In India, a nation where Badminton is one of the most popular sports, players at dozens of academies have been harnessing the power of AI to get an edge on their competitors.
A startup company in the city of Bengaluru is making this possible with an artificial intelligence model that analyzes video footage. VISIST.AI’s program will inform the player about flaws in their posture, grip, swing velocity and overall technique.
More than 70 academies in the city have been employing the technology, many of which are overseen by the Padukone School of Badminton. VISIST.AI initiated a partnership with Padukone in late July, enabling its sports tech to make its way to others parts of India as well.
“Sports academies are evolving into tech-enabled talent incubators,” said VISIST.AI CEO, “utilizing AI-driven analysis alongside human oversight for transparent athlete development pathways.”
Chintada has explained that his company’s program sends assessment reports and intelligent journal entries to badminton coaches, thereby acting like an assistant coach. He claims that they have praised the AI model’s capabilities and said that their coaching ability has been elevated because of it.
This machine learning video analysis technology also aims to help prevent injuries.
The Indian Institute of Science’s (IISC) Foundation for Science, Innovation and Development has supported VISIST.AI since its founding in 2020 through financial backing and research assistance. The IISC is India’s number one research institute.
Badminton is the second most popular sport in India after cricket.
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AI badminton analysis the subject of Nature study in March
Chinese researchers recently published an investigation on this subject in Scientific Reports titled “Predicting Badminton Outcomes Through Machine Learning and Technical Action Frequencies.”
They used an artificial intelligence model to predict match outcomes with a high level of accuracy. Like VISIST.AI’s video analytics model, they say it provides valuable insights for coaches.
“This platform enables players, coaches, and researchers to input player characteristics and receive strategic recommendation through an intuitive interface,” the authors wrote, “further leveraging the machine learning model’s capabilities to support tactical decision-making before badminton competitions.”
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