AI enables fast analysis of performance data, such as speed, heart rate and blood oxygen levels, gathered from bike computers<\/a> as well as wearables such as heart rate monitors<\/a> and cycling watches<\/a>. <\/p> This data is used by some of the best cycling apps<\/a> to generate personalised insights to help cyclists improve their performance. <\/p> \u201cIt\u2019s easy to assume that only pro racers are using machine learning technologies, but there are a growing number of consumer products using them to personalise training and extract new insights from the data you collect,\u201d says Chris Ruddock, a wearable technology specialist. <\/p> \u201cMore are also incorporating AI interfaces that make data simpler and more engaging than ever.\u201d <\/p> Ruddock cites TrainerRoad<\/a>\u2019s Adaptive Training platform as an example. \u201cThis recommends session plans based on how you complete previous workouts. This compensation is based on how you respond to each effort, theoretically removing the need to do Functional Threshold Power (FTP) ramp tests mid-block to recalibrate the intensity of sessions.\u201d <\/p> The wearable WHOOP tracker<\/a>, which counts Wout van Aert among its users, is another example. <\/p> WHOOP has introduced a Coach feature powered by OpenAI (of ChatGPT fame), which generates individual responses to users\u2019 health and fitness questions based on their unique biometric data. For example, \u2018Can you build me a 100-mile cycle training plan?\u2019 Apparently, it can. <\/p> To minimise bonking<\/a>, Team Jumbo-Visma (now Visma-Lease a Bike) has designed an AI model trained to predict the caloric needs of its riders. <\/p> Martijn Redegeld, the team’s performance nutritionist, explains that, in preparation for the 2023 Vuelta, the team entered core data (for example, power and GPX files), while the model sourced other input itself, such as weather conditions. <\/p> \u201cWith all this relevant input, the model starts to do its magic, and within minutes, we have the daily caloric needs of each rider accurately predicted for 21 stages,\u201d states Redegeld. <\/p> \u201cThese predictions form an important starting point for the personalised nutrition plans generated in the team\u2019s Athlete’s FoodCoach software, used by riders, nutritionist and chefs during a race.\u201d <\/p> But it\u2019s not only the elite who can fine-tune their sustenance. Another product, AI Endurance, calculates daily energy recommendations to maintain energy balance, and optimise nutrition, fuelling and recovery from training. <\/p> Driver assistance systems are common in cars, but AI can also be used in the prevention of cycling accidents, analysing traffic data and identifying hazards. <\/p> For example, Copilot is a bike light and camera<\/a> that monitors the road using AI to detect approaching vehicles. <\/p> Cyclists receive audible alerts based on the actions a vehicle is taking \u2013 such as whether it\u2019s attempting to overtake or approach dangerously. It also syncs with a mobile app, enabling cyclists to see what\u2019s happening behind them. <\/p> Clark Haynes, Founder of Velo.ai, the company behind Copilot, says the product has a key advantage over a continuous bike light. \u201cThe light on Copilot reacts to driver behaviour so, as a vehicle is approaching faster or coming closer to the cyclist, the light pattern will blink rapidly in response, increase intensity and change colour to draw as much attention as possible to the cyclist.\u201d <\/p> The rear-facing camera records the ride, automatically pulling video clips of close calls or collisions. \u201cIt\u2019s a huge advantage to immediately have access to a library of notable clips from your ride that downloads to your phone,\u201d says Haynes. <\/p> Several bike-fit apps, such as MyVeloFit and Bike Fast Fit EZ, are using AI to crunch the necessary data. It\u2019s a more convenient (and perhaps cost-effective) solution than visiting a bike fit specialist<\/a>, but you’ll need a stationary trainer or rollers, as well as basic mechanical skills.<\/p> MyVelo uses two aspects of AI \u2013 Computer Vision and Pose Estimation \u2013 to analyse your position from mobile video footage. <\/p> \u201cWe specifically look at the joint positions and angles the rider is achieving on their existing bike as well as their centre of gravity and weight distribution,\u201d says Jesse Jarjour, founder of MyVeloFit. \u201cWe then cross-reference that information to their stated riding goals and mobility to make recommendations on what changes (if any) the rider should make to their bike.\u201d <\/p> Jarjour says the advantages of a remote AI-enabled bike fit include being able to regularly monitor the fit. \u201cIt also means it’s easier if you end up going to an in-person fitter,\u201d Jarjour adds. <\/p> AI-powered bikes, such as the CAROL and Renpho, use biometric data to personalise our workouts based on power output, goals and cadence. <\/p> Renpho, for example, uses AI to generate workout algorithms that personalise the session by automatically adjusting the resistance level. There\u2019s also a power test to measure users\u2019 FTP<\/a> and identify the right training-intensity zone<\/a>. <\/p>![\"WHOOP](\"https:\/\/c02.purpledshub.com\/uploads\/sites\/39\/2022\/05\/WHOOP-3-849f875.jpg\")
Optimising your calories <\/h2>
![\"\"](\"https:\/\/c02.purpledshub.com\/uploads\/sites\/39\/2024\/05\/GettyImages-1718619298-1.jpg\")
Increasing safety <\/h2>
![\"\"](\"https:\/\/c02.purpledshub.com\/uploads\/sites\/39\/2024\/05\/GettyImages-1007576632.jpg\")
Getting comfortable <\/h2>
![\"Liam](\"https:\/\/c02.purpledshub.com\/uploads\/sites\/39\/2023\/03\/cypaero-314-8b77be1.jpg\")
Powering training<\/h2>
![\"Wahoo](\"https:\/\/c02.purpledshub.com\/uploads\/sites\/39\/2022\/10\/Wahoo-RGT-Magic-Roads-cdddf0a.jpg\")
<\/h2>