IMU-based motor variability metrics in para-swimming

Recent advances in wearable technology have allowed for a developed understanding of how swimmers move, by collecting acceleration data of the swimmers. Current advances can provide information on how each side of the body responds differently to fatigue and can automatically detect which type of stroke is being completed. However, how swimmers accelerate up and down in the water, and how their body rotates has not been explored in detail for para-swimmers. Additionally, advanced signal analyses can provide information on the underlying detail of these signals, which can become altered when fatigued. The purpose of this project is to create an automatic workflow to capture and process this acceleration data, calculating metrics that provide us with a new understanding of how fatigue affects para-swimmers.

Matthew Slopecki
Faculty Supervisor: 
Julie Cote
Partner University: