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- W2182480005 abstract "C5/C6 tetraplegic patients and transhumeral amputees may be able to use voluntary shoulder motion as command signals for a functional electrical stimulation (FES) system or a transhumeral prosthesis. Such prostheses require, at the most basic level, the control of endpoint position in three dimensions, hand orientation, and grasp. Spatiotemporal syner- gies exist between the proximal and distal arm joints for goal- oriented reaching movements as performed by able-bodied subjects. To fit these synergies, we utilized three-layer artificial neural networks. These networks could be used as a means for obtaining user intent information during reaching movements. We conducted reaching experiments in which subjects reached to and grasped a handle in a three-dimensional gantry. In our previous work, the three rotational angles at the shoulder were used to predict elbow flexion/extension angle during reaches on a two-dimensional plane. In this paper, we extend this model to include the two translational movements at the shoulder as inputs and an additional output of forearm pronation/supination. Counterintuitively, as the complexity of the task and the complexity of the neural network architecture increased, the performance also improved." @default.
- W2182480005 created "2016-06-24" @default.
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- W2182480005 date "2008-01-01" @default.
- W2182480005 modified "2023-09-27" @default.
- W2182480005 title "PredictionofDistalArmPosture in 3-D Space From Shoulder Movements for Control of Upper Limb Prostheses Experimentsindicatethatvoluntaryshouldermovementsmaybeusedtogeneratethe controlsignalsneededforoperationofarmsandhandsthatareparalyzedorartificial." @default.
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