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Adaptive control of lower-limb prostheses

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  • Full or part time
    Prof D Howard
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

About This PhD Project

Project Description

Unilateral trans-femoral amputee gait consumes up to 60% more energy than able-bodied gait. For higher level amputees, research suggests that energy efficiency drops by well over 80%. Recently it has been shown that energy consumption in high level amputees increases significantly when walking on slopes, suggesting studies in level walking may underestimate the extent of the problem. The negative effects of high energy consumption are compounded by reductions in walking speed of typically 40% for trans-femoral amputees with associated low activity levels, particularly in elderly amputees. These deficits are even greater in bilateral amputees. This has a tremendous impact on what amputees can achieve and the consequences for their quality of life.

The energy storage and return capabilities of prostheses are crucial to improving the situation and yet modern prostheses only store and return significant energy below the knee, and energy is not returned in a controlled manner. For example, stored energy is not available for plantar-flexion (push-off) at the end of stance. Furthermore, modern prosthetic systems don’t transfer energy between joints, which is a lost opportunity as, for example, the excess of eccentric work at the knee could be stored and used in a controlled manner at other joints.

For these reasons, we believe there is an opportunity for truly transformative research leading to a step change in the performance of lower limb prostheses. This requires advances in: a) mechanical design to provide flexible energy storage and return; and b) intelligent adaptive control to match the behaviour of the prosthesis to the terrain being crossed and various gait variables. The first aspect is being tackled in a current EPSRC funded project involving the Universities of Salford and Manchester. The PhD proposed here will tackle the second aspect (intelligent adaptive control) and will complement the work being undertaken in the EPSRC project.

The aim will be to use sensors integrated into the prosthesis to derive measures describing the terrain being crossed (e.g. ramp gradient, step dimensions etc.) and also various gait characteristics such as speed, stride length, double-stance period etc. To achieve this, powerful regression algorithms will be used to map the raw sensor signals onto the required terrain and gait variables. To improve the performance of the regression algorithms and increase their resilience to noise and gait variability, fast signal pre-processing techniques will be employed prior to the regression stage.

The terrain and gait measures will then be used to adapt the way that the prosthesis stores and returns energy and, hence, to optimise amputee performance. The adaptive control rules will be obtained by undertaking simulation-based optimisation studies. This will involve the mathematical modelling and simulation of amputee gait and prosthesis dynamics. A combination of multi-body dynamics and empirical muscle models will be used to represent the musculoskeletal system. The prosthesis will be modelled as a combination of passive viscoelastic elements and active energy storage and return elements (e.g. miniature hydraulic systems).


Candidates should have a first or upper second class honours in an area relevant to the proposed research. This includes engineering, physics, mathematics or computer science. Candidates with other closely-related first degrees should contact the supervisor to discuss possibilities. Please note that this studentship is only available to UK/EU citizens.


Application where funding can be secured from other sources will be accepted at any time. For further information visit:

Further information and applying

For further information, please contact Professor David Howard at [email protected]

For more information on research within the School of Computing Science & Engineering and to make an application please visit:

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