Press release
For immediate release
New AI model could improve digital coaching and rehab
A novel AI system capable of recognising yoga poses with high accuracy could pave the way for more effective digital coaching tools, rehab platforms and movement-monitoring applications.
A new study, co-authored by the University of East London (UEL), analysed four novel AI models and their ability to identify yoga poses; the researchers found that their best-performing model, Hierarchichal CoAtNet 1, achieved accuracy levels of over 93% during testing, significantly outperforming previous models.
Dr Laura Vanderbloemen, Senior Lecturer at UEL and co-author of the study, said:
“This research shows how AI can be used to make movement-based coaching and rehabilitation more accessible. By recognising yoga poses with a high degree of accuracy and providing feedback in real time, these systems could help support people who cannot easily access in-person instruction, whether because of their location, mobility challenges or cost. It demonstrates the potential for AI, computer vision and robotics to expand access to health and wellbeing tools for a wider range of people.”
The AI model incorporates in its learning the natural hierarchical relationships between yoga poses, so, rather than treating each pose as an isolated category, the AI model identifies broader pose families, before learning about specific variations, similarly as to how humans understand and categorise movement.
This system could have practical applications in the real-world and deliver feedback in real time, as the model processed images in approximately 16 to 17 milliseconds per batch under testing conditions and achieved real-time speeds of around 65 to 70 frames per second during streaming inference.
The researchers believe this model could support a wide range of applications that require accurate monitoring of physical activity, along with providing feedback that yoga instructors, physios and healthcare professionals could use to better understand posture quality and movement patterns and improve personalised coaching and rehabilitation.
The research was a collaborative effort involving researchers from the University of East London, Nirma University, Imperial College London and Doctor On Click, bringing together expertise in artificial intelligence, computer vision, digital health and movement science.
ENDS
Notes to editors
Dr Laura Vanderbloemen is available for interviews, please contact pressoffice@uel.ac.uk to arrange.
The full paper is available at: doi.org/10.1038/s41598-026-54558-1
About the University of East London: The University of East London (UEL), founded in 1898, is a careers-first university dedicated to empowering students with the skills, experience and networks they need to thrive in a changing world. With over 40,000 students from more than 160 countries, UEL places social mobility, inclusive excellence and real-world impact at the heart of its mission. Based in Stratford and the Royal Albert Dock, UEL is shaping a healthier, fairer and more sustainable future through transformative education, research and innovation. In 2026, UEL is celebrating another Year of Health, which includes launching a new Health Campus that will address health inequalities and foster innovation in the sector. For more information, visit www.uel.ac.uk.
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