Determining position, speed and stride length using machine learning with sensor fusion based on ultrawideband local positioning system technology

Sensors that track human movement are becoming more and more popular in all kinds of applications including healthcare, sport and general human movement. However, traditional sensors generally have problems tracking individuals indoors and they are not very accurate when measuring subtle movements. Using innovative technology, new wearable sensors have been developed to track human movement that have solved the problems associated with previous sensors. Further development of these new sensors is still required and that is the overall aim of this project.

The goal of this project is to develop software that accurately calculates the speed and stride rate of athletes who wear a small sensor when they walk or run. To do this, we will compare the information we receive from the sensors with data that we collect in a laboratory using a video-based motion capture system, which is highly accurate. We will also use some advanced Artificial Intelligence techniques to process the information to help us develop the software. The newly developed software will allow the partner company to market and sell a new system that is very accurate and can be used indoors, giving them a major advantage in the marketplace

Intern: 
Pratham Singh
Faculty Supervisor: 
Darren Stefanyshyn
Province: 
Alberta
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