Researchers develop 'digital twins' to teach us how to exercise smarter

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Scientists use computer simulations of joint and muscle movements to teach us to exercise smarter

Researchers have developed computer simulations of joint and muscle movements that can teach us how to exercise smarter and prevent knee pain and further damage.

One in five Australians over the age of 45 suffer from painful and debilitating osteoarthritis, with the knee being the most commonly affected joint.

Dr Claudio Pizzolato from Griffith University is making computer avatars or ‘digital twins’ of individual patients to see how their muscles and joints work.

“We use computer simulations to understand how people use their muscles,” says Dr Pizzolato, working at Griffith University’s Gold Coast Orthopedic Research Alliance.

“Then we can help patients reduce stress and impact on their joints by learning how to move differently when they walk and exercise.”

The digital twins are personalized using medical imaging and help researchers to understand how people use their muscles and how that affects the forces in their knees.

“We are able to calculate, in real-time, the forces acting on a person’s knee during walking and see on a computer screen what is happening inside their knee,” says Dr Pizzolato.

“Using this information we can give patients a personalized exercise program so they can move in ways that are kinder to their joints.”

Researchers hope this technology will not only reduce pain, but also prevent and slow down the progression of osteoarthritis and other musculoskeletal diseases.

Dr Pizzolato won the 2018 Fresh Science Judges’ Award (Queensland) for his work. Fresh Science is a national competition run by Science in Public, which helps early-career researchers find and share their stories of discovery.

Fresh Science Queensland is presented by Econnect Communication and supported by the Queensland Government, Queensland University of Technology, the University of Queensland and Griffith University.

Real time Biofeedback of in vivo knee articular forces

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