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Can AI-driven health apps, developed with synthetic data, pump up your exercise?
During the COVID-19 pandemic, dwelling health apps had been all the trend. From January by November 2020, roughly 2.5 billion well being and health apps had been downloaded worldwide. That development held and exhibits 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 growth 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 supply “high-performance synthetic data, with a focus on data for human-centric computer vision applications.”
The firm simply introduced a brand new area, Smart Fitness, on its self-service, visible synthetic data platform that helps AI builders produce the data they should analyze folks exercising and practice sensible health gear to “see.”
“At Datagen, our focus is to aid computer vision teams and accelerate their development of human-centric computer vision tasks,” Ofir Zuk, CEO of Datagen, instructed VentureBeat. “Almost every use case we see in the AI space is human-related. We are specifically trying to solve and help understand the interconnection between humans and their interaction with surrounding environments. We call it human in context.”
Synthetic visible data represents health environments
The Smart Fitness platform supplies 3D-annotated synthetic visible data within the type of video and pictures. This visible data 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 data to iterate on their mannequin and enhance its efficiency shortly. For instance, in instances of pose estimation evaluation, a bonus the Smart Fitness platform supplies is the potential to shortly simulate totally different digicam sorts for capturing a wide range of differentiated train synthetic data.
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 among 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 positioned 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 drawback is, pc imaginative and prescient engineers want huge quantities of visible data to coach AI for health evaluation when utilizing a way like pose estimation. Data involving people performing workouts in varied varieties and interacting with a number of objects is very complicated. The data should even be high-variance and sufficiently numerous to keep away from bias. Collecting correct data which covers such a range is almost inconceivable. On high of that, handbook annotation is gradual, liable to human error, and costly.
While an appropriate degree of accuracy in 2D pose estimation has already been reached, 3D pose estimation lacks by way of producing correct mannequin data. That is very true for inference from a single picture and with no depth data. Some strategies make use of a number of cameras pointed on the particular person, capturing data from depth sensors to realize better predictions.
However, a part of the issue with 3D pose estimation is the dearth of huge annotated datasets of individuals in open environments. For instance, massive datasets for 3D pose estimation resembling Human3.6M had been captured fully indoors to eradicate visible noise.
There is an ongoing effort to create new datasets with extra numerous data relating to environmental situations, clothes selection, sturdy articulations, and different influential elements.
The synthetic data answer
To overcome such issues, the tech trade is now extensively utilizing synthetic data, a kind of data produced artificially that may carefully mimic operational or manufacturing data, 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 data; can be utilized to customise data to match situations that actual data doesn’t permit; and it permits for giant coaching datasets with out requiring handbook labeling of data.
According to a report by Datagen, using synthetic data reduces time-to-production, eliminates privateness considerations, supplies decreased bias, annotation and labeling errors, and improves predictive modeling. Another benefit of synthetic data is the power to simply simulate totally different digicam sorts whereas producing data to be used instances resembling 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 numerous environments and situations – in a fraction of the time.
“With the prowess of synthetic data, teams can generate all the data they need with specific parameters in a matter of a few hours,” Zuk mentioned. “This not only helps retrain the network and machine learning model, but also allows you to get it fine-tuned in no time.”
In addition, he defined, the Smart Fitness platform optimizes your capacity to seize hundreds of thousands of considerable visible train data, eliminating the repetitive burden of capturing every aspect in particular person.
“Through our constantly updating library of virtual human identities and exercise types, we provide detailed pose information, such as locations of the joints and bones in the body, that can help analyze intricate details to enhance AI systems,” he mentioned. “Adding such visual capabilities to fitness apps and devices can significantly improve the way we see fitness, enabling organizations to provide better services both in person and online.”
Fitness AI and synthetic data within the enterprise
According to Arun Chandrasekaran, distinguished VP Analyst at Gartner, synthetic data is, thus far, an “emerging technology with a low degree of enterprise adoption.”
However, he says it’ll see rising adoption to be used instances for which data have to be assured to be nameless or privateness have to be preserved (resembling medical data); augmentation of actual data, particularly the place prices of data assortment are excessive; the place there’s a must stability class distribution inside current coaching data (resembling with inhabitants data), and rising AI use instances for which restricted actual data 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 particular interest will be the ability to boost data quality, cover the wide gamut of scenarios and privacy preservation during the ML training phase,” he mentioned.
Zuk admits that it’s nonetheless early days for bringing AI and synthetic data, and even digital applied sciences total, into the health area.
“They are very non-reactive, very lean in terms of their capabilities,” he mentioned. “I would say that adding these visual capabilities to these fitness apps, especially as people exercise more in their own home, will definitely improve things significantly. We clearly see an increase in demand and we can just imagine what people can do with our data.”
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