Results 31 to 40 of about 9,632 (295)

Comparative Study of Machine Learning Algorithms to Classify Hand Gestures from Deployable and Breathable Kirigami-Based Electrical Impedance Bracelet

Multimodal Technologies and Interaction, 2020
Wearable devices are gaining recognition for their use as a biosensor platform. Electrical impedance tomography (EIT) is one of the sensing techniques that utilizes wearable sensors as its primary data acquisition system.
Godwin Ponraj Joseph Vedhagiri, Xin Zhi Wang, Kirthika Senthil Kumar, Hongliang Ren   +3 more
doaj   +1 more source

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

Journal of Imaging, 2022
Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge.
Fahmid Al Farid, Noramiza Hashim, Junaidi Abdullah, Md Roman Bhuiyan, Wan Noor Shahida Mohd Isa, Jia Uddin, Mohammad Ahsanul Haque, Mohd Nizam Husen   +7 more
doaj   +1 more source

Spectrum-Based Hand Gesture Recognition Using Millimeter-Wave Radar Parameter Measurements

IEEE Access, 2019
Radar sensors offer several advantages over optical sensors in the gesture recognition for remote control of electronic devices. In this paper, we investigate the feasibility of human gesture recognition using the spectra of radar measurement parameters.
Changjiang Liu, Yuanhao Li, Dongyang Ao, Haiyan Tian   +3 more
doaj   +1 more source

Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement

Frontiers in Neuroscience, 2022
Electromyography (EMG) data has been extensively adopted as an intuitive interface for instructing human-robot collaboration. A major challenge to the real-time detection of human grasp intent is the identification of dynamic EMG from hand movements ...
Mo Han, Mehrshad Zandigohar, Sezen Yağmur Günay, Gunar Schirner, Deniz Erdoğmuş   +4 more
doaj   +1 more source

Hand character gesture recognition based on a single millimetre‐wave radar chip

IET Radar, Sonar & Navigation, 2022
In this work, a new method for hand character gesture recognition based on a single millimetre‐wave radar chip is proposed. Most current radar‐based gesture recognition approaches use Doppler spectrograms or the feature extraction of Doppler spectrograms.
Wei Li, Jiahao Jiang, Yi Yao, Danian Liu, Yang Gao, Qi Li   +5 more
doaj   +1 more source

Prototype Learning for Micro-gesture Classification [PDF]

CoRR
1st Place in Micro-gesture Classification in MiGA at IJCAI ...
Guoliang Chen, Fei Wang 0067, Kun Li 0008, Zhiliang Wu, Hehe Fan, Yi Yang 0001, Meng Wang 0001, Dan Guo 0001   +7 more
openaire   +2 more sources

3D gesture classification with convolutional neural networks [PDF]

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
In this paper, we present an approach that classifies 3D gestures using jointly accelerometer and gyroscope signals from a mobile device. The proposed method is based on a convolutional neural network with a specific structure involving a combination of 1D convolution, averaging, and max-pooling operations. It directly classifies the fixed-length input
Duffner, Stefan, Berlemont, Samuel, Lefebvre, Grégoire, Garcia, Christophe   +3 more
openaire   +1 more source

Low Power Neuromorphic EMG Gesture Classification

CoRR, 2022
3 Pages, 5 figures, 1 ...
Sai Sukruth Bezugam, Ahmed Shaban, Manan Suri   +2 more
openaire   +2 more sources

Multi-Stream Convolutional Neural Network-Based Wearable, Flexible Bionic Gesture Surface Muscle Feature Extraction and Recognition

Frontiers in Bioengineering and Biotechnology, 2022
Surface electromyographic (sEMG) signals are weak physiological electrical signals, which are highly susceptible to coupling external noise and cause major difficulties in signal acquisition and processing.
Wansu Liu, Biao Lu
doaj   +1 more source

Surgical Gesture Classification from Video Data [PDF]

, 2012
Much of the existing work on automatic classification of gestures and skill in robotic surgery is based on kinematic and dynamic cues, such as time to completion, speed, forces, torque, or robot trajectories. In this paper we show that in a typical surgical training setup, video data can be equally discriminative.
Benjamín Béjar Haro, Luca Zappella, René Vidal   +2 more
openaire   +2 more sources