A novel sEMG-based dynamic hand gesture recognition approach via residual attention network [PDF]
, 2023 With the emergence of more and more lightweight, convenient and cheap surface electromyography signal (sEMG) snsors, gesture recognition based on sEMG sensors has attracted much attention of researchers.
Yang, Lei +7 more
core +1 more source
Dynamic gesture recognition results (a.lr = 0.01; b.lr = 0.001). [PDF]
, 2023 Dynamic gesture recognition results (a.lr = 0.01; b.lr = 0.001).
Yan Feng (128912)
core +1 more source
Dynamic gesture recognition pipeline based on A-CBLN. [PDF]
, 2023 Dynamic gesture recognition pipeline based on A-CBLN.
Xiao Zhang (152326) +5 more
core +1 more source
A Transformer-Based Network for Dynamic Hand Gesture Recognition [PDF]
2020 International Conference on 3D Vision (3DV), 2020 Transformer-based neural networks represent a successful self-attention mechanism that achieves state-of-the-art results in language understanding and sequence modeling. However, their application to visual data and, in particular, to the dynamic hand gesture recognition task has not yet been deeply investigated. In this paper, we propose a transformer-
Andrea D'Eusanio +5 more
openaire +2 more sources
Fusion of dynamic and static features for gait recognition over time [PDF]
, 2005 Gait recognition aims to identify people at a distance based on the way they walk. This paper deals with a problem of recognition by gait when time-dependent covariates are added, i.e.
Veres, Galina V +7 more
core +1 more source
Research on Gesture Recognition Method Based on Computer Vision
MATEC Web of Conferences, 2018 Gesture recognition is an important way of human-computer interaction. With time going on, people are no longer satisfied with gesture recognition based on wearable devices, but hope to perform gesture recognition in a more natural way.
Wang Xianghan +4 more
doaj +1 more source
HandFormer: A Dynamic Hand Gesture Recognition Method Based on Attention Mechanism
Applied Sciences, 2023 The application of dynamic gestures is extensive in the field of automated intelligent manufacturing. Due to the temporal and spatial complexity of dynamic gesture data, traditional machine learning algorithms struggle to extract accurate gesture ...
Yun Zhang, Fengping Wang
doaj +1 more source
Personalized Zebrafish Models for Fusion‐Positive Pediatric Sarcomas
Pediatric Blood &Cancer, EarlyView.ABSTRACT Clinical sequencing efforts have revolutionized our approaches to categorizing pediatric cancers in real time. This has dramatically improved our ability to profile pediatric tumors, identify actionable vulnerabilities, and influence clinical care.
Lisa H. Hall +2 more
wiley +1 more source
Determining Parental Factors for Clinical Trial Attrition in Pediatric Acute Lymphoblastic Leukemia
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background/Objectives Despite high enrollment rates on Children's Oncology Group (COG) protocols, attrition after initial consent is challenging, introducing bias and prolonging trial completion. While adult oncology literature has identified predictors of withdrawal, little is known about caregiver decision‐making for child participation in ...
Kimberly L. Stathas +3 more
wiley +1 more source
Gesture Recognition Aplication based on Dynamic Time Warping (DTW) FOR Omni-Wheel Mobile Robot [PDF]
, 2011 This project presents of the movement of omni-wheel robot moves in the trajectory obtained from the gesture recognition system based on Dynamic Time Warping. Single camera is used as the input of the system, which is also a reference to the movement
Gama, Indra
core