Using machine learning to truly personalize fitness

It’s amazing to see that exiting a full year of lockdowns, closed gyms and constant mask wearing, the personal fitness industry in 2021 is now a $100 billion market. And what’s also super interesting is that how consumers are staying in shape in the wake of a global pandemic has drastically shifted how and where those dollars are being spent. For example, while previously consumers might have gone to the local gym or attended studio classes, more than ever people have turned to at-home virtual classes and connected fitness equipment. At the same time, that shift has opened the door to fitness driven by artificial intelligence that take into account their strengths, weaknesses, and overall fitness goals in a way that traditional gyms and training programs can’t. Supported by wearable devices sporting simple tracking technologies for things like heart rate, overall exercise activity, and sleep patterns, AI fitness solutions are making a play for mainstream fitness consumers by offering a truly personalized training program  - and capitalizing on this market demands robust machine learning muscle, however. That’s why equipment manufacturers and fitness companies are using machine learning applications to handle the massive amounts of data processing required to deliver personalized fitness programming.
 
The new fitness normal 
A number of companies are harnessing the power of machine learning to help develop personalized fitness applications. Example FitnessAI, Tonal, and Tempo, they all incorporate some form of machine learning to collect, interpret, and apply anonymized user datasets at scale, in turn making the potential of personalized fitness both possible and practical – and of course, helping users meet their fitness goals. For example, the FitnessAI iOS app uses data from about 6 million+ workouts to build customized fitness plans. Users just enter basic biometric and goal data, and the app creates a personalized training program that specifies what exercises to do, what weights to use and how many reps to complete. The app then uses an AI algorithm to suggest a progressive increase in weights and reps relative to user size and strength, in turn providing a more personalized training experience. Applications such as Freeletics fill a similar niche, allowing users to define their own goals and customize nutrition plans.
 
Big data, big gains: The AI advantage
Sports and fitness is now a tech data-driven field even at personal level and this is awesome. Research into human capabilities, limits, and overall performance has led to the development of generalized programs that help build strength, reduce fat, or improve endurance.That said, on a personal level, however, performance and potential deviation from the median — every person’s physical makeup is different, meaning that they perform, adapt, and gain strength or endurance at vastly different rates. Traditionally, physical trainers filled this gap, and their in-person expertise combined generalized knowledge with client characteristics to shape programs suited to each individual. 
 
AI in personalized training today offers a way to bridge the gap by leveraging machine-learning algorithms to aggregate generalized physical data, collect specific and anonymized information about users and then combine these datasets to create truly personalized training programs. In fact, the main benefits for users are access to training planning, monitoring and even motivation at a fraction of the cost, which means that more people can be reached than ever before. 
 
Lastly, right now, integrating machine learning technology into fitness equipment requires access to massive amounts of personal user data, such as current fitness level, height, weight, and, in some cases, anonymized images of body shape and type. Then, fitness companies must develop AI outputs that deliver individualized suggestions and lead to sustainable fitness improvements over time. But as the industry evolves from supervised ML to synthetic datasets as well as game theory and deep learning, the need for massive curated datasets will be less of an issue – this is an AI industry wide trend. 
 
 
Expected benefits of AI-driven fitness tech
 

• Person-centric: While trained fitness professionals offered customized physical activity frameworks, AI tools provide access to truly specific workout plans. By using a combination of user information, form data captured by on-board cameras, and physical measurements taken by sensors that detect physical effort exerted or monitor users during exercises, intelligent fitness tools can deliver specificity on a level that was previously impossible.

 

• Scalability: AI fitness modeling helps improve user outcomes by intelligently moving the goalposts. Example Tonal — as users get more comfortable at current resistance levels, the device automatically increases resistance to ensure ongoing improvement. This scalability helps provide long-term value for users by delivering dynamic rather than static goal-setting that evolves in tandem with their performance. Companies today are also pairing their AI offerings with certified, instructor-led classes that allow users to access professional trainer expertise without the in-person component, which is a major competitive advantage over traditional fitness facilities in that they can deliver workouts to an almost unlimited number of clients.

 

• Durability: One of the most feared issues with classic physical training is the “plateau” which occurs when current exercise regimens stop delivering measurable results but instead lead to a “leveling out” of personal performance. For many gym-goers, this lack of measurable progress leads to waning exercise interest and eventual abandon. With AI-powered training programs can offer a more durable success approach, by combining current user data with aggregate workout information from millions of other users. The same can be achieved with the other issue in fitness which is overtraining and strain recovery management: AI tools are better positioned to create programs that deliver steady gains and track strain and time recovery much better.

 
 
In conclusion, I think that machine learning technology is becoming an increasingly accessible option for companies looking to build out data-driven fitness products to deliver business value and user delight. Further, if you think about machine learning solutions baked into connected fitness technologies to offer a way for users to receive personalized, real-time feedback about their fitness efforts in order to help them achieve specific goals over time. It’s clear today that the rapid expansion of this exercise market vertical and shows that truly personalized training with integrated machine learning muscle has arrived, and it’s going truly awesome.

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@philippemora > I come from the future. I work and I workout. Always be kind and passionate. 🙏❤️💪🏋️‍♀️🔥🚀