Autonomous Smart Watches: Toward an ultra low power microphone detector with events classification
Today sensors and wearable technologies are gaining popularity, with people increasingly surrounded by “smart” objects. Machine learning is used with great success in wearable devices and sensors in several real-world applications. In this project we address the challenges of context recognition on low energy and self-sustainable wearable devices.
You will investigate the possibility to implement neural network in a ultra low power energy efficient micro-controller (Ambiq Apollo). The mail goal of the project will be implement and evaluate a complete neural network using already available framework to achieve context recognition. The idea behind this project would be to build a working prototype of a wearable multi-sensor devices which is able to collect and process data directly on board matching the requirements of long life time when supplied by a battery.
Depending on the applicant's profile and project type, his tasks may involve some of the following:
- Developing the context recognition algorithm with neural network and implement them on the Ambiq Apollo
- Design and develop a new version of the wearable device which include sensors and the new micrcoprocessor (Ambiq Apollo)
- 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
- programming the circuit for specific application, field testing
- printed circuit-board design to make it suitable for long term monitoring.
- Looking for Semester and Master Project Students
(not all need to be met by the single candidate)
- analog electronics and signal conditioning with operational amplifiers: amplifiers, filters, integrators etc.
- knowledge of micro-controller programming (C)
- basic knowledge on signal processing is a plus.
- plus is knowledge on printed circuit board (PCB) using Altium.
- 30% Theory
- 50% Implementation
- 20% Testing