Fitness AI: How synthetic data powers better workouts

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Can AI-driven health apps, developed with synthetic data, pump up your exercise? 

During the COVID-19 pandemic, house health apps had been all the craze. From January via November 2020, roughly 2.5 billion health and fitness apps had been downloaded worldwide. That development held and reveals no indicators of slowing down, with new data predicting progress from $10 million in 2022 to $23 million by 2026. 

As extra folks use health apps to coach and monitor their improvement and efficiency, health apps are more and more utilizing AI to energy their choices by offering AI-based exercise evaluation, incorporating applied sciences together with pc imaginative and prescient, human pose estimation, and pure language processing methods.

Tel-Aviv-based Datagen, which was based in 2018, claims to offer “high-performance artificial knowledge, with a concentrate on knowledge for human-centric pc imaginative and prescient purposes.”

The firm simply introduced a brand new area, Smart Fitness, on its self-service, visible synthetic data platform that helps AI builders produce the information they should analyze folks exercising and prepare sensible health tools to “see.” 

“At Datagen, our focus is to assist pc imaginative and prescient groups and speed up their improvement of human-centric pc imaginative and prescient duties,” Ofir Zuk, CEO of Datagen, informed VentureBeat. “Almost each use case we see within the AI house is human-related. We are particularly attempting to resolve and assist perceive the interconnection between people and their interplay with surrounding environments. We name it human in context.” 

Synthetic visible knowledge represents health environments

The Smart Fitness platform offers 3D-annotated artificial visible knowledge within the type of video and pictures. This visible knowledge precisely represents health environments, superior movement, and human-object interactions for duties associated to physique key level estimation, pose evaluation, posture evaluation, repetition counting, object identification and extra. 

In addition, groups can use the answer to generate full-body in-motion knowledge to iterate on their mannequin and enhance its efficiency shortly. For instance, in instances of pose estimation evaluation, a bonus the Smart Fitness platform offers is the potential to shortly simulate totally different digicam varieties for capturing a wide range of differentiated train artificial knowledge.

Source: Datagen

Challenges to coaching AI for health

Pose estimation, which is a pc imaginative and prescient approach that helps decide the place and orientation of the human physique with a picture of an individual, is likely one of the distinctive options that AI has to supply. It can be utilized in avatar animation for synthetic actuality, for instance, in addition to markerless movement seize and employee pose evaluation. 

To accurately analyze posture, it’s essential to seize a number of photographs of the human actor with its interacting setting. A educated convolutional neural community then processes these photographs to foretell the place the human actor’s joints are situated within the picture. AI-based health apps typically use the system’s digicam, recording movies as much as 720p and 60fps to seize extra frames throughout train efficiency. 

The downside is, pc imaginative and prescient engineers want huge quantities of visible knowledge to coach AI for health evaluation when utilizing a method like pose estimation. Data involving people performing workouts in numerous types and interacting with a number of objects is extremely advanced. The knowledge should even be high-variance and sufficiently various to keep away from bias. Collecting correct knowledge which covers such a spread is almost inconceivable. On prime of that, handbook annotation is gradual, vulnerable to human error, and costly. 

While an appropriate stage of accuracy in 2D pose estimation has already been reached, 3D pose estimation lacks by way of producing correct mannequin knowledge. That is particularly true for inference from a single picture and with no depth info. Some strategies make use of a number of cameras pointed on the particular person, capturing info from depth sensors to realize higher predictions. 

However, a part of the issue with 3D pose estimation is the shortage of huge annotated datasets of individuals in open environments. For instance, giant datasets for 3D pose estimation corresponding to Human3.6M had been captured completely indoors to remove visible noise.

There is an ongoing effort to create new datasets with extra various knowledge relating to environmental circumstances, clothes selection, robust articulations, and different influential elements.

The artificial knowledge resolution

To overcome such issues, the tech trade is now broadly utilizing artificial knowledge, a sort of knowledge produced artificially that may carefully mimic operational or manufacturing knowledge, for coaching and testing synthetic intelligence methods. Synthetic data gives a number of vital advantages: It minimizes the constraints related to using regulated or delicate knowledge; can be utilized to customise knowledge to match circumstances that actual knowledge doesn’t enable; and it permits for big coaching datasets with out requiring handbook labeling of knowledge.

According to a report by Datagen, using artificial knowledge reduces time-to-production, eliminates privateness considerations, offers diminished bias, annotation and labeling errors, and improves predictive modeling. Another benefit of artificial knowledge is the flexibility to simply simulate totally different digicam varieties whereas producing knowledge to be used instances corresponding to pose estimation. 

Exercise demonstration made easy

With Datagen’s sensible health platform, organizations can create tens of hundreds of distinctive identities performing a wide range of workouts in several environments and circumstances – in a fraction of the time. 

“With the prowess of artificial knowledge, groups can generate all the information they want with particular parameters in a matter of some hours,” Zuk mentioned. “This not solely helps retrain the community and machine studying mannequin, but in addition lets you get it fine-tuned very quickly.”

datagen's Smart Fitness platform dashboard
Source: Datagen

In addition, he defined, the Smart Fitness platform optimizes your capacity to seize hundreds of thousands of considerable visible train knowledge, eliminating the repetitive burden of capturing every aspect in particular person. 

“Through our continually updating library of digital human identities and train varieties, we offer detailed pose info, corresponding to areas of the joints and bones within the physique, that may assist analyze intricate particulars to boost AI methods,” he mentioned. “Adding such visible capabilities to health apps and gadgets can considerably enhance the way in which we see health, enabling organizations to offer higher companies each in particular person and on-line.”

data gen Smart Fitness platform post tracking gif of person doing lunge
Source: Datagen

Fitness AI and artificial knowledge within the enterprise

According to Arun Chandrasekaran, distinguished VP Analyst at Gartner, artificial knowledge is, to date, an “rising know-how with a low diploma of enterprise adoption.” 

However, he says it would see rising adoption to be used instances for which knowledge have to be assured to be nameless or privateness have to be preserved (corresponding to medical knowledge); augmentation of actual knowledge, particularly the place prices of knowledge assortment are excessive; the place there’s a must steadiness class distribution inside current coaching knowledge (corresponding to with inhabitants knowledge), and rising AI use instances for which restricted actual knowledge is on the market. 

Several of those use instances are key for Datagen’s worth proposition. When it involves enhancing the capabilities of sensible health gadgets or apps, “of specific curiosity would be the capacity to spice up knowledge high quality, cowl the extensive gamut of situations and privateness preservation in the course of the ML coaching part,” he mentioned. 

Zuk admits that it’s nonetheless early days for bringing AI and artificial knowledge, and even digital applied sciences total, into the health house. 

“They are very non-reactive, very lean by way of their capabilities,” he mentioned. “I might say that including these visible capabilities to those health apps, particularly as folks train extra in their very own house, will certainly enhance issues considerably. We clearly see a rise in demand and we are able to simply think about what folks can do with our knowledge.” 

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