76 citations · 202 across the 16 of their papers we have counts for
6 papers · 1 filter
Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement
Mo Han, Mehrshad Zandigohar, Sezen Yagmur Gunay +2
Electromyography (EMG) data has been extensively adopted as an intuitive interface for instructing human-robot collaboration. A major challenge of the real-time detection of human…
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…
From Hand-Perspective Visual Information to Grasp Type Probabilities: Deep Learning via Ranking Labels
Mo Han, Sezen Ya{ğ}mur Günay, İlkay Yıldız +5
Limb deficiency severely affects the daily lives of amputees and drives efforts to provide functional robotic prosthetic hands to compensate this deprivation. Convolutional neural…
HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands
Mo Han, Sezen Ya{ğ}mur Günay, Gunar Schirner +2
Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this per…
NetCut: Real-Time DNN Inference Using Layer Removal
Mehrshad Zandigohar, Deniz Erdogmus, Gunar Schirner
Deep Learning plays a significant role in assisting humans in many aspects of their lives. As these networks tend to get deeper over time, they extract more features to increase ac…
Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand
Mehrshad Zandigohar, Mo Han, Deniz Erdogmus +1
For lower arm amputees, prosthetic hands promise to restore most of physical interaction capabilities. This requires to accurately predict hand gestures capable of grabbing varying…