Matteo Cognolato

According to our database1, Matteo Cognolato authored at least 11 papers between 2016 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2021
Improving Robotic Hand Prosthesis Control With Eye Tracking and Computer Vision: A Multimodal Approach Based on the Visuomotor Behavior of Grasping.
Frontiers Artif. Intell., 2021

2019
An Augmented Reality Environment to Provide Visual Feedback to Amputees During sEMG Data Acquisitions.
Proceedings of the Towards Autonomous Robotic Systems - 20th Annual Conference, 2019

Video-based Prediction of Hand-grasp Preshaping with Application to Prosthesis Control.
Proceedings of the International Conference on Robotics and Automation, 2019

Analyzing the Trade-Off Between Training Session Time and Performance in Myoelectric Hand Gesture Recognition During Upper Limb Movement.
Proceedings of the 16th IEEE International Conference on Rehabilitation Robotics, 2019

2018
Visual Cues to Improve Myoelectric Control of Upper Limb Prostheses.
Proceedings of the 7th IEEE International Conference on Biomedical Robotics and Biomechatronics, 2018

Hand Gesture Classification in Transradial Amputees Using the Myo Armband Classifier.
Proceedings of the 7th IEEE International Conference on Biomedical Robotics and Biomechatronics, 2018

2017
Visual Cues to Improve Myoelectric Control of Upper Limb Prostheses.
CoRR, 2017

Semi-automatic Training of an Object Recognition System in Scene Camera Data Using Gaze Tracking and Accelerometers.
Proceedings of the Computer Vision Systems - 11th International Conference, 2017

Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data.
Proceedings of the International Conference on Rehabilitation Robotics, 2017

Megane Pro: Myo-electricity, visual and gaze tracking data acquisitions to improve hand prosthetics.
Proceedings of the International Conference on Rehabilitation Robotics, 2017

2016
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.
Frontiers Neurorobotics, 2016


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