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Found 8 papers, 0 papers with code
Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement
We collected and utilized data from large gesture vocabularies with multiple dynamic actions to encode the transitions from one grasp intent to another based on common sequences of the grasp movements.
Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control
Conclusion: Our experimental data analyses demonstrate that EMG and visual evidence show complementary strengths, and as a consequence, fusion of multimodal evidence can outperform each individual evidence modality at any given time.
From Hand-Perspective Visual Information to Grasp Type Probabilities: Deep Learning via Ranking Labels
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
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.
Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand
For lower arm amputees, prosthetic hands promise to restore most of physical interaction capabilities.
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users.
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner.
Disentangled Adversarial Transfer Learning for Physiological Biosignals
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways.
