By David Lloyd, Claudio Pizzolato, Laura Diamond and David Saxby, Griffith University in Gold Coast
Australian basketballer Maddison Rocci is standing perfectly still in a film studio, more than 100 cameras trained on every part of her body. She stands, arms outstretched, as the shutters click away. Behind the lenses is a team of filmmakers who usually work with Hollywood directors. Today, however, they’re working with scientists.
Once the cameras go quiet, a team of biomechanical engineers and software programmers from Griffith University’s Centre for Biomedical and Rehabilitation Engineering (GCORE) and movie animators from Myriad Studios and Naughty Monkey use data to create a digital twin of Rocci, that replicates her anatomy, inside and out.
This is the ‘digital athlete’. It uses 3D body scans, MRI and motion capture data to give a detailed representation of the body shape, bones, joints, muscles and other soft tissues in Rocci’s daily performance environment. The scientists can now see inside her body as she runs, jumps, twists, turns and shoots. The stress on her muscles and joints is captured, the data helping coaches devise a better training routine, or a tweak in technique.
How to Build a digital twin
For example, coaches can examine in real time when Rocci (or any athlete) does a side-step, which is both a common movement and cause of many anterior cruciate ligament injuries in the knee. The information is both instant and personalised, which is critical, because each athlete will experience different stresses due to their unique physiology.
Maintaining health of and/or repairing joint tissues requires “ideal” loading and strain of tissue. Recent research has shown such ‘biofeedback’ can be achieved by integrating a patient’s personalised digital twin and motion capture with wireless wearable devices.The personalised digital twins break down movements into smaller, predictable movements, and works with neuromusculoskeletal rigid body models and real-time code optimisation and artificial intelligence (AI) or machine learning methods.
Recent work has also shown that laboratory-quality biomechanical measurements and modelling can be even be achieved outside the laboratory, with a smaller number of wearable sensors or computer vision methods. Expect commercial, affordable versions of the technology in the not-to-distant future.
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The ability to non-invasively and accurately predict internal tissue loading in real-time in the real-world has long been considered a holy grail for biomechanists. With the development of this technology, it is possible to imagine that training and rehabilitation may soon be guided by biofeedback systems based on a digital twin of any person’s musculoskeletal system.
Real-time visual biofeedback has enabled people to adjust their knee and hip movements on demand using their innate solutions or trainer directed instructions. Importantly, when patients effectively altered their movements it was associated with clinically meaningful improvements in their hip pain and function.
Optimising performance for athletes is one thing. Creating a digital twin this way has other potential applications, including for the military and the disabled. This could be used to prevent common musculoskeletal injuries that are very common in the military, and for neurorehabilitation of people with spinal cord injuries.
A complete integrated system, called BioSpine, is currently undergoing trials of augmented reality-based training to enable spinal cord injured people to walk in a metaverse, or do actual physical cycling with muscle electrical stimulation and motor assistance all controlled by the person’s thoughts in an immersive augmented reality environment. With development, the technology has the potential to help quadriplegics and paraplegics “walk” again.
The authors wish to acknowledge the important contribution by Duncan Jones and Myriad Studios into this research.