System which ‘recognises’ how people walk could improve disease and injury treatment

Scientists have developed a system which is able to recognise similarities in how people walk and run – and which could lead to improved treatment and guidance for conditions such as leg amputations, Parkinson’s disease and strokes.

A team at Nottingham Trent University has used statistical methods to develop a ‘machine-learning algorithm’ capable of identifying and remembering the most specific characteristics of how people – or groups of people – move.

It means that, as data is built up regarding a set of patients’ movement, it will be possible to identify issues early and give individuals tailored and improved guidance or treatment relating to their particular condition.

The researchers believe that in the future the system might also be able to play a role in disease diagnosis, as it will be possible to identify subtle changes in patients which could indicate the early onset of certain conditions.

Clinicians currently use a series of tools, such as video analysis, to assess a person’s gait. They then use the data they obtain to produce a graph or score to interpret how good someone’s gait is.

Whilst such approaches are useful, researchers argue that assessments can be subjective and may dismiss some of the full complexity of data relating to movement.  

New methods are therefore needed to remove the potential for human error in treatment and guidance and to ensure that important details are not missed.

The new system, developed by researchers in the university’s School of Science and Technology, is able to account for all of the features of movement in relation to hip, knee and ankle joints and the forces experienced during gait such as walking and running.

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Red shows the participant's gait in shoes, blue shows gait whilst barefoot

It involves using 3D motion capture to record the person moving, before the data is fed into the machine-learning algorithm.

The team says it could be used to help evaluate current treatments, recommend new ones, and observe how a condition improves or worsens over time.

As part of a study, reported in the journal PLOS One, researchers tested the method with two groups of participants. The algorithm was able to predict with up to 94% accuracy whether people had run with or without shoes on.

“Our system is highly sensitive to the most subtle of changes and could help to ensure more timely, accurate and reliable treatment across a range of conditions,” said lead scientist Dr Cleveland Barnett, a biomechanics researcher at Nottingham Trent University.

He said: “It could be used to monitor more objectively the gait of patients, for instance, or even pinpoint factors which contribute to falls among vulnerable groups, such as the elderly, so that risks are identified as early as possible.

“Our work could make a real impact in being able to help clinicians and researchers recognise characteristics similar to – or different from – a particular group. Importantly, it can be implemented to find distinguishing features even when the sample size is small, as can be the case in gait analysis.”

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    Nottingham Trent University (NTU) has been awarded the highest, gold, rating in the Government’s Teaching Excellence Framework for its outstanding teaching and learning.

    NTU is one of the largest UK universities with nearly 28,000 students and more than 3,500 staff across four campuses, contributing £496m to the UK economy every year. It is one of the most environmentally-friendly universities, containing some of the country’s most inspiring and efficient award-winning buildings.

    The University is passionate about creating opportunities and its extensive outreach programme is designed to enable Nottingham Trent to be a vehicle for social mobility. NTU is the sixth biggest recruiter of students from disadvantaged backgrounds in the country and 95.6% of the its graduates go on to employment or further education within six months of leaving.

    NTU is home to world-class research, winning The Queen’s Anniversary Prize in 2015 - the highest national honour for a UK university. It recognised the University’s pioneering projects to improve weapons and explosives detection in luggage, enable safer production of powdered infant formula and combat food fraud.

    With an international student population of approximately 2,600 from around 100 countries, the University prides itself on its global outlook.

System which ‘recognises’ how people walk could improve disease and injury treatment

Published on 21 September 2017
  • Subject area: Sciences including sport sciences
  • Category: Press; School of Science and Technology

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