West Virginia University

University of Pittsburgh

Anton Sobinov

Sergiy Yakovenko

Matthew Boots

Valeriya Gritsenko

Robert Gaunt

Jennifer Collinger

Lee Fisher

Russell Hardesty

A system and method for controlling a device, such as a prosthetic limb are provided. A biomimetic controller of the system comprises a signal processor and a musculoskeletal model. The signal processor processes M biological signals received from a residual limb to transform the M biological signals into N activation signals, where M and N are integers and M is less than N. The musculoskeletal model transforms the N activation signals into intended motion signals. A prosthesis controller transforms the intended motion signals into three or more control signals that are outputted from an output port of the prosthesis controller. A controlled device receives the control signals and performs one or more tasks in accordance with the control signals.

Systems and methods for approximating musculoskeletal dynamics

A system and method for controlling a device, such as a virtual reality (VR) and/or a prosthetic limb are provided. A biomimetic controller of the system comprises a signal processor and a musculoskeletal model. The signal processor processes M biological signals received from a residual limb to transform the M biological signals into N activation signals, where M and N are integers and M is less than N. The musculoskeletal model transforms the N activation signals into intended motion signals. A prosthesis controller transforms the intended motion signals into three or more control signals that are outputted from an output port of the prosthesis controller. A controlled device receives the control signals and performs one or more tasks in accordance with the control signals.

An approximation method and system are provided for more quickly controlling a prosthetic or other device by reducing computational processing time in a muscle model that can be used to control the prosthetic. For a given muscle, the approximation method can quickly compute polynomial structures for a muscle length and for each associated moment arms, which may be used to generate a torque for a joint position of a physics model. The physics model, in turn, produces a next joint position and velocity data for driving a prosthetic. The approximation method expands the polynomial structures as long as expansion is possible and sufficiently beneficial. The computations can be performed quickly by expanding the polynomial structures in a way that constrains the muscle length polynomial to the moment arm polynomial structures, and vice versa.

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Morgantown, WV, United States of America

Anton Sobinov