Author: cdt-students

Title: Bringing the assessment of biomechanical interventions into clinical practice and use in knee osteoarthritis.

Supervisors: Dr Carina Price

                      Professor Richard Jones

Background: The knee joint experiences higher loads therefore it is affected the most and is commonly affected by knee osteoarthritis (KOA). There is no cure for knee osteoarthritis therefore its management is crucial. Biomechanical interventions such as orthotic devices are commonly recommended to prevent the progression of the disease. However, not all patients experience a decrease in EKAM which is an important objective of biomechanical interventions. An increased EKAM is linked to decrease in function and progression of knee osteoarthritis. Gold standard methods such as 3D marker-based motion capture are used to assess the effects of biomechanical interventions. However, there are many limitations of marker-based systems, they are inaccessible to clinicians as well as expensive. Alternative methods are required to objectively assess interventions in clinic without expensive and time-consuming systems. Plantar pressure measurement systems can be used to evaluate interventions instead of gait laboratories, this is a common method of assessment in diabetic patients. Another potential alternative method to assess the effects of interventions is markerless motion capture systems.  

Aim: Assessing the ability of plantar pressure and markerless systems to identify biomechanical responders (decreased EKAM) compared to 3D marker-based systems. To explore the reliability of the different systems and their feasibility to assess an intervention. This will help to identify an objective lower-cost system for the assessment of interventions in knee osteoarthritis patients in clinic.   

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The title of my PhD is “K Level Classification through real-world activity assessment of lower limb amputees”. My supervisors are Dr Sibylle Thies, Professor Malcolm Granat and Sir Saeed Zahedi.

The aim of the PhD is to create an objective assessment method that can enhances the reliability and consistency of lower limb prosthetic patient’s K level classification. The objective method will be a sensor based system that can measures variables which characterize aspects of human movement, and to use these to classify the patient’s activity levels in the real world. From reviews of past literature and interviews with clinical experts the most desired variables to measure are cadence, step count, walking with or without an aid and if the patient can travers different terrain. The system will need to be either attached or built into the prosthesis and be as cost efficient as possible to meet the feasibility requirements

My hobbies are really just ice hockey.

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Title: Neural Driven Motion Synthesis for Assistive Technology

Supervisor: Dario Farina

Host institution: Imperial College London Project summary (abstract style):
Unintuitive and inefficient control schemes of electronic lower limb Prosthetic and Orthotic (P&O) devices limit the number of cases where they are a worthwhile intervention. Furthermore, commercial control algorithms are challenged by uneven terrain and turning during walking. Muscle signals contain information about the intended upcoming motion, which is required for responsive control strategies. Predicting the motion directly from these signals is prone to producing unfeasible or unsafe movement. Instead, an abstract representation of the underlying intent can be first estimated from muscle signals, from which stable motion is synthesised in a separate step. This project investigates data-driven, hierarchical control schemes targeting lower limb P&O devices.

Project summary (biography style):
My project investigates the requirements for synthesising walking motion. I’m quantifying the available information from muscle signals for lower limb device control,  and I’m looking for hierarchical systems, intent representations and control strategies that increase the reliability and versatility of the generated movement.

Other interests:
Tabletop gaming, hiking, doodling

Previous Degree: MEng degree in Biomedical Engineering – University of Glasgow

Host Institution: University of Strathclyde

PhD Topic: Developing an outcome measurement tool for assessing real world use of partial hand prosthetics

​Supervisors: Sarah Day and Arjan Buis

Hobbies and Interests: Watching football, hillwalking, travelling and socialising with friends

Title

Intelligent feedback to assist upper limb rehabilitation

Supervisors

Prof. Liudi Jiang – University of Southampton

Prof JW McBride – University of Southampton

Detailed information of your project

Upper limb rehabilitation aims to reduce the barriers of daily-living for both upper-limb amputees and those with movement disorders, such as a stroke. These conditions dramatically affect the patient’s quality of life and they can be dependent on rehabilitation technology to live independently. Many clinically used upper limb rehabilitation devices already make use of electromyography (EMG) signals as a control mechanism, relying on user-based sensors to completed feedback loops, for instance from visual cues. There is a growing body of research into implementing intelligent feedback from electronic sensors to either replace or compliment the user-based feedback. By doing this the system has the capabilities of producing more accurate, more precise and faster responses however evidence for this in current systems is sparse and further developments are needed before this technology becomes a clinical standard.

The overall aim of this PhD research is to develop advanced technologies and signal processing strategies for intelligent feedback with a view to assisting upper limb rehabilitation.  The project will build upon an in-depth understanding of upper limb biomechanics and state of the art assistive technologies, such as myoelectric prostheses. The research will review current electromyography (EMG), biomimetic sensors, signal processing and control techniques with the aim to combine and develop these systems to create more stability, precision and accuracy within the upper limb rehabilitation device.

Hobbies and interests

I am enjoying learning new skills so have recently been learning British Sign Language and brushing up my French. I am also a part of the Southampton Philharmonic Choir where I sing first Soprano. I also enjoy performing stand-up comedy and perform a set about once a month in local venues. I also enjoy reading, crafts, and water sports.

My project is “Optimisation of the skin-prosthetics coupling: Biomechanics of the skin-prosthetics interface”

“22% of prosthesis users abandon their device within one year and 63% report one or more skin problem before 1 year of use. Why is this the case? My project aims to understand some of the mechanisms that might lead to skin breakdown and developing therapies and products to reduce the prevalence of injury. This involves taking classical engineering theory and experiments and marrying them with biological techniques to approach the problem as an entire system, not individual and mutually exclusive components.”

The work will be supervised by Dr. Marc Masen (https://www.imperial.ac.uk/tribology/biotric/), Dr. Claire Higgins (https://www.higginslab.org/) and Dr. Peter Worsley (https://www.southampton.ac.uk/healthsciences/research/staff/peter-worsley-research.page)

Research interests

• Tribology of Human Tissue
• Skin Injury, Inflammation and Apoptosis
• BioTribology
• Prosthetics
• Mechanobiology
• Florescent Microscopy

Publications

https://www.nature.com/articles/s41598-021-95861-3

Links

Tweets by Jack_Hayes2

https://www.linkedin.com/in/jack-hayes-64822217b/

My PhD project is titled ‘Improving the rehabilitation outcomes of bilateral above / through-knee amputees’. My project utilises musculoskeletal modelling to evaluate the increased burden on the residual tissue and to guide the development of a novel rehabilitation programme which will aim to alleviate these high forces.

I enjoy being active with swimming and cycling, watching films / reading books and socialising with my friends while exploring new places and restaurants.

Abstract: Development of an Industrial Test Platform for Foot Health Device and Footwear Testing

Supervisors: Dr Dan Parker, Chris Nester and Guowu Wei. 

Medical devices for the foot encompass therapeutic inserts available over-the-counter such as insoles and protective pads, as well as devices prescribed by professionals, such as orthotics. The development of these devices involves new products being conceived, designed, samples being tested and validated against well-defined regulatory requirements,
before moving into production and consumer/patient use. With a large number of product development processes involving marketing, design, innovation and product delivery professionals, there exists a risk of inefficiencies. The specific risk that this project relates to is how product design decisions are made and informed by the testing of prototypes and
how results of this testing relate to regulatory and marketing requirements.
This industry-linked project aims to develop a physical test platform that can identify the initial and long-term performance of an orthotic and footwear product and the key effects of these products on the internal behaviour of the foot, so that product design decisions and regulatory and marketing concepts can be informed prior to more expensive, risker
clinical testing involving human participants.

The test platform comprises a phantom-foot and means of loading the foot similar to in-vivo. Two typical use contexts have been devised:

1. At an early innovation stage, the platform will enable new orthotic and footwear product designs to be tested efficiently with standardised methodology, using realistic loading conditions to evaluate the initial and long-term performance of the product (with regards to its purpose).
2. To assess the effects of orthotic materials or product designs on internal foot structures by measuring simulated tissue compression and stiffness under realistic in-vivo loading conditions.