There are different options on the market for optimized AI training plans that are based on applying machine learning to your individual data. In this post, we compare the different options and their features.
We are big fans of TrainerRoad (TR) and their training plans which we have personally used extensively in the past. TR has announced adaptive training which "uses machine learning and science-based coaching principles to intelligently adjust your training plan, so you get the right workout, every time".
At this point TR is recommending one-off workouts 'TrainNow'. This feature looks similar to Garmin's daily recommended workout. TR is looking to incorporate more machine learning into their app in the future. For more info, see DC Rainmaker's post.
AI Endurance uses cutting edge machine learning technology (we even developed some our own methods) to create personalized AI training plans. The plans are based on your historical data and are predicted to give you the biggest performance gains for the time you have available for training. The AI is guided by best practices established around endurance training. It adapts your training as you progress through a training plan.
You can get cycling plans, running plans and also triathlon plans that take both your running and cycling data into account simultaneously. We predict your performance on your event date for the most common event types from 5k to Ironman.
You can also easily get your AI Endurance workouts into Zwift for both cycling and running. So if you're using Zwift already you can simply execute your personalized workouts there. You can also do your workouts directly from your Garmin device.
We believe that this is just the beginning of using machine learning to help endurance athletes improve. It is a classic problem where no human being can possibly wrap their head around all the possible ways we could structure endurance training and what the possible outcomes would be. AI however - can.
by Grant Paling. In the first of three blog posts, Grant shares his experience of qualifying for Great Britain's Age Group team in the upcoming European Triathlon Championships.
In this post, we discuss staying motivated executing your cycling training plan when you’re not training for an event. Also we give an update on our training and give a status report on how AI Endurance’s performance predictions are stacking up against reality.
by Stefano Andriolo. We demonstrate a universal relationship between cycling power and DFA alpha 1 from every day workout data that allows accessible and regular tracking of aerobic and anaerobic thresholds without the need of an exercise lab or even a dedicated testing protocol.
by Markus Rummel. We present the first results of AI Endurance's new capability to calculate Respiration Frequency (RF) from in-activity heart rate variability (HRV) data. RF demonstrates its potential in assessing the validity of HRV threshold determination.
