B03 - Modelling and design of ultra low power implants for closed loop stimulation
Summary of the work programme
In this subproject, a modular, fully implantable and highly integrated stimulation system suitable for the representative implant applications studied at the CRC, is to be designed, modeled, methodically studied and prototyped. This is intended to support multimodal energy sources and to work extremely energy-efficiently even in challenging closed loop scenarios. During the first funding period of ELAINE our research led to STELLA, an open source, modular and highly adaptable stimulation platform. The STELLA architecture includes basic sensory readout as well as a bidirectional wireless data communication module. Additionally, we analysed the integration of energy supply with the use of mechanical and thermal energy sources. This included the placement and optimisation of thermoelectric generators. As a result, ultra-long term animal studies were enabled that were previously impossible.
In the second funding period, we plan on extending the STELLA architecture with modules that allow for extensive therapy exploration through sensor information, working towards closed loop stimulation, while remaining fully implantable to enable undisturbed animal studies. Due to stringent constraints resulting from closed loop scenarios, a simultaneous optimisation of energy management and sensor data processing is mandatory. Hence, novel virtual prototyping and model-based design approaches will be investigated for ultra-low power, patient-specific electrostimulating implants. Energy management does not only differ depending on the area of application (brain, cartilage or bone), but it is highly dependent on the energy supply (battery or mechanical energy harvesting) as well as physical (size, weight or mechanical stability) and electrical (voltage, current, power or stimulation pattern) parameters. Data collection and processing on the implant and data transmission between the implant and the outside world for function monitoring and therapy support are also gaining in importance. In particular, this is essential for closed loop approaches where the implant’s stimuli are adapted based on information gathered from sensor data in order to leverage the effectiveness of therapy and reduce adverse effects. Scientific challenges to be addressed are ensuring an autonomous energy supply through lowest energy consumption of reliable and highly miniaturised electronics, miniaturised implant-specific energy harvesting mechanisms, efficient transmission, intermediate storage and management of the electrical energy, as well as ultra low power sensor data aggregation, processing, and communication.
University of Rostock
Institute of Applied Microelectronics and Computer Engineering