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In previous work, the authors proposed a data-driven optimisation algorithmfor the personalisation of human-prosthetic interfaces, demonstrating thepossibility of adapting prosthesis behaviour to its user while the userperforms tasks with it. This method requires that the human and the prosthesispersonalisation algorithm have same pre-defined objective function. This waspreviously ensured by providing the human with explicit feedback on what theobjective function is. However, constantly displaying this information to theprosthesis user is impractical. Moreover, the method utilised task informationin the objective function which may not be available from the wearable sensorstypically used in prosthetic applications. In this work, the previous approachis extended to use a prosthesis objective function based on implicit humanmotor behaviour, which represents able-bodied human motor control and ismeasureable using wearable sensors. The approach is tested in a hardwareimplementation of the personalisation algorithm on a prosthetic elbow, wherethe prosthetic objective function is a function of upper-body compensation, andis measured using wearable IMUs. Experimental results on able-bodied subjectsusing a supernumerary prosthetic elbow mounted on an elbow orthosis suggestthat it is possible to use a prosthesis objective function which is implicit inhuman behaviour to achieve collaboration without providing explicit feedback tothe human, motivating further studies.

The Use of Implicit Human Motor Behaviour in the Online Personalisation of Prosthetic Interfaces