A Non-Linear Body Machine Interface for Controlling Assistive Robotic Arms

Rizzoglio, Fabio; Giordano, Marco; Mussa-Ivaldi, Ferdinando A.; Casadio, Maura

doi:10.1109/tbme.2023.3237081

Cited by 7 publications

(1 citation statement)

References 43 publications

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“…For instance, it can also be applied to select sensors for predicting the user intention in assistive lower limb exoskeletons [39], [40]. Furthermore, our proposed problem formulation can be applied in cases where ground truth labels are not available, such as in the detection of back injury risk in [41], or in unsupervised scenarios like the Body-Machine Interface studied in [42].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Sensor Selection With Composite Features in Identifying User-Intended Poses for Human-Prosthetic Interfaces

Mohammadi

Tan

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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A Human-Prosthetic Interface (HPI) serves to estimate and realise the limb pose intended by the human user, using the information obtained from sensors worn by the user. In recent studies, the HPI maps multijoint limb poses (i.e. coordinated movement of the body and limbs) to the inputs of multiple sensors. This is in contrast to the conventional methods where each degree of freedom of the powered prosthesis is mapped to the input of one/a pair of sensors. In this approach, it is necessary to systematically select sensors that carry the most information for the intended set of poses, to improve system accuracy and/or minimise the number of sensors, thus the complexity, in the prosthetic system. In this paper, sensor selection process is systematically formulated to maximise the information contained in the input features for a given number of sensors. Most importantly, it accounts for composite features, which are features requiring information from multiple sensors. Such composite features exist and are important in HPIs as we seek to capture coordinated motion involving movements of multiple limb and body segments. A non-convex optimisation problem is formulated which accounts for the constraint introduced by the composite features. A projection matrix is utilised as the optimisation variable to select intended features for evaluation. The problem is solved by the proposed Sensor Selection with Composite Features (SS-CF ) algorithm which adapts convex-relaxation techniques. The SS-CF is benchmarked against HPI with expert-selected sensors in the literature and against a greedy heuristic method. The outcome demonstrated the efficacy of the SS-CF algorithm.

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