Gunar Schirner
Papers
11
Total Citations
116
H-Index
5
About
Gunar Schirner is a prominent researcher at Northeastern University whose work spans two compelling frontiers: intelligent prosthetic hand control and bio-inspired robotic locomotion. His most significant contributions center on developing multimodal approaches to human grasp intent inference, combining electromyography (EMG) signals with computer vision to dramatically improve the reliability of robotic prosthetic hands for transradial amputees. His highly cited 2024 paper on multimodal EMG and vision fusion (44 citations) addresses a critical challenge in prosthetics — the susceptibility of EMG-only systems to motion artifacts and muscle fatigue — while his HANDS dataset paper (28 citations) has provided the research community with an invaluable multimodal benchmark resource. Schirner has progressively advanced deep learning and convolutional neural network techniques for translating visual hand-perspective information into actionable grasp predictions. More recently, his research has expanded into snake robotics, exploring loco-manipulation, contact-implicit motion planning, and dynamic tumbling locomotion control with Northeastern's COBRA platform. Through datasets, algorithms, and novel robotic systems, Schirner's interdisciplinary research meaningfully advances human-robot interaction, assistive technology, and autonomous locomotion — making his work essential reading for researchers in robotics, biomedical engineering, and machine learning.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4From hand-perspective visual information to grasp type probabilities8 citations · 2019
- 5
- 6
- 7
- 8
- 9
- 10