PROSTHETIC HAND SYSTEM WITH AN INTEGRATED IMAGE RECOGNITION SUBSYSTEM

    公开(公告)号:US20240277491A1

    公开(公告)日:2024-08-22

    申请号:US18444401

    申请日:2024-02-16

    CPC classification number: A61F2/586 G06T1/0007 G06T7/0002 A61F2002/704

    Abstract: A prosthetic hand assembly includes a hand structure including an actuatable wrist, a palm, and a plurality of actuatable fingers coupled with a plurality of actuators each configured to selectively direct movement of the actuatable wrist, a camera disposed on one side of the hand structure, the camera is selectively operable to generate a plurality of successive images of an object positioned adjacent the hand structure, a processor coupled with the plurality of actuators, the processor is communicatively coupled with the camera and configured to receive the plurality of successive images of the object, wherein the processor is configured to determine one or more characteristic of the object from the plurality of successive images of the object, the processor is configured to selectively drive the plurality of actuators to affect the actuatable wrist and plurality of actuatable fingers based on the one or more characteristics.

    Powered Joint System with Enhanced Neural-Based Controller

    公开(公告)号:US20230380994A1

    公开(公告)日:2023-11-30

    申请号:US18032790

    申请日:2021-10-20

    Abstract: A powered joint system for providing volitional control of joint movement includes a knee joint, one or more electromyography (EMG) sensors, and a controller. The one or more EMG sensors are adapted for placement on skin of a residual limb of a user to detect EMG signals from a posterior side of a residual limb. The controller is communicatively coupled to the knee joint and the one or more EMG sensors. The controller comprises one or more processors and one or more hardware storage devices storing instructions that are executable by the one or more processors to configure the controller to perform various acts, including to receive an EMG signal from the one or more EMG sensors (the EMG signal being representative of muscle activation at the posterior side of the residual limb of the user) and determine a target knee torque based on the EMG signal.

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