AI and Synthetic Data’s Analysis of Human Movement

Can applications that are AI-driven and built with synthetic data examine the specifics of human movement?

Fitness applications are progressively using AI to control their offerings by supplying AI-based workout analysis, integrating technologies like human pose estimation, computer vision, and natural language processing techniques. As more individuals use fitness applications to train and track their progress and performance, this trend will only continue.

AI fitness training challenges

One of the special applications of AI poses estimation, a computer vision approach that aids in determining the position and orientation of the human body from an image of a person. It can be utilized, for instance, in markerless motion capture, worker position analysis, and avatar animation for virtual reality.

It is required to take numerous pictures of the human actor and its surrounding environment to properly analyze posture. The joints of the human actor are then identified in these photos using a trained convolutional neural network.

AI-based fitness apps typically take advantage of the camera on the device to record films up to 720p and 60fps to capture more frames while an exercise is being performed.

The issue is that when utilizing a method like a posture estimation, computer vision experts require enormous volumes of visual data to train AI for fitness assessments. Data involving humans engaging in many types of exercise and interacting with several items is quite complicated. To prevent bias, the data must also have high variance and be sufficiently broad. It is practically impossible to gather accurate data that covers such a wide range. Additionally, hand annotation is costly, expensively slow, and subject to human error.

While 2D pose assessment has already attained an acceptable degree of accuracy, 3D pose estimation falls short in terms of producing reliable model data.

This is especially true when inference is made using only a single image and without any depth data. Some techniques use many cameras directed at the subject to gather depth sensor data for more accurate predictions.

However, the lack of significant annotated datasets of individuals in open spaces contributes to the issue with 3D posture estimation. For instance, to remove visual noise, huge datasets for 3D pose estimation, like Human3.6M, were fully indoors during capture.

There is a continuing attempt to develop additional datasets with more varied data about various types of apparel, the environment, well-articulated speech, and other important elements.

Environments for fitness are represented by synthetic visual data.

The Smart Fitness platform offers synthetic visual data that has been 3D-annotated in the form of photos and videos. For tasks including body key point estimate, pose analysis, repetition counting, posture analysis, object identification, and other activities, this visual data reflects fitness surroundings, advanced motion, and human-object interactions with accuracy.

Teams can also utilize the solution to produce full-body in-motion data to swiftly iterate on and enhance the performance of their model. For instance, the Smart Fitness platform offers the advantage of being able to easily mimic several camera types for recording a variety of diverse exercise synthetic data in cases of posture estimation analysis.

The synthetic data solution

For testing and training AI systems, the IT sector today frequently uses synthetic data, a sort of data created artificially that can closely resemble operational or production data. Significant advantages of synthetic data include the following: It reduces the restrictions related to using regulated or sensitive data, allows for massive training datasets without the need for human labeling of data, and can be used to tailor data to match situations that real data does not allow.

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