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Biomechanical modelling of the human hand for improved control of myoelectric prostheses

School of Engineering

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Dr E Chadwick , Dr M Giannaccini No more applications being accepted Funded PhD Project (European/UK Students Only)

About the Project

The loss of a hand or greater part of the upper limb can mean a devastating loss of function for an individual. Myoelectric prostheses are robotic hands intended for replacement of the natural hand in cases of hand amputation or limb difference. These devices offer the potential for restoring some of the lost function, but challenges remain in the control of these devices.

Myoelectric signals (EMG) from muscles in the remaining forearm can be used to control hand posture and the digits of the artificial hand. The state-of-the-art is characterised by devices featuring sophisticated hardware allowing for dexterous movement of the wrist and fingers and multiple functional grips, but control of such devices by end-users is severely limited by the control algorithms and the signals available from users.

We have previously described the use of a real-time biomechanical model of the hand as a controller for artificial hands following amputation [1]. We have shown that a comprehensive biomechanical model of the hand can be used to interpret muscle activation signals and predict the kinematics of the missing hand [2], and we have demonstrated the combined use of residual limb kinematics and muscle EMG signals to predict the motion of missing limb segments [3].

However, a number of questions remain regarding the use of such a model in this scenario, and further model developments are necessary to take this work to the next stage. This project will aim to improve the model-based control of prosthetic hands by addressing the following specific objectives:

- Improve and further validate an existing biomechanical model of the hand by the addition of stabilising mechanisms that compensate for the lack of intrinsic muscle activity.
- Carry out biomechanical analyses / simulations that estimate intrinsic muscle activity from measured extrinsic activity.
- Optimise muscle recording and signal processing techniques to drive biomechanical simulations for controlling robotic hand devices.
- Evaluate the potential for model-based control of devices in prosthesis users in a simulation environment and with actual hardware.

Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Mechanical or Biomedical Engineering, Robotics, Human Movement Science or related area.

Essential background and Knowledge:

- Experience in mathematical modelling
- Programming experience in Matlab or Python
- Interest in medical devices and assistive technology


Formal applications can be completed online:

• Apply for the Degree of Doctor of Philosophy in Engineering
• State the name of the lead supervisor as the Name of Proposed Supervisor
• State the exact project title on the application form

If a suitable candidate is found before the closing date of 1 May 2020 the advert will be removed.

The start date of the project is as soon as possible but no later than 1 October 2020.

Funding Notes

Tuition Fees will be paid at UK/EU rates only and with a monthly stipend paid in arrears.

International students are welcome to apply, providing they can meet the difference between UK/EU and International tuition fees.


1. Blana, D., E. K. Chadwick, A. J. van den Bogert, and W. M. Murray (Apr. 2017). Real-time simulation of hand motion for prosthesis control. Computer Methods in Biomechanics & Biomedical Engineering 20 (5), 540–549.
2. Blana, D., A. J. van den Bogert , W. M. Murray, A. Ganguly, A. Krasoulis, K. Nazarpour, and E. K. Chadwick (May 2019) Model-based control of individual finger movements for prosthetic hand function. bioRxiv preprint no. 629246.
3. Blana, D., T. Kyriacou, J. M. Lambrecht, and E. K. Chadwick (Aug. 2016). Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment. Journal of Electromyography & Kinesiology 29, 21–27.
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