AI/ML Technologies Utilization for Fitness Industry

As the consciousness for personal health and wearable devices adoption increases, the Global Health & Fitness Market is expected to grow at 8.71% CAGR, resulting in a projected market volume of US$5.77 billions by 2027.

These days, the fitness market offers connected devices and apps that motivate users via feedback loops and community competitions (see, for example, Strava, Peloton, Interactive, Zwift, Hydrow, Inc., and Tonal).

However, to win in the growing competition, future fitness apps should address the major challenges in the industry:

1.Lack of true personalization

Motivation and personalization are key factors that can improve user engagement, but creating effective strategies that keep users interested can be a challenge.

2. Interoperability between devices and platforms

Consumers often use multiple devices and apps, but they may not communicate with each other effectively, which can lead to a fragmented user experience.

3. Difficulty creating sustained behavior change in users

While many consumers buy connected fitness devices and apps with good intentions, they often struggle to stay engaged over time.

AI/ML-powered technologies could overcome these challenges in the connected fitness and device market by providing users with a more unified, engaging, and personalized experience, and creating sustained behavior change.

There are multiple ways in which AI/ML technologies can advance workout plans, create recommender systems for numerous fitness activities, and empower users with new strategies and applications.

AI/ML technologies could improve workout planning and offer personalized recommendations for various fitness activities (workout plans and specific exercises), based on individual preferences and goals, as well as provide new insights into personal training strategies. 

1. AI-based Personalized Workout Plans

Using physiological data such as heart rate, heart rate variability, breathing rate, blood pressure, sleep patterns, activity levels, etc., we could build a goal-oriented model for individual physiological reaction prediction and custom feedback on training effectiveness and recovery/stress status.

There are a few approaches to building a personalized recommendation system, namely content-based filtering, collaborative filtering, and hybrid. For instance, DeepFM is one of the top algorithms combining the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.

2. AI-powered Exercise Recommendations

ML algorithms constantly assess the trends in a user’s personal data (both from remote patient monitoring and self-reported data) and make informed changes. This process allows for the targeting of the client’s fitness activity through the suggested exercise types. This approach is based on individual physiology, activity levels, and preferences.

There are a few options of ML algorithms to use:

• Mixed Integer Nonlinear Programming (MINLP): This approach uses optimization algorithms that are perfect for problem-solving with nonlinearities in the objective and/or constraints, as well as continuous and integer variables.
• Genetic algorithms (GA): This is a more generic approach for problem optimization.

As well, the workout plans should be constantly readjusted to control continuous progress and ensure that the user stays on track based on personal physiological data and activity levels.

A significant step forward in the recommendation algorithm is a combination of batch training with online training, which allows real-time system adaptation. This is precisely how top recommendations work on TikTok, Instagram, YouTube shorts, and similar apps.

As a result, according to some developers, using DeepFM and online learning could improve the conversion rate by 30%, merchandise value by 30%, and engagement time by 50%.

To sum things up, here is a list of potential use cases of AI-based recommendations in the fitness & sports domain:

• Recommendations on exercises, workout plans, and nutrition 
• Modification suggestions to improve users’ technique and avoid injuries 
• Customized workout plans for individuals based on their goals, fitness level, and preferences 
• Personalized coaching based on the users’ progress and goals

AI can be used to develop algorithms that can integrate data from multiple sources, making it easier for users to access and analyze their data. This would also provide users with a more holistic view of their health and fitness.

See the list of potential data sources for assessing and monitoring fitness progress.

1. Assessment Devices

This assessment method collects information from fitness bracelets, smart watches, and other wearables. For example, Firstbeat uses physiological data from an ECG-based wearable device (heart data, sleep patterns, activity levels, etc.) to identify stress levels, patterns, trends, and anomalies. 

2. CV and Movement Tracking

For example, STÆDIUM, a recent product launched by Freeletics, uses pose estimation to identify different parts of any movement and help improve exercise performance and workout effectiveness.

3. GPS-Connected Devices

For example, Strava allows users to connect GPS devices to track workouts such as running and cycling. As well, the service provides smart route recommendations based on personal preferences.

4. Smart Sports Equipment

For example, Tempo’s Move allows for AI-powered weight monitoring during the workout. And Hydrow rowing equipment allows for customizable training.

AR/VR/XR and gamification are powerful tools for boosting customer engagement in digital fitness and sport. On the other hand, this implementation involves high risks and requires significant investment.

Therefore, it is necessary to create a robust plan to have on hand. So, what is the process?

By converting physiological data into actionable biofeedback and adjusting the gameplay and settings accordingly, we could improve training effectiveness and the recovery process. In addition, this way users are taught to recognize lifestyle-related aspects such as stress, recovery, and physical activity.

The first step is monitoring and analyzing the physiological data from wearables (heart rate, HRV, breathing rate, blood pressure, etc.) to identify patterns, trends, and anomalies. Then this data is translated into feedback to adjust the game’s settings and create a customized experience that is tailored to the user’s individual needs.

To understand this process in practice, imagine a workout game that knows when you’re feeling fatigued and adjusts the difficulty accordingly. Or a yoga app that changes the pace and difficulty based on your stress and mindfulness. 

As a result, users receive meaningful software with effective feedback and advanced personalized recommendations that are not generic and that help users achieve their fitness goals by keeping them motivated and pushing them to their full potential.

Here are additional ways to use extended reality and gamification in fitness apps

• Build a personalized training environment based on the user’s goals, fitness level, and preferences.
• Generate suggestions to improve the user’s technique and avoid injury. 
• Create an immersive experience during workout sessions. 
• Build a personalized coaching experience based on the user’s progress and goals.
• Develop a virtual presence for joint team activities (metaverse services).

Our proposed AWS solution aims to enhance personalization and interoperability in fitness applications by leveraging various AWS services. The solution creates a robust and scalable infrastructure that supports data ingestion, processing, analysis, and visualization.

The solution also integrates Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, and Amazon QuickSight to provide a comprehensive and efficient data analytics platform for fitness applications.

A key component of this solution is the integration of a BI dashboard in Amazon QuickSight that offers powerful analytics and visualization capabilities using data from HealthKit. This dashboard allows users to gain insight into various aspects of their fitness journey including daily patterns and trends.

By visualizing HealthKit data through the BI dashboard, users can easily identify patterns in their activity levels such as differences between weekday and weekend patterns. These insights enable users to make informed decisions about their fitness routine, helping them achieve their fitness goals more effectively.

In summary, our AWS solution offers numerous benefits for fitness applications, including:

• Enhanced Personalization: By utilizing AI/ML technologies and HealthKit data, the solution enables fitness applications to deliver highly personalized workout plans, exercise recommendations, and coaching experiences tailored to individual users’ goals, preferences, and physiological data.
• Improved Interoperability: The integration of multiple AWS services creates a seamless data flow between various devices and platforms, promoting a more cohesive and unified user experience.
• Powerful Analytics and Visualization: The BI dashboard in Amazon QuickSight provides users with valuable insights and visualizations of their fitness data, helping them to better understand their progress, patterns, and areas for improvement.
• Scalability and Flexibility: Leveraging AWS infrastructure allows fitness applications to easily scale up or down based on demand, ensuring consistent performance and cost-efficiency.
• Data Security and Compliance: AWS solutions adhere to strict security and compliance standards, ensuring that users’ sensitive health and fitness data is protected and handled responsibly.

By implementing this AWS solution, fitness applications can create a more engaging and personalized experience for users, promoting sustained behavior change and ultimately leading to better fitness outcomes.

Integrating AI/ML components into fitness and sports products holds the future of user engagement, motivation, and health effectiveness.

Neurons Lab helped create an AI-based system for iPlena that analyzes customers’ photos and provides personalized recommendations to reduce pain and improve posture in just 3 minutes daily.

The Neurons Lab team created MLOps pipelines to ensure accessible building, training, and deployment processes for AI/ML models using AWS services. For more details about this AI solution and AWS-based infrastructure, check out the case study on the Neurons Lab website.

Interested in building an AI-based solution for fitness areas? 

Reach out to our globally distributed AI R&D.

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