Short Range Radars For Biomedical Application
Short Range radars have been shown potentially to be used in many application scenarios including for remote sensing of biosignals in a more comfortable and easier way than wearable and contact devices. However, their performance has not been tested and reported either in practical scenarios and very few works exploit machine learning to further improve the signal processing. PBL is building a strong collaboration with Infineon to have the unique opportunity to work with novel short range radards developing systems and applications for biomedical applications.
The goal of the present project is to investigate and develop a novel embedded system for acquiring and processing short range data with machine learning. According to the background and level of the work the students can be involved in the algorithms that will be implemented and evaluated on real hardware, on the data acquisition, on the hardware design, on the hardware-software co-design or in all of them (i.e. in the case of a master thesis) . Moreover, the project has the goal of acquiring data from real subjects to have a data set to train and evaluate the algorithms on the proposed application scenario. This work will be done in collaboration with Infineon for the sensor side, and Hospital of Lausanne or Zurich for the biomedical application.
Goal & Tasks
The project(s) will address the following challenges:
- Investigate and develop techniques and methods to perform biomedical signal processing (hearth rate, respiration rate, etc) with short-range radar and energy-efficient NN to extract useful information (I..e prevent to detect some diseases.
- The algorithms will be evaluated and optimized for the capability of the embedded Platform to both increase the energy efficiency and, at the same increase the response time of the detection, aiming to achieve an always-on system.
- Acquire a large dataset for biosignal with short-range radars and possibly other biomedical applications to have the possibility to test and train the ML that will be implemented on the hardware.
- A complete hardware and software prototype of a smart sensor system, which includes all the subsystems (sensor acquisition, preprocessing, and processing and radio communication), will be developed to demonstrate the benefits of the proposed approach and the capability to achieve perpetual low latency and energy efficiency on the challenging scenario of biomedical applications
(not all need to be met by the single candidate)
- Knowdleg of high and low level programming languages (e.g. Python, embedded C)
- Knowdleg of embedded systems
- Knowledge of machine learning and signal processing
- Motivation to learn neural networks
- Motivation to build and test a real system and acquiring field data
Detailed Task Description
A detailed task description will be worked out right before the project, taking the student's interests and capabilities into account.
- Looking for Bachelor, Semester and Master Project Students
- Supervisors: Michele Magno,
- 35% Theory and Algorithms
- 35% Implementation
- 30% Data acquisition, Verification, and Testing