Transforming how humans interact with technology

Illustration of a human walking

Human-design centred approach in using technology to understand the underlying clinical/physiological data

A significant proportion of the adult population commonly suffer from musculoskeletal (MSK) pain, in particular in the lower back and knee. This type of pain is caused by overuse, injury or inflammation, and is associated with a detrimental impact on quality of life and the ability to engage in exercise. Musculoskeletal conditions, such as those caused by arthritis, are the leading contributor to disability worldwide and account for £4.76 billion of the annual NHS spend. MSK conditions can lead to early retirement from work, reduce accumulated wealth and limit participation in social roles. Multiple MSK conditions are characterised and exacerbated by disordered gait and can be identified and managed by clinical gait analysis and gait re-education. However, this relies on facilities that are expensive, requires extensive training to use, needs the patient to travel to the clinic, and often produces outcomes that are variable and inefficient. The early detection, diagnosis and treatment of such conditions will improve an individual’s quality of life and reduce the socio-economic impact.

In collaboration with a UK-based SME and with funding support from EIRA, researchers from the University of Kent, Dr Lex Mauger of School of Sport and Exercise Sciences and Dr Caroline Li of School of Computing, led a research study that resulted in the development of ‘smart’ pressure sensitive insoles to detect MSK disordered walking gait, using an ink printed onto thin, flexible textiles that can be placed under a shoe insole.

The study compared the walking gait of participants whilst wearing smart-soles in a non-pain condition, and then when acute MSK pain was induced. In a translational approach, the smart insoles were wirelessly connected to an app running a Machine Learning algorithm. Data acquired from smart-soles in no-pain conditions was recorded and analysed using machine learning AI and then compared to the measurements when pain was induced. The team were able to detect when subtle changes in walking gait arose from lower limb MSK pain, which demonstrated a proof-of-principle that the smart-soles could be used to detect changes in walking gait arising from MSK pain. By taking a multi-disciplinary approach and linking business with academia, this project has helped establish a proof-of-concept for the insoles and pathed the way for follow-on work.

The technology is at an early demonstrator phase that the project team, through their ongoing collaborative work, hope to further develop and refine for use in clinical and sporting populations where identifying changes in gait facilitates the treatment and management of chronic conditions, such as the rehabilitation from acute injury, or enhancement of athletic performance.

The vision is that the insoles will fulfil an unmet need by being prescribed to out-patients in a single clinic-based gait analysis appointment. Following this, patients’ gait could be monitored out of clinic during their daily activities. Data could be fed back to a clinical lead to monitor patient progress, and an accompanying app would provide the patient with real-time feedback and advice on their gait pattern, helping them to self-manage their condition, significantly mitigating the economic and quality of life impact.

This is an example of the interdisciplinary research at the University of Kent bringing together the expertise of Dr. Lex Mauger from School of Sport and Exercise Sciences and Dr. Caroline Li from School of Computing.

Dr. Lex Mauger is an exercise physiologist. His research predominantly focusses on furthering our understanding of how the pain that we experience during intense exercise impacts our capacity for exercise and sporting performance. To explore this experimentally he artificially increases the intensity of pain experienced during exercise using an intramuscular injection of saline and then uses techniques like transcranial magnetic stimulation and peripheral electrical stimulation to identify if and how this pain causes fatigue. His other main project in this area is funded by the World Anti-Doping Agency (WADA) and investigates the performance enhancing effects of analgesics (pain killers) in elite endurance sport.

Dr Caroline Li‘s research in using brain imaging technologies and AI to generate images could be transformative in the way in which humans interact with technology to signal their preferences in design. Her research focuses on signal processing and its applications in body sensors, including: EEG-based biomarker discovery for brain diseases, neurofeedback applications for medical and sport applications and brain computer interface. She is also working on signal processing methods including: adaptive filtering, tracking methods and machine learning methods for pattern classification.

Dr Caroline Li is also collaborating with London NHS hospital trusts and researchers in China on a clinical trial to study the use of a rheumatoid arthritis drug in improving COVID-19 outcomes in hospitalised COVID-19 patients. The £1.3 million project is funded by the charity LifeArc. She will lead the AI part of the project to understand the underlying clinical/physiological data. The focus will be on exploring data science and developing interpretable AI models in the clinical trial to discover the potential of the repurposed drug.