In a world where we depend on technology in our everyday lives more than ever before, it is quickly becoming clear that as tech evolves, so too does our application and understanding of these innovative developments. 

In this article, we discuss one of the products of such technological evolution. A technology that is changing how we experience sports and fitness, transforming our ability to progress and meet our health goals. The development of cameras and processors merged with one of the world’s most significant recent advancements – the rise of AI. It has given us an exciting, fresh application that promises a whole new age of fitness.

How does computer vision increase user engagement?

Computer vision defined – how it works in fitness

Computer Vision (CV) is pretty much exactly what it sounds like – the ability of a computer to recognize visuals, interpret them and convert them into digital data, much like the human eye and brain does.

So, how does it work?

Computer vision goes far beyond simply seeing images and recognizing them; it can respond to these images, translating what it sees into usable data. It even boasts the capacity to take meaningful actions or make advanced recommendations based on what the camera sees in front of it. This, coupled with progress in the field of AI in sports, means that we’re entering a new era of fitness and sports training.

Through the application of advanced algorithms and machine learning capabilities, computer vision uses some pretty standard image recognition types, each developed to perform a specific function or based on differing analytic capabilities:

  • Classification – Identifying a single object within a field of vision and giving it a single label. Practical example: if the user is supposed to train with some equipment (e.g. dumbbells, ball, barbell, etc.), the computer detects if they hold the right object.
  • Semantic Segmentation – In contrast to classification, this is the process of assigning a label to every pixel in an image. Practical example: the computer detects how much space the trainee has to perform the exercises and adjusts recommendations accordingly.
  • Classification & Localization – Predicting the class of one object in an image while being able to identify the location of the objects in an image before drawing a bounding box around it. Practical example: pose estimation – computer detects whether the trainee performs the right exercise or whether they do it correctly. However, if more than one person is in the frame, the computer can detect only that the mistake was made, but not by whom.
  • Object Detection – The ability to identify and locate objects in an image or video and define them from their backgrounds. Practical example: same as above; however, in this case, the computer can analyze more than one trainee at a time. Imagine there are 2 people in the frame, and each of them is supposed to do 6 squats – the computer can assess who did it correctly.
  • Instance Segmentation – The process of detecting and delineating each distinct object and object instance appearing in an image. Practical example: this type of image recognition can be used in strength training. One day the trainee holds a dumbbell with the writing “5 kg,” after a few days, the computer will check whether the weight has been increased (progress tracking) or whether the trainee is exercising with the recommended load (e.g. if they are to increase the load every series).

So, what is computer vision being used for? From recognizing a single, stationary object to more complex, advanced image recognition and object differentiation, CV is making waves in a field where most new technologies are having a tremendous impact – sports. 

How does computer vision increase user engagement?

As advancing technologies empower athletes to push the boundaries of excellence, it is important to understand how computer vision helps sports and fitness industries connect and engage with their participants. 

But how computer vision is actually used in sports is subject to many applications. Most notably, it allows people to analyze their progress more effectively. Apps that boast CV can provide data-driven recommendations and accurate feedback to users, boosting engagement and adding a new interactive dimension. And this is the essence of the challenge that computer vision in fitness is capable of addressing. 

One of the biggest issues faced by sports and fitness training apps is their inherent weakness in keeping users engaged. Without the ‘human touch’ of a real-life coach or trainer, 50% of people are highly likely to give up and abandon their programs within the first six months. Besides this, computer vision sports analytics are fully capable of informing a user when they’re performing an exercise or activity incorrectly, changing how people train and stay fit. 

Computer vision for sports is changing how we approach the way we engage users, allowing apps to become more adept at providing critical feedback to users and giving them an edge in keeping people engaged and committed to exercising. 

Sports computer vision helps keep fitness app users engaged

What are the benefits of computer vision?

Okay, now that we have a good understanding of how computer vision in sports works and how this tech is resolving the engagement challenge, let’s look at some of the impressive advantages of this advancement. 

Helping users to perform exercises in the correct way

Almost everyone who works out knows that even the slightest variation in their technique can have big consequences on the end results. From missing fitness targets to sustaining an injury, having the ability to spot and correct these differences can have a major positive impact. 

Here’s where computer vision comes into play, making it possible to identify and compare a user’s workout or sporting movements to the “ideal” technique and then advise on the changes needed to start getting their programs right. 

Potentially prevents mistakes that could lead to injuries

Injury is the biggest concern among sportspeople and fitness enthusiasts alike. A Mayo Clinic study found that, on average, people are likely to suffer up to 9 injuries per 1,000 training hours, so it is clear that having the correct feedback can significantly reduce this incident rate. Even a minor injury can put a sudden stop to your routine, rendering months of effort and training moot. 

CV is able to learn which actions are likely to cause injury and then warn a user to avoid high-risk motions or recommend an alternative, safer option. 

Appeals to users & increases the attractiveness of apps            

Already being used by top sporting professionals and athletes, computer vision is a magnet for users looking to employ technology that appeals to those on the frontlines of innovation and progress. Take, for example, Second Spectrum, one of the NBA’s Official Tracking Providers. By combining computer vision for sports, AR, and the historical data of the players themselves, they can tap into engaging statistics and visualize these in graphic format. The result? Their tool, Court Vision, is making basketball broadcasts much more attractive for fans while providing in-depth analytics for the teams themselves in real-time.

Improving user retention

While sports apps note a 27.6% retention rate one day after downloading, this figure drops to 9.9% by Day 30, translating to a 90% retention loss in just one month. These figures are a symptom of a common problem – the perception of a lack of sustainable value that the app has for the user.

Computer vision offers a legitimate solution to the problem of low retention, especially meaningful to those apps relying on paid subscribers. The boost in the app-user experience offered by CV through delivering a more personalized training feedback experience means that computer vision is a valuable and vital addition to any app feature arsenal. 

Use cases of computer vision in sports

Use cases of computer vision in sports

Don’t be fooled by the nature of sports computer vision as a tool only for elite, professional athletes. Advances in mobile technologies and the scaling of AI development services mean that at-home users can now take full advantage of this innovative and helpful resource. 

Personalized, at-home training

Here are examples of popular computer vision-powered apps that people are enjoying in the comfort of their own homes.

  • Tonal – uses the latest dynamics to respond to your every move. The app begins by evaluating your current state before granting access to countless guided workouts and training programs, each tailored to specific needs. Users can review detailed metrics and scores from each custom workout while keeping track of their progress over predefined periods.
when it comes to computer vision for sports Tonal is a great inspiration
Source: Tonal
  •  Kemtai – is an adaptive home exercise platform that uses computer vision to analyze users’ exact movements, offer detailed feedback for improvements and corrections, and take advantage of AI to offer bespoke, personalized fitness experiences. Boasting corporate wellness features alongside physiotherapy and musculoskeletal evaluations, Kemtai is one of the most advanced CV exercise platforms out there.
Kemtai is a great example of an app that leverages sports computer vision
Source: Kemtai
  • Mirror – takes an innovative approach to in-home, computer vision-driven training. The software uses CV and – you guessed it – a smart mirror to supplement your workout at home. Additionally, fully integrated intelligent weights track reps and movement form, while the platform allows users to compete with friends live. This truly is the next generation of home exercise. 

Computer vision in yoga training

Beyond the home workout regimes and sports optimization offered by computer vision-enabled apps, yoga training is another field that benefits tremendously from the advantages of using computer vision to perfect techniques and improve training. 

A digital computer vision technique called human pose estimation uses AI to identify specific, pre-programmed poses in real-time and give users the feedback they need on the spot. The AI applies keypoint skeleton models to track the motion of the users’ joints and offer on-the-move updates on making the necessary corrections. 

Other use cases for computer vision in sports        

Computer vision is also used in a variety of other individual and team disciplines:

  • Hockey – Computer vision has, for some time now, been used in hockey to track the action in real-time. The Action Recognition Hourglass Network (ARHN), for example, is a visual processing model that is able to track, identify and classify a range of elements from player positions and skating movements to their precise body positions while taking shots at goal. 
  • Tennis – Some sports, like tennis, cricket, and badminton, rely on computer vision sports analytics to ensure fairness and accuracy. In tennis, computer vision technology is used to track and monitor balls, nicks, edges, and foot movement. It’s able to spot if an object is in play or not, far better than an umpire’s or referee’s eye can. 
  • American Football – The NFL uses computer vision to assist in team tactics and play preparation by using computer vision football video footage to generate offensive formation labeling and recommendations by looking at player movements and determining the best course to be taken by a team. From opposition analysis to calculating the odds that a player will choose to run or pass the ball, CV has played an important role in the evolution of the game for some time. 
  • Basketball – Players in the NBA are benefitting from computer vision basketball-assisted programs like the Noah Shooting System. By employing a raft of CV-enabled cameras to track myriad shooting analytics, players are able to tap into the power of AI to improve their game and improve shooting accuracy. 

Computer vision sports analytics

Computer vision sports analytics are capable tools for allowing people to improve their workout and training techniques, progress, and form. But in order for this technology to serve its purpose, it must be powered by accurate and effective analytics.

These analytics are even more powerful if collected over an extended period, and this is where CV really stands up. Besides simply informing the user if they are performing an exercise correctly at that moment, AI and the application of advanced algorithms mean that CV has the power to use collected data, interpret it, and learn what works for the user and what doesn’t.

Cost of computer vision in sports apps 

There’s no doubt the cost of creating a fitness app powered by features like computer vision may seem challenging initially, as it requires a particular skill set, a bigger budget, and more time. However, the high interest, retention, and engagement among users of a more advanced fitness or sports app mean that in the longer term, the ROI is far better.

For this reason, it’s worth working with a capable partner who offers cutting-edge fitness app development solutions. Choosing one like that, you’ll know that your project is well taken care of – and built with the utmost care to avoid unnecessary costs, leaving you with an end-product you can be proud of and one your users will enjoy.

Read also: machine learning for business

Computer vision sports analytics are capable tools for allowing people to improve their workout and training techniques, progress, and form.

Computer vision in fitness & sports – summary

Computer vision in fitness and sports has been around for a while. Yet, it is only now being rolled out in fitness apps that people are turning to for at-home training. Beyond professional sports, computer vision is changing how people stay in shape, avoid injury and keep fit. 

In taking on the challenges of low retention and engagement, computer vision analytics and AI solutions are reshaping how we’re able to fulfill users’ growing needs and increase their loyalty to your app.