THESIS DFENSE
 

Author: Mark Shperkin
Advisor: Dr. Homayoun Valafar
Date: July 15, 2025
Time: 03:00 pm
Place: Room 2267, Innovation building

Abstract

Competitive swimming performance analysis has traditionally relied on manual video review and multi-sensor systems, both of which are resource-intensive and impractical for everyday training use. This thesis investigates whether a single wrist-worn inertial measurement unit (IMU) can be used to automatically segment and classify swimming activities with high accuracy. We propose a multi-task deep learning pipeline based on the MTHARS (Multi-Task Human Activity Recognition and Segmentation) architecture introduced by Duan et al. to perform stroke classification, lap segmentation, stroke count estimation, and underwater kick count estimation. Data were collected from eleven collegiate-level swimmers wearing left-wrist–mounted IMUs, each performing five 100-yard sets per stroke (butterfly, backstroke, breaststroke, freestyle, and individual medley) in a 25-yard pool. This research investigates whether a single IMU can accurately classify and segment all competitive swim strokes and evaluate performance across key swimming activities. Moreover, this pipeline delivers reliable multi-metric analysis while significantly reducing the complexity and cost of sensor setups. This work contributes to the growing field of wearable-based athlete monitoring and has the potential to empower coaches and athletes with real-time, fine-grained performance feedback in competitive swimming using minimal hardware.