor systems, a novel, distributed, power management system will be developed.