MedTech Solutions

MedTech Solutions undertakes collaborative research with partners across Lancashire and beyond.

We support the design and development of medical devices and related interventions, provide insights into the efficacy and effectiveness of such solutions, and accelerates the pace at which these reach healthcare providers to bring benefits to patients and service users.

MedTech Solutions formed in June 2020 bringing together a collective of scientists, engineers and technicians from across the University to help solve problems faced by the NHS and the wider healthcare sector.

Our group includes experts in mechatronics and intelligent machines, additive manufacturing, design and development of medical devices, computer vision and machine learning, psychology, health sciences, and microbial interactions with hosts and surfaces. Our team has extensive experience and a successful track record of grant capture, assisting small and large companies, and working alongside the NHS and other healthcare partners.

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News updates

England footballer backs device created following UCLan and Manchester Metropolitan research

England footballer Harry Maguire has thrown his support behind a new rehabilitative device – created following research by UCLan and Manchester Metropolitan University.

Harry Maguire is the latest professional footballer to support the device, after Wayne Rooney.

Engineering Innovation Centre

Med-Tech Solutions are embedded in the Engineering Innovation Centre (EIC) located on our Preston campus which brings together world-leading research, leading business minds and inspiring teaching in a spirit of collaboration and discovery.

Within the EIC our MedTech Solutions group offers a wide-ranging portfolio of industry engagement opportunities. When you work with us, you have access to specialist staff, state-of-the-art engineering facilities, industry leading technologies and the expertise of our researchers inside a first-class facility.

Find out more

Associated groups

Computer Vision, Machine Learning and Medical Image Computing (CVML)

The ongoing emphasis is on developing new vision and machine learning algorithms and their transfer to real-world applications. The particular areas of interest include: Bayesian methodology for data modelling, pattern recognition and tracking; statistical shape analysis; deformation modelling for model-based recognition, segmentation and registration; medical imaging; intelligent energy management; data mining; and applications of deep learning.

Allied Health Research unit (AHRu)

The Allied Health Research unit’s work includes research across the health care professions to help answer important and clinically relevant questions, with research spanning the innovation pathway from intervention development and evaluation through to implementation.

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Contact us

Professor Jim Richards
Director of Research for the Faculty of Allied Health and Wellbeing
School of Health, Social Work and Sport

Professor Richards' research includes the clinical application of biomechanics, the development of new assessment tools for chronic disease, conservative and surgical management of orthopaedic and neurological conditions, and the development of evidence based approaches for impro...