Over the past decade, the number of sports games played in a calendar year has drastically increased, leading to shorter recovery times and increased injury risks among athletes. Wearable technologies are used to help trainers manage workload, improve performance, and prevent injury. Current solutions aim to measure performance through distance, exercise times and heat map metrics, while StaminaSense provides detailed overviews of an athlete’s physiological state through biometric sensors and motion tracking. This data allows coaches to make educated choices on training, substitutions, and recovery strategies, which can reduce injury and optimize athlete output. StaminaSense uses wearable technology with sensors to obtain data. The system collects input during athletic activity and processes data through a model trained on the athlete wearing the equipment. The output is presented on a mobile device, recommending rest periods when certain thresholds are met. The biggest advantage of StaminaSense over current solutions is its integration of internal physiological monitoring combined with machine learning to offer a simplified approach to athlete management.
We would like to extend our sincerest gratitude to our supervisor, Maran Ma, for her guidance, expertise, and support throughout the duration of this capstone project. We are also grateful to the faculty of Electrical and Computer Engineering at the University of Waterloo and the ECE498 instructional team for providing us with the resources and facilities that allowed for the development and research of this project. We would like to recognize the collaborative effort and shared dedication of our entire team: Eric Chanthalima, Michael Tham, Jinha Kim, and Kordian Mazurkiewicz. Everyone brought unique contributions to the table, enabling us to realize this project.