From Hand-Perspective Visual Information to Grasp Type Probabilities: Deep Learning via Ranking Labels

no code implementations • 8 Mar 2021 • Mo Han, Sezen Ya{ğ}mur Günay, İlkay Yıldız, Paolo Bonato, Cagdas D. Onal, Taşkın Padır, Gunar Schirner, Deniz Erdo{ğ}muş

Convolutional neural network-based computer vision control of the prosthetic hand has received increased attention as a method to replace or complement physiological signals due to its reliability by training visual information to predict the hand gesture.

HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands

no code implementations • 8 Mar 2021 • Mo Han, Sezen Ya{ğ}mur Günay, Gunar Schirner, Taşkın Padır, Deniz Erdo{ğ}muş

Specifically, paired images from human eye-view and hand-view of various objects placed at different orientations have been captured at the initial state of grasping trials, followed by paired video, EMG and IMU from the arm of the human during a grasp, lift, put-down, and retract style trial structure.

Motion Planning