Georgia Tech Research Corporation

Dean Molinaro

Aaron Young

Theodore J Johnson

Keaton Scherpereel

Jonathan Meditz

Joshua Bishop

Bharat Kanwar

Avery Yang

Jared Li

Noel Csomay-Shanklin

Exemplary task-agnostic exoskeleton control system and method are disclosed that are task-agnostic utilizing instantaneous estimates of biological joint moments from deep neural networks to assist the user movements. The exemplary control system employs multiple body sections and joints in-the-loop estimation to provide multi-joint assistance operation, e.g., within an autonomous, clothing-integrated exoskeleton. The exemplary control system may deploy a deep domain adaptation (DDA) method configured to translate human movement data between a simulated sensor domain and a real sensor domain.

Biomechanical and physiological state estimation for task agnostic wearable robot control and human monitoring

An exemplary embodiment of the present invention provides an exosuit support system, comprising first and second orthotic interfaces, first and second members, and first and second actuators. The first and second orthotic interfaces can be configured to attach to left and right legs of a user, respectively. The first member can comprise a first end connected to the first orthotic interface and a second end connected to a portion of the system configured to be positioned proximate a right shoulder of the user. The second member can comprise a first end connected to the second orthotic interface and a second end connected to a portion of the system configured to be positioned proximate a left shoulder of the user. The first actuator can be configured to apply a first tension to the first member. The second actuator can be configured to apply a second tension to the second member.

Exosuit support systems and methods

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Atlanta, GA, United States of America

Dean Molinaro