Core ML as a Technological Solution for Fitness Apps

3 min read

Summary: This review continues the series of articles covering the possibilities of Artificial Intelligence and Machine Learning. It’s been one of our company’s essential technological priorities, and we’re glad to share our findings with you. Our article will tell you about Core ML, a modern-day framework powered by machine learning algorithms developed by Apple. Core ML is easily integrated into an existing application.

Context of development

Doing exercise in a proper physical form is critical to achieving maximum results in sports. Mistakes in exercise techniques can lead to injuries, decrease training productivity, and delay achieving athletic goals. It’s common for sports coaches to use their experience and knowledge to help people improve their technique. However, the evolution of machine learning is bringing new tools that allow for automatic analysis of the exercising method and provide a trainee with feedback. These days, sports increasingly use Machine Learning to help people improve their shape and achieve more impressive results. Core ML became one of the most promising ML-enhanced developments for sports.

What is Core ML

Core ML is the foundational ML framework developed by Apple for iOS and macOS. It allows developers to create ML-powered applications across Apple devices such as iPhones and iPads. With its help, you can create Machine Learning models that recognize objects, classify data, process text, and do other functions associated with Machine Learning. Once developers have created a model, they can seamlessly integrate it into an application. After the model is deployed on the user’s device, it can be tuned with the user’s data.

We want to shed some light on how Apple implemented an ML model framework. An ML model is pre-trained in the cloud using deep learning frameworks such as TensorFlow, Keras, Caffe, etc. So, Core ML supports convolutional, the most common deep and recurrent neural networks.  Core ML supports the vision for image analysis (object classification), the foundation API used for natural language processing (speech recognition), and gameplay kit frameworks (organizing game logic).

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Advantages of using Core ML for developers

  • Availability offline
  • Low latency and near real-time results
  • Privacy as the data doesn’t leave the device
  • Easy integration of train ML models
  • Fast performance
  • Low cost of integration

How Core ML works for sports solutions

In sports applications, Core ML can be employed to analyze exercise techniques and provide feedback to trainees based on the analysis results. For example, a workout app can apply Machine Learning models built with Core ML to recognize body position and motions. The user can then obtain recommendations on how to improve their techniques.

Core ML also analyzes other parameters that impact sports performance, such as heart rate, respiration, speed, and acceleration. Sports coaches and medical professionals can use this data to assess athletes’ fitness levels and health more accurately. This data also helps them develop individual training and rehabilitation programs.

In addition, Core ML is applied to create virtual self-trainers. Tracking body position through the front camera can be particularly useful. Using this tool, you can accurately determine the body position and movements and match them with the correct form of exercise. Body position tracking through the front camera facilitates various sports, such as fitness, yoga, dancing, gymnastics, etc. For example, the yoga application can employ Core ML to recognize motions and then correct the technique of performing exercises, for instance, on asana positions.


Let’s summarize the basic features of Core ML in fitness & sports applications:

  • analysis of exercise techniques and generating recommendations to improve a method;
  • assessing the quality of the exercises, such as push-ups, squats, pull-ups, and others;
  • analysis of the execution technique in more complex sports such as figure skating or acrobatics;
  • recognition of body position and motion

Yoga app development as an example of using Core ML

Implementing Core ML in a yoga app takes several steps. First, a Machine Learning model must be trained to recognize yoga-specific body movements and positions to develop an application. Model training can be done with the help of Machine Learning libraries such as TensorFlow or Keras.

After the model has been trained, it has to be integrated into the application through CoreML. To do this, developers must define how the application will interact with the machine learning model. A common way to do it is via the Core ML API.  To ensure body position tracking, developers must integrate the front camera with the application. It could be done with the help of various libraries and computer vision tools, such as OpenCV.

The Ready app lets users track their body positions and motions while doing yoga exercises. The application uses machine learning models to analyze the images taken with the front camera and determines the user’s current position and movements. Next to it, the application generates recommendations for improving the technique exercises or correcting mistakes.

The recognition model for yoga poses and movements is usually trained with machine learning methods such as supervised and unsupervised learning. To create a model for supervised learning, it is necessary to prepare a dataset with images of trainees in various positions and movements and put the corresponding class labels on them. This data can be obtained by recording and analyzing videos and using 3D models.

Once the dataset has been prepared, it can be used to train a machine learning model using algorithms such as convolutional neural networks or recurrent neural networks. The learning process will allow the machine learning algorithms to determine the optimal weights and parameters so the model can recognize yoga poses and movements correctly.

coreml development

The model used for unsupervised learning requires the use of unlabeled data. This can be done with the help of clustering methods since they let us find the similarity between images of body positions and movements. This method is helpful for massive datasets and when the class labeling is missing.

As a final recommendation, obtaining a solid data set is insufficient for efficient model training to recognize yoga poses and movements. Another critical factor is the optimal selection of machine learning algorithms and model design and adaptation for devices with limited resources, such as smartphones or tablets.

Bottom Line: Core ML as a mobile-adapted gateway to make a nifty personal workout app

As the machine learning solution for sports & fitness, CoreML has diverse applications due to its

self-training mechanism and easy implementation. To benefit from this mobile-first technology, developers should consider preparing a solid data set, selecting optimal machine learning algorithms, and adjusting the training model for work on devices with limited resources. The use of CoreML in sports greatly facilitates personal workout plans and leverages the possibility of improvement based on continuous feedback.

You may want to read about the Shanghai fitness app.

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