TEX-CTS: Textile-Embedded Pressure Mapping for Optimisation of Non-Invasive Carpal Tunnel Therapy
Supervisor: Dr Haotian Chen
Industry Partner: Pressure Profile Systems UK
School: Engineering
Description:
Carpal tunnel syndrome (CTS) is one of the most common peripheral neuropathies, affecting millions of individuals worldwide, particularly those engaged in repetitive manual tasks, prolonged computer use, or assembly-line work. It is a major contributor to work-related musculoskeletal disorders and can lead to pain, numbness, reduced hand strength, sleep disturbance, and loss of productivity. As work patterns increasingly involve sustained wrist activity in both industrial and digital environments, there is growing demand for effective, accessible, and non-invasive therapeutic solutions that can be used in daily life without disrupting normal function.
Non-invasive wrist support devices are frequently prescribed to modify mechanical loading at the wrist and reduce median nerve compression. However, optimisation of such orthotic devices remains largely empirical, relying on subjective comfort feedback rather than quantitative measurement of pressure distribution at the human–device interface. A clearer understanding of how applied mechanical tension translates into spatial pressure patterns is essential for improving device comfort, safety, and therapeutic effectiveness.
This project will develop a textile-integrated capacitive pressure sensing glove capable of mapping the interface pressures generated by a next-generation non-invasive CTS support device co-developed with an industry partner, PPS UK, in Glasgow. The aim is to enable quantitative, data-driven optimisation of device geometry and applied tension through high-resolution spatial pressure measurement.
The student will fabricate flexible capacitive pressure sensors using functional conductive and dielectric inks deposited via screen printing onto textile substrates. The sensing elements will be arranged in an array configuration to provide spatially resolved pressure mapping across the wrist interface. Particular attention will be given to achieving high sensitivity, linear response, and fast signal recovery suitable for wearable biomedical applications.
Following fabrication, the student will characterise the electromechanical performance of the sensors through controlled mechanical loading experiments, evaluating sensitivity, linearity, hysteresis, repeatability, and response time. The sensing array will then be integrated with a capacitive readout circuit and microcontroller-based data acquisition system to enable real-time pressure mapping.
Experimental testing will examine how different levels of applied mechanical tension influence spatial pressure distribution across the wrist. The resulting data will provide quantitative insight into loading characteristics and inform design recommendations for improved orthotic geometry and tension control, demonstrating the feasibility of integrating smart textile electronics into non-invasive therapeutic device optimisation.