Lower limb amputations often severely restrict patients when trying to perform activities of daily living, even when using prostheses. Active lower limb prostheses are a promising alternative to the more common passive prostheses but even those still have significant limitations and shortcomings. This project aims to overcome a number of those limitations and shortcomings through the development of a novel control strategy for active lower limb prostheses.

The novel control strategy will be widely applicable and will consist of a novel classifier, a novel variable impedance control and novel ‘amputee in the loop’ learning algorithms. The strategy will be tested on two different hardware platforms and for varying activity patterns and contexts. Performance in each setting will be measured via well-designed evaluation processes with a focus on
patient reported outcome measures (PROMs).

The hypothesised outcomes with respect to the state-of-the-art are: a higher number of supported activity scenarios, high classification accuracy, a more natural switching of modes, high anti interference capability, a reduced need for parameter tuning, increased system simplicity and reliability. This will bring significant improvements of the quality of life for active prosthesis users as well as socio-economic benefits in the prosthetics sector.