The human body has not yet adapted properly from the hunter-gatherer lifestyle to a more sedentary one, leading to a rise in dangerous, avoidable diseases including obesity, type 2 diabetes, and osteoporosis (Smyth, 2019). People are realising the importance of living a healthier, more active lifestyle. The advance in wearable sensors and mobile fitness applications reflects this. These technologies allow users to track their activities and set goals, but they do not take an active role in prescribing specific training and recovery activities for users (Smyth, 2019).
The context of endurance sports is great from a machine learning perspective for several reasons (Smyth, 2019). Firstly, the number of people participating in endurance events each year is large, and within that there are a lot of inexperienced individuals needing assistance. Secondly, the aforementioned rise in fitness technologies means that there is a ton of data available from mobile fitness apps such as Strava. Lastly, there are a number of interesting problems to be solved using machine learning techniques such as: fitness level estimation, training session classification, recovery and injury prediction, how to develop personalised training programs, goal time prediction and pacing planning (Smyth, 2019).
Prior work in this area includes the development of a novel application for recommender systems to predict a personal best marathon finish time and pacing plan for a user (Smyth, 2017). Initially this was done using runners who had run two or more marathons. The finish time prediction was accurate for fast runners, but less so for slower runners who would likely be those who would benefit most from this prediction. A later paper (Smyth, 2018), largely improved this using a richer training history for runners. However, these methodologies do not allow for first time marathon runners to determine a predicted finish time.
Therefore, this project will involve extending this previous work to inexperienced marathon runners by including data about different distance runs in place of missing marathons, as well as working on solving some of the other tasks mentioned. The explainability of recommendations made to users will also be a focus for this project since this will allow users to understand why specific training activities are being suggested to them.
Smyth, B., Cunningham, P. 2017. “A novel recommender system for helping marathoners to achieve a new personal-best,”