most citedFrom Unstable Contacts to Stable Control: A Deep Learning Paradigm for HD-sEMG in Neurorobotics

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

eess.SP2023

A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning

Golara Ahmadi Azar, Qin Hu, Melika Emami +3

Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computer interfaces that can interpret the deep spatiotemporal dynamic…

cs.HC2023

ViT-MDHGR: Cross-day Reliability and Agility in Dynamic Hand Gesture Prediction via HD-sEMG Signal Decoding

Qin Hu, Golara Ahmadi Azar, Alyson Fletcher +2

Surface electromyography (sEMG) and high-density sEMG (HD-sEMG) biosignals have been extensively investigated for myoelectric control of prosthetic devices, neurorobotics, and more…

cs.RO20231 cited

From Unstable Contacts to Stable Control: A Deep Learning Paradigm for HD-sEMG in Neurorobotics

Eion Tyacke, Kunal Gupta, Jay Patel +2

In the past decade, there has been significant advancement in designing wearable neural interfaces for controlling neurorobotic systems, particularly bionic limbs. These interfaces…

cs.RO2023

A Smart Handheld Edge Device for On-Site Diagnosis and Classification of Texture and Stiffness of Excised Colorectal Cancer Polyps

Ozdemir Can Kara, Jiaqi Xue, Nethra Venkatayogi +5

This paper proposes a smart handheld textural sensing medical device with complementary Machine Learning (ML) algorithms to enable on-site Colorectal Cancer (CRC) polyp diagnosis a…

cs.RO2023

How Does the Inner Geometry of Soft Actuators Modulate the Dynamic and Hysteretic Response?

Jacqueline Libby, Aniket A. Somwanshi, Federico Stancati +4

This paper investigates the influence of the internal geometrical structure of soft pneu-nets on the dynamic response and hysteresis of the actuators. The research findings indicat…

cs.LG2023

FiMReSt: Finite Mixture of Multivariate Regulated Skew-t Kernels -- A Flexible Probabilistic Model for Multi-Clustered Data with Asymmetrically-Scattered Non-Gaussian Kernels

Sarmad Mehrdad, S. Farokh Atashzar

Recently skew-t mixture models have been introduced as a flexible probabilistic modeling technique taking into account both skewness in data clusters and the statistical degree of…