52 citations · 139 across the 8 of their papers we have counts for
8 papers
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…
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…
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Mo Han, Ozan Ozdenizci, Toshiaki Koike-Akino +2
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological st…