UCB capstone project providing running training recommendations
Description (less than 140 characters):
Elevate your running journey with PacePerfect! Receive personalized run recommendations tailored to your running history and goals, improving speed and minimizing injury.
Problem & Motivation:
The US running community is growing rapidly, with more people participating in marathons and leisure runs. However, runners lack personalized resources to improve their performance. This project aims to bridge the gap by developing custom training plans for speed, endurance, and recovery, empowering runners with clear direction and the opportunity to excel in their running journey.
Data Source & Data Science Approach:
The PacePerfect app utilizes cutting-edge machine learning technology and vast athlete data to create personalized running experiences. By analyzing the running history and specific goals of individual users, the app suggests personalized training runs. It gathers essential running metrics like distance, speed, and pace to understand users' habits comprehensively. The app scores training runs based on intensity, ensuring perfect alignment with users' goals and abilities, while also curating a diverse and engaging training plan to keep users motivated and excited.
Evaluation
The project's success is evaluated based on the impact of PacePerfect on users' running performance and experience. By following the tailored training plans produced by the recommender system, users should experience significant positive changes in their running capabilities....
Key Learnings & Impact:
Insights from Subject Matter Experts (SMEs) played a pivotal role in shaping the PacePerfect app's development. We learned about the significance of different types of essential runs in a comprehensive training plan. This led to the integration of a user-friendly interface, enabling runners to customize their training based on intensity runs and long run preferences, aligning with their individual goals. Additionally, we discovered the importance of analyzing run load and intensity in crafting schedules. This led to the creation of an intensity function that classifies runs based on distance and individual athlete profiles. As a result, the app effectively and safely helps users reach their running goals.
Acknowledgements:
We express our gratitude to Professors Joyce Shen and Zona Kostic for their invaluable guidance and advice during the semester, which greatly contributed to the development of the PacePerfect app. Additionally, the team extends heartfelt thanks to our Subject Matter Experts and other individuals who offered valuable insights and unwavering support throughout the development process. Their guidance and expertise played a crucial role in making this project successful and impactful.