Biomedical Circuits, Systems, and Applications
Research on biomedical sensing systems and signal processing algorithms has been very prolific in recent years with a variety of solutions in a wide range of application scenarios, for example long-term monitoring of human vital signs for disease detection. Low-power consumption and energy efficiency are the key features of such systems starting from the sensor node for data acquisition, towards embedded systems for data handling, and accurate algorithms for data processing.
Many research topics are actively ongoing around the human body, from chip design, to system development, to algorithmic investigations in various application scenarios. In the following sections you find links to past and current projects that you might find interesting.
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Human Intranet is an open, scalable platform that seamlessly integrates an ever-increasing number of sensor, actuation, computation, storage, communication and energy nodes located on, in, or around the human body acting in symbiosis with the functions provided by the body itself. Human Intranet presents a system vision in which, for example, disease would be treated by chronically measuring biosignals deep in the body, or by providing targeted, therapeutic interventions that respond on demand and in situ.
In the following, a summary of the main projects is given. More details can be found here.
Noninvasive brain–machine interfaces (BMIs) and neuroprostheses aim to provide a communication and control channel based on the recognition of the subject’s intentions from spatiotemporal neural activity typically recorded by EEG electrodes.
In this project, our goal is to develop efficient and fast learning algorithms that replace traditional signal processing and classification methods by directly operating with raw data from electrodes. Furthermore, we aim to efficiently deploy those algorithms on tightly resource-limited devices (e.g., Microcontroller units) for near sensor classification using artificial intelligence.
Epilepsy Seizure Detection Device
Seizure detection systems hold promise for improving the quality of life for patients with epilepsy that afflicts nearly 1% of the world's population. In this project, our goal is to develop efficient techniques for EEG as well as non-EEG signals to detect an upcoming seizure in an ultra-low-power device.
Ultrasound is one of the most used medical imaging modalities. Its main features are: real-time operation, low-cost, wide-availability, excellent spatial and time resolution, low-power. Recently, multiple innovative systems are being developed, targeting both high-end and wearable/embedded applications.
At IIS, we are exploring the next generation of medical ultrasound imaging systems, spanning from very high-end systems (FPGA-based digital probes with >100 channels and ultra high-speed Gb/s interfaces), to wearable wireless systems (FPGA or MCU-based, equipped with WiFi or Bluetooth links).
More information can be found here.
Every human and animal body generates a large and steady amount of data as consequence of several underlying life-long processes, e.g., respiration, vascular system dynamic, muscle contraction. By acquiring and processing these vital signals, usually by electrical or optical means, substantial amount of information can be extracted, enabling sense-making being used to take informative decisions. Successful application examples range from commercial fitness-tracker gadgets to medical-grade devices that enables tele-health remote medicine, as well as edge-cutting scientific research on living biological models.
It is a joint effort between the Analog and Mixed Signal and Digital Design Groups. More info here.