Difference between revisions of "Embedded Gesture Recognition Using Novel Mini Radar Sensors"
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Latest revision as of 14:36, 10 November 2020
Wearable technology is gaining popularity, with people wearing everything “smart” from clothing to glasses and watches. In this project, the students will design and hardware and software implementation and in-field evaluation of a smart wearable device with a wireless interface for gesture recognition using novel mini radar sensors. The specific goal to design a device able to get the data from the sensor and processed them with machine learning algorithms in order to classify the gesture of the user and take an action with the wireless interface (Bluetooth Low energy). On the wearable device side, both hardware and firmware will be designed with low power in mind. The final goal of the project is to design a new generation of wearable devices to using machine learning algorithms in an energy-efficient way. The candidate will work with micro-controllers, sensors, wireless communication at firmware level as well as data analysis tools and training tools on the PC/cloud. The hardware and software load of the thesis will be balanced according to the skills and preferences of the candidate students when the details task description will be provided before the student project will start. In the field measurements of the system will be performed from the students as an important activity in order to evaluate power consumption, reliability, functionality, classification accuracy, and energy efficiency and to further optimize the system.
Depending on the applicant's profile and project type, his tasks may involve some of the following:
- lab. testing/characterization of the existing prototype: verification of the prototype's characteristics w.r. design specification (simulations), measuring power-consumption, and assessing detection performance in lab. Conditions
- High-level software programming, machine learning, wireless communication
- programming the circuit for a specific application, field testing, data acquisition
- Machine Learning for microcontrollers.
- PCB design to build a working prototype which includes all the subsystems
Figure sourse: Project Soli Google ATAP "https://atap.google.com/soli/"
- Looking for bachelor, Semester and Master Project Students
- Supervisors: Michele Magno
(not all need to be met by the single candidate)
- Experience using the laboratory instrumentation - signal generators, oscilloscopes, DAQ cards, Matlab etc.
- analog electronics and signal conditioning with operational amplifiers: amplifiers, filters, integrators etc.
- knowledge of microcontroller programming and PC programming (C/C++, preferably microcontroller with Bluetooth Low Energy but it is not mandatory)
- basic knowledge on signal processing and machine learing is a plus.
- plus is knowledge on printed circuit board (PCB) using Altium.
- 35% Theory
- 45% Implementation
- 20% Testing