There are more than one million annual amputations globally as a result of vascular diseases, and cancer. Due to the increasing rate of diabetes and the population ageing, a growth of amputation is expected with the prediction that the amputee population will double by 2050. A prosthesis allows a certain restoration of functional mobility after an amputation. However, neither passive nor active prostheses can directly address the fundamental problems of chronic pain trauma and muscle atrophy in millions of amputees worldwide. Chronic amputation-related pain impairs function. In addition, the early decline in the use of the affected limb results in progressive muscle atrophy with strength loss. Concurrently, a mechanical adaption occurs in order to compensate for the collective effects due to limb loss. A common compensation strategy is to overload the intact limbs in terms of time and intensity, which will cause secondary musculoskeletal disorders, further compromising their health-related quality of life.

In the project, we will work towards a new generation of therapies for patients with lower limb amputations using a combination of functional electrical stimulation (FES) and musculoskeletal modelling techniques. The computational design of the FES rehabilitation protocol has the potential to improve the pain management, muscle strength and mobility for lower limb amputees by tailoring the FES prescription to the unique needs of each patient.

The project will deliver:
1. A detailed musculoskeletal model of lower limb amputees based on the experimental gait data, including motion, ground reaction force, muscle EMG and high-resolution magnetic resonance imaging.
2. A computational optimisation framework to design the FES rehabilitation protocol, which will address the clinical questions such as which muscles to stimulate and when to simulate them logically and non-intuitively.
3. A feasibility study to evaluate the effectiveness and reliability of model-based FES protocol design, potentially resulting in a future clinical tool.

The project is supported by UK EPSRC (EP/V057138/1)