The synergy complement control approach for seamless limb-driven prostheses

Johannes Kühn, Tingli Hu, Alexander Tödtheide, Edmundo Pozo Fortunić, Elisabeth Jensen, Sami Haddadin

Research output: Contribution to journalArticlepeer-review


Limb-driven control allows for direct control by using residual limb movements rather than unnatural and complex muscle activation. Existing limb-driven methods simultaneously learn a variety of possible motions, ranging from a residual limb to entire arm motions, from human templates by relying on linear or nonlinear regression techniques. However, the map between a low-dimensional residual limb movement and high-dimensional total limb movement is highly underdetermined. Therefore, this complex, high-dimensional coordination problem cannot be accurately solved by treating it as a data-driven black box problem. Here we address this challenge by introducing the residual limb-driven control framework synergy complement control. Firstly, the residual limb drives a one-dimensional phase variable to simultaneously control the multiple joints of the prosthesis. Secondly, the resulting prosthesis motion naturally complements the movement of the residual limb by its synergy components. Furthermore, our framework adds information on contextual tasks and goals and allows for seamless transitions between these. Experimental validation was conducted using subjects with preserved arms employing an exo-prosthesis setup, and studies involving participants with and without limb differences in a virtual reality setup. The findings affirm that the restoration of lost coordinated synergy capabilities is reliably achieved through the utilization of synergy complement control with the prosthesis.

Original languageEnglish
Pages (from-to)481-492
Number of pages12
JournalNature Machine Intelligence
Issue number4
StatePublished - Apr 2024


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