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‘Sensor suits’ and AI combined to monitor movement disorders

Professional Engineering

Luchen Li, wearing the sensor suit, and research leader Professor Aldo Faisal
Luchen Li, wearing the sensor suit, and research leader Professor Aldo Faisal

Wearable ‘sensor suits’ and AI technology have been combined to create digital twins of medical patients, researchers have said, enabling precise predictions of how movement disorders will progress.

Led by Professor Aldo Faisal from Imperial College London, the multi-disciplinary team said it was able to identify clear movement patterns, predict future disease progression and significantly increase the efficiency of clinical trials.

The work, carried out across two studies, focused on two different disorders – Duchenne muscular dystrophy (DMD) and Friedreich's ataxia (FA). The rare, degenerative and genetic diseases affect movement and eventually lead to paralysis. There are currently no cures for either disease, but researchers hope the results from their work could “significantly” speed up the search for new treatments.

“Our approach gathers huge amounts of data from a person’s full-body movement – more than any neurologist will have the precision or time to observe in a patient,” said Professor Faisal.

“Our AI technology builds a digital twin of the patient and allows us to make unprecedented, precise predictions of how an individual patient’s disease will progress. We believe that the same AI technology working in two very different diseases shows how promising it is to be applied to many diseases and help us to develop treatments for many more diseases even faster, cheaper and more precisely.”

In the DMD-focused study, researchers and clinicians at Imperial, Great Ormond Street Hospital and University College London trialled a sensor suit with motion capture technology on 21 children with DMD and 17 healthy age-matched controls. The children wore the sensors while carrying out standard clinical assessments and their everyday activities.

In the FA study, teams at Imperial, the Ataxia Centre, UCL Queen Square Institute of Neurology and the MRC London Institute of Medical Sciences worked with patients to identify key movement patterns and predict genetic markers of disease.

In both studies, the data from the sensors was collected and fed into AI technology to create individual avatars and analyse movements. “This vast data set and powerful computing tool allowed researchers to define key ‘movement fingerprints’ seen in children with DMD as well as adults with FA,” the research announcement said. “Many of these AI-based movement patterns had not been described clinically before in either DMD or FA.”

The team discovered that the new technique could significantly improve predictions of how individual patients’ disease would progress over six months, compared to current gold-standard assessments. Such precise predictions could allow clinical trials to run more efficiently, enabling quicker access to novel therapies and more precise drug dosages.

In the DMD study, researchers showed that the method could reduce the number of children required to detect if a novel treatment is working to just a quarter of those required with current methods. Similarly, in the FA study, the team showed they could achieve the same precision with 10 patients instead of 160-plus.

“This AI technology is especially powerful when studying rare diseases, when patient populations are smaller. In addition, the technology allows [us] to study patients across life-changing disease events, such as loss of ambulation, whereas current clinical trials target either ambulant or non-ambulant patient cohorts,” the announcement said.

Co-author of both studies Professor Richard Festenstein, from the MRC London Institute of Medical Sciences and department of brain sciences at Imperial, said: “Patients and families often want to know how their disease is progressing, and motion capture technology combined with AI could help to provide this information. We’re hoping that this research has the potential to transform clinical trials in rare movement disorders, as well as improve diagnosis and monitoring for patients above human performance levels.”

As well as monitoring patients in clinical trials, the technology could one day potentially be used to monitor or diagnose a range of common diseases that affect movement behaviour, such as dementia, stroke or orthopaedic conditions.

The two studies were published in Nature Medicine.

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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.


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