52 citations · 65 across the 5 of their papers we have counts for
5 papers
Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder
Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger +5
We study the task of gesture recognition from electromyography (EMG), with the goal of enabling expressive human-computer interaction at high accuracy, while minimizing the time re…
A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition
Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger +5
Objective: The objective of the study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. Approach: We use a problem t…
User Training with Error Augmentation for Electromyogram-based Gesture Classification
Yunus Bicer, Niklas Smedemark-Margulies, Basak Celik +7
We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuratio…
Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control
Mehrshad Zandigohar, Mo Han, Mohammadreza Sharif +8
Objective: For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. Current control methods based on physiological si…
Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach
Md Navid Akbar, Mathew Yarossi, Marc Martinez-Gost +6
A deep neural network (DNN) that can reliably model muscle responses from corresponding brain stimulation has the potential to increase knowledge of coordinated motor control for n…