Log in

Relevant bibliographies by topics / Gesture classification and feature extraction / Journal articles

To see the other types of publications on this topic, follow the link: Gesture classification and feature extraction.

Journal articles on the topic 'Gesture classification and feature extraction'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Gesture classification and feature extraction.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Wu, Yutong, Xinhui Hu, Ziwei Wang, Jian Wen, Jiangming Kan, and Wenbin Li. "Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification." Applied Sciences 9, no. 24 (2019): 5343. http://dx.doi.org/10.3390/app9245343.

Full text

Abstract:

It is necessary to complete the two parts of gesture recognition and wireless remote control to realize the gesture control of the automatic pruning machine. To realize gesture recognition, in this paper, we have carried out the research of gesture recognition technology based on surface electromyography signal, and discussed the influence of different numbers and different gesture combinations on the optimal size. We have calculated the 630-dimensional eigenvector from the benchmark scientific database of sEMG signals and extracted the features using principal component analysis (PCA). Discri

APA, Harvard, Vancouver, ISO, and other styles

2

Trivedi, Kaustubh, Priyanka Gaikwad, Mahalaxmi Soma, Komal Bhore, and Prof Richa Agarwal. "Improve the Recognition Accuracy of Sign Language Gesture." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4343–47. http://dx.doi.org/10.22214/ijraset.2022.43220.

Full text

Abstract:

Abstract: Image classification is one of classical issue of concern in image processing. There are various techniques for solving this issue. Sign languages are natural language that used to communicate with deaf and mute people. There is much different sign language in the world. But the main focused of system is on Sign Language (SL) which is on the way of standardization in that the system will concentrated on hand gestures only. Hand gesture is very important part of the body for exchange ideas, messages, and thoughts among deaf and dumb people. The proposed system will recognize the numbe

APA, Harvard, Vancouver, ISO, and other styles

3

Gaikwad, Priyanka, Kaustubh Trivedi, Mahalaxmi Soma, Komal Bhore, and Prof Richa Agarwal. "A Survey on Sign Language Recognition with Efficient Hand Gesture Representation." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 21–25. http://dx.doi.org/10.22214/ijraset.2022.41963.

Full text

Abstract:

Abstract: Image classification is one amongst classical issue of concern in image processing. There are various techniques for solving this issue. Sign languages are natural language that want to communicate with deaf and mute people. There's much different sign language within the world. But the most focused of system is on Sign language (SL) which is on the way of standardization there in the system will focused on hand gestures only. Hand gesture is extremely important a part of the body for exchange ideas, messages, and thoughts among deaf and dumb people. The proposed system will recogniz

APA, Harvard, Vancouver, ISO, and other styles

4

Wei Li, Wei Li, Yang Gao Wei Li, Jun Chen Yang Gao, Si-Yi Niu Jun Chen, Jia-Hao Jiang Si-Yi Niu, and Qi Li Jia-Hao Jiang. "Human Gesture Recognition Based on Millimeter-Wave Radar Using Improved C3D Convolutional Neural Network." 電腦學刊 34, no. 3 (2023): 001–18. http://dx.doi.org/10.53106/199115992023063403001.

Full text

Abstract:

<p>In this paper, we propose a time sequential IC3D convolutional neural network approach for hand gesture recognition based on frequency modulated continuous wave (FMCW) radar. Firstly, the FMCW radar is used to collect the echoes of human hand gestures. A two-dimensional fast Fourier transform calculates the range and velocity information of hand gestures in each frame signal to construct the Range-Doppler heat map dataset of hand gestures. Then, we design an IC3D network for feature extraction and classification of the dynamic gesture heat map. Finally, the experiment results show tha

APA, Harvard, Vancouver, ISO, and other styles

5

Chang, Ying, Lan Wang, Lingjie Lin, and Ming Liu. "Deep Neural Network for Electromyography Signal Classification via Wearable Sensors." International Journal of Distributed Systems and Technologies 13, no. 3 (2022): 1–11. http://dx.doi.org/10.4018/ijdst.307988.

Full text

Abstract:

The human-computer interaction has been widely used in many fields, such intelligent prosthetic control, sports medicine, rehabilitation medicine, and clinical medicine. It has gradually become a research focus of social scientists. In the field of intelligent prosthesis, sEMG signal has become the most widely used control signal source because it is easy to obtain. The off-line sEMG control intelligent prosthesis needs to recognize the gestures to execute associated action. In order solve this issue, this paper adopts a CNN plus BiLSTM to automatically extract sEMG features and recognize the

APA, Harvard, Vancouver, ISO, and other styles

6

Ansar, Hira, Ahmad Jalal, Munkhjargal Gochoo, and Kibum Kim. "Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities." Sustainability 13, no. 5 (2021): 2961. http://dx.doi.org/10.3390/su13052961.

Full text

Abstract:

Due to the constantly increasing demand for the automatic localization of landmarks in hand gesture recognition, there is a need for a more sustainable, intelligent, and reliable system for hand gesture recognition. The main purpose of this study was to develop an accurate hand gesture recognition system that is capable of error-free auto-landmark localization of any gesture dateable in an RGB image. In this paper, we propose a system based on landmark extraction from RGB images regardless of the environment. The extraction of gestures is performed via two methods, namely, fused and directiona

APA, Harvard, Vancouver, ISO, and other styles

7

Satybaldina, Dina, and Gulzia Kalymova. "Deep learning based static hand gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 398. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp398-405.

Full text

Abstract:

Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and

APA, Harvard, Vancouver, ISO, and other styles

8

Satybaldina, Dina, and Gulzia Kalymova. "Deep learning based static hand gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 398–405. https://doi.org/10.11591/ijeecs.v21.i1.pp398-405.

Full text

Abstract:

Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human-computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For preproce

APA, Harvard, Vancouver, ISO, and other styles

9

Bai, Duanyuan, Dong Zhang, Yongheng Zhang, Yingjie Shi, and Tingyi Wu. "Gesture Recognition of sEMG Signals Based on CNN-GRU Network." Journal of Physics: Conference Series 2637, no. 1 (2023): 012054. http://dx.doi.org/10.1088/1742-6596/2637/1/012054.

Full text

Abstract:

Abstract To improve the accuracy of surface electromyogram signal (sEMG) gesture recognition algorithm and solve the problem of manually extracting many features, this paper proposes a deep neural network-based gesture recognition method. A neural network integrating CNN and GRU was designed. The 8-channel sEMG data collected by the MYO armband is input to the CNN for feature extraction, and then the obtained feature sequence is input to the GRU network for gesture classification, and finally the recognition result of the gesture category is output. The experimental findings that the proposed

APA, Harvard, Vancouver, ISO, and other styles

10

Wang, Zhiyuan, Chongyuan Bi, Songhui You, and Junjie Yao. "Hidden Markov Model-Based Video Recognition for Sports." Advances in Mathematical Physics 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/5183088.

Full text

Abstract:

In this paper, we conduct an in-depth study and analysis of sports video recognition by improved hidden Markov model. The feature module is a complex gesture recognition module based on hidden Markov model gesture features, which applies the hidden Markov model features to gesture recognition and performs the recognition of complex gestures made by combining simple gestures based on simple gesture recognition. The combination of the two modules forms the overall technology of this paper, which can be applied to many scenarios, including some special scenarios with high-security levels that req

APA, Harvard, Vancouver, ISO, and other styles

11

Tang, Gaopeng, Tongning Wu, and Congsheng Li. "Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results." Sensors 23, no. 17 (2023): 7478. http://dx.doi.org/10.3390/s23177478.

Full text

Abstract:

As a convenient and natural way of human-computer interaction, gesture recognition technology has broad research and application prospects in many fields, such as intelligent perception and virtual reality. This paper summarized the relevant literature on gesture recognition using Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar from January 2015 to June 2023. In the manuscript, the widely used methods involved in data acquisition, data processing, and classification in gesture recognition were systematically investigated. This paper counts the information related to FMCW milli

APA, Harvard, Vancouver, ISO, and other styles

12

Nandyal, Suvarna, and Suvarna Laxmikant Kattimani. "Umpire Gesture Detection and Recognition using HOG and Non-Linear Support Vector Machine (NL-SVM) Classification of Deep Features in Cricket Videos." Journal of Physics: Conference Series 2070, no. 1 (2021): 012148. http://dx.doi.org/10.1088/1742-6596/2070/1/012148.

Full text

Abstract:

Abstract Gesture Recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation, monitoring patients or elder people, surveillance systems, sports gesture analysis, human behaviour analysis etc., to virtual reality. In recent years, there has been increased interest in video summarization and automatic sports highlights g

APA, Harvard, Vancouver, ISO, and other styles

13

Zhang, Yajun, Bo Yuan, Zhixiong Yang, Zijian Li, and Xu Liu. "Wi-NN: Human Gesture Recognition System Based on Weighted KNN." Applied Sciences 13, no. 6 (2023): 3743. http://dx.doi.org/10.3390/app13063743.

Full text

Abstract:

Gesture recognition, the basis of human–computer interaction (HCI), is a significant component for the development of smart home, VR, and senior care management. Most gesture recognition methods still depend on sensors worn by the user or video-based gestures for recognition, can be used for fine-grained gesture recognition. our paper implements a gesture recognition method that is independent of environment and gesture drawing direction, and it achieves gesture recognition classification by using small sample data. Wi-NN, proposed in this study, does not require the user to wear additional de

APA, Harvard, Vancouver, ISO, and other styles

14

Chopparapu, SaiTeja, and Joseph Beatrice Seventline. "An Efficient Multi-modal Facial Gesture-based Ensemble Classification and Reaction to Sound Framework for Large Video Sequences." Engineering, Technology & Applied Science Research 13, no. 4 (2023): 11263–70. http://dx.doi.org/10.48084/etasr.6087.

Full text

Abstract:

Machine learning-based feature extraction and classification models play a vital role in evaluating and detecting patterns in multivariate facial expressions. Most conventional feature extraction and multi-modal pattern detection models are independent of filters for multi-class classification problems. In traditional multi-modal facial feature extraction models, it is difficult to detect the dependent correlated feature sets and use ensemble classification processes. This study used advanced feature filtering, feature extraction measures, and ensemble multi-class expression prediction to opti

APA, Harvard, Vancouver, ISO, and other styles

15

Nurul Khotimah, Wijayanti, Nanik Suciati, and Tiara Anggita. "Indonesian sign language recognition using kinect and dynamic time warping." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (2019): 495. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp495-503.

Full text

Abstract:

Sign Language Recognition System (SLRS) is a system to recognise sign language and then translate them into text. This system can be developed by using a sensor-based technique. Some studies have implemented various feature extraction and classification methods to recognise sign language in the different country. However, their systems were user dependent (the accuracy was high when the trained and the tested user were the same people, but it was getting worse when the tested user was different to the trained user). Therefore in this study, we proposed a feature extraction method which is inva

APA, Harvard, Vancouver, ISO, and other styles

16

Sevim, Yusuf. "A New Feature Extraction Method for EMG Signals." Traitement du Signal 39, no. 5 (2022): 1615–20. http://dx.doi.org/10.18280/ts.390518.

Full text

Abstract:

Surface Electromyography (sEMG) is an important tool for gesture recognition. Features and classification methods have to be carefully selected to be successful in the recognition of electromyografic signals. In most of the sEMG studies, time and frequency domain features have been extracted and classified with a single classifier. But neither one feature nor one classifier alone has achieved high classification accuracies. Using a feature and classifier combination would be a solution for this problem, and increase the accuracies. As a contribution to this field, a new time domain EMG feature

APA, Harvard, Vancouver, ISO, and other styles

17

Li, Jianyong, Chengbei Li, Jihui Han, Yuefeng Shi, Guibin Bian, and Shuai Zhou. "Robust Hand Gesture Recognition Using HOG-9ULBP Features and SVM Model." Electronics 11, no. 7 (2022): 988. http://dx.doi.org/10.3390/electronics11070988.

Full text

Abstract:

Hand gesture recognition is an area of study that attempts to identify human gestures through mathematical algorithms, and can be used in several fields, such as communication between deaf-mute people, human–computer interaction, intelligent driving, and virtual reality. However, changes in scale and angle, as well as complex skin-like backgrounds, make gesture recognition quite challenging. In this paper, we propose a robust recognition approach for multi-scale as well as multi-angle hand gestures against complex backgrounds. First, hand gestures are segmented from complex backgrounds using t

APA, Harvard, Vancouver, ISO, and other styles

18

Li, Ling Hua, and Ji Fang Du. "Visual Based Hand Gesture Recognition Systems." Applied Mechanics and Materials 263-266 (December 2012): 2422–25. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2422.

Full text

Abstract:

This paper describes the techniques used in visual based hand gesture recognition systems. The study is discussed from three aspects: the two categories, the five components, and the methods of feature extraction of visual based hand gesture recognition systems. The two categories are 3D model based systems and appearance model based systems. The five components are image sequences capture, pre-processing, hand regions detection, feature extraction and gesture classification. The methods of feature extraction are Hidden Markov Model (HMM), Artificial Neural Networks (ANN), and Support Vector M

APA, Harvard, Vancouver, ISO, and other styles

19

Olaniyi, Abiodun AYENI. "A Robust Facial Expression Recognition System for Android Devices." J. of Advancement in Engineering and Technology 7, no. 3 (2020): 04. https://doi.org/10.5281/zenodo.3750534.

Full text

Abstract:

This research work presents an idea for detecting an unknown human face in an input imagery and recognizing the facial expression. The objective of this research is to develop a highly intelligent android application for facial expression recognition. A Facial Expression Recognition system needs to solve the following problems: detection and location of faces in a clustered scene, facial feature extraction, and facial expression classification. In this research work three basic expressions were considered, which are: Happy, Sad, and Angry. Georgia Tech face detection dataset and some locally c

APA, Harvard, Vancouver, ISO, and other styles

20

Lu, Ming-Xing, Guo-Zhen Du, and Zhan-Fang Li. "Multimode Gesture Recognition Algorithm Based on Convolutional Long Short-Term Memory Network." Computational Intelligence and Neuroscience 2022 (March 2, 2022): 1–10. http://dx.doi.org/10.1155/2022/4068414.

Full text

Abstract:

Gesture recognition utilizes deep learning network model to automatically extract deep features of data; however, traditional machine learning algorithms rely on manual feature extraction and poor model generalization ability. In this paper, a multimodal gesture recognition algorithm based on convolutional long-term memory network is proposed. First, a convolutional neural network (CNN) is employed to automatically extract the deeply hidden features of multimodal gesture data. Then, a time series model is constructed using a long short-term memory (LSTM) network to learn the long-term dependen

APA, Harvard, Vancouver, ISO, and other styles

21

Zheng, Lianqing, Jie Bai, Xichan Zhu, et al. "Dynamic Hand Gesture Recognition in In-Vehicle Environment Based on FMCW Radar and Transformer." Sensors 21, no. 19 (2021): 6368. http://dx.doi.org/10.3390/s21196368.

Full text

Abstract:

Hand gesture recognition technology plays an important role in human-computer interaction and in-vehicle entertainment. Under in-vehicle conditions, it is a great challenge to design gesture recognition systems due to variable driving conditions, complex backgrounds, and diversified gestures. In this paper, we propose a gesture recognition system based on frequency-modulated continuous-wave (FMCW) radar and transformer for an in-vehicle environment. Firstly, the original range-Doppler maps (RDMs), range-azimuth maps (RAMs), and range-elevation maps (REMs) of the time sequence of each gesture a

APA, Harvard, Vancouver, ISO, and other styles

22

Arozi, Moh, Wahyu Caesarendra, Mochammad Ariyanto, M. Munadi, Joga D. Setiawan, and Adam Glowacz. "Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements." Symmetry 12, no. 4 (2020): 541. http://dx.doi.org/10.3390/sym12040541.

Full text

Abstract:

A number of researchers prefer using multi-channel surface electromyography (sEMG) pattern recognition in hand gesture recognition to increase classification accuracy. Using this method can lead to computational complexity. Hand gesture classification by employing single channel sEMG signal acquisition is quite challenging, especially for low-rate sampling frequency. In this paper, a study on the pattern recognition method for sEMG signals of nine finger movements is presented. Common surface single channel electromyography (sEMG) was used to measure five different subjects with no neurologica

APA, Harvard, Vancouver, ISO, and other styles

23

Hellara, Hiba, Rim Barioul, Salwa Sahnoun, Ahmed Fakhfakh, and Olfa Kanoun. "Comparative Study of sEMG Feature Evaluation Methods Based on the Hand Gesture Classification Performance." Sensors 24, no. 11 (2024): 3638. http://dx.doi.org/10.3390/s24113638.

Full text

Abstract:

Effective feature extraction and selection are crucial for the accurate classification and prediction of hand gestures based on electromyographic signals. In this paper, we systematically compare six filter and wrapper feature evaluation methods and investigate their respective impacts on the accuracy of gesture recognition. The investigation is based on several benchmark datasets and one real hand gesture dataset, including 15 hand force exercises collected from 14 healthy subjects using eight commercial sEMG sensors. A total of 37 time- and frequency-domain features were extracted from each

APA, Harvard, Vancouver, ISO, and other styles

24

Theresa, W. Gracy, S. Santhana Prabha, D. Thilagavathy, and S. Pournima. "Analysis of the Efficacy of Real-Time Hand Gesture Detection with Hog and Haar-Like Features Using SVM Classification." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 2s (2022): 199–207. http://dx.doi.org/10.17762/ijritcc.v10i2s.5929.

Full text

Abstract:

The field of hand gesture recognition has recently reached new heights thanks to its widespread use in domains like remote sensing, robotic control, and smart home appliances, among others. Despite this, identifying gestures is difficult because of the intransigent features of the human hand, which make the codes used to decode them illegible and impossible to compare. Differentiating regional patterns is the job of pattern recognition. Pattern recognition is at the heart of sign language. People who are deaf or mute may understand the spoken language of the rest of the world by learning sign

APA, Harvard, Vancouver, ISO, and other styles

25

Saykol, Ediz, Halit Talha Türe, Ahmet Mert Sirvanci, and Mert Turan. "Posture labeling based gesture classification for Turkish sign language using depth values." Kybernetes 45, no. 4 (2016): 604–21. http://dx.doi.org/10.1108/k-04-2015-0107.

Full text

Abstract:

Purpose – The purpose of this paper to classify a set of Turkish sign language (TSL) gestures by posture labeling based finite-state automata (FSA) that utilize depth values in location-based features. Gesture classification/recognition is crucial not only in communicating visually impaired people but also for educational purposes. The paper also demonstrates the practical use of the techniques for TSL. Design/methodology/approach – Gesture classification is based on the sequence of posture labels that are assigned by location-based features, which are invariant under rotation and scale. Grid-

APA, Harvard, Vancouver, ISO, and other styles

26

Zhao, Chuanxin, Fei Xiong, Taochun Wang, Yang Wang, Fulong Chen, and Zhiqiang Xu. "Wear-free gesture recognition based on residual features of RFID signals." Intelligent Data Analysis 26, no. 4 (2022): 1051–70. http://dx.doi.org/10.3233/ida-215972.

Full text

Abstract:

Traditionally, RFID is frequently used in identification and localization. In this paper, an extension application of RFID is designed to recognize gestures. Currently, gesture recognition is mainly used for feature extraction through wearable sensors and video cameras, which have shortcomings such as inconvenience to carry and interference with obstacles. This paper proposes a gesture recognition system based on radio frequency identification (RFID), where users do not need to wear devices. In the proposed model, the interference information generated by the gesture action on the tag signal i

APA, Harvard, Vancouver, ISO, and other styles

27

Erizka, Banuwati Candrasari, Novamizanti Ledya, and Aulia Suci. "Hand gesture recognition using discrete wavelet transform and hidden Markov models." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 5 (2020): 2265–75. https://doi.org/10.12928/TELKOMNIKA.v18i5.13725.

Full text

Abstract:

Gesture recognition based on computer-vision is an important part of human-computer interaction. But it lacks in several points, that was image brightness, recognition time, and accuracy. Because of that goal of this research was to create a hand gesture recognition system that had good performances using discrete wavelet transform and hidden Markov models. The first process was pre-processing, which done by resizing the image to 128x128 pixels and then segmented the skin color. The second process was feature extraction using the discrete wavelet transform. The result was the feature value in

APA, Harvard, Vancouver, ISO, and other styles

28

Wang, Yu. "Research on the Construction of Human-Computer Interaction System Based on a Machine Learning Algorithm." Journal of Sensors 2022 (January 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/3817226.

Full text

Abstract:

In this paper, we use machine learning algorithms to conduct in-depth research and analysis on the construction of human-computer interaction systems and propose a simple and effective method for extracting salient features based on contextual information. The method can retain the dynamic and static information of gestures intact, which results in a richer and more robust feature representation. Secondly, this paper proposes a dynamic planning algorithm based on feature matching, which uses the consistency and accuracy of feature matching to measure the similarity of two frames and then uses

APA, Harvard, Vancouver, ISO, and other styles

29

Paraskevopoulos, Georgios, Evaggelos Spyrou, Dimitrios Sgouropoulos, Theodoros Giannakopoulos, and Phivos Mylonas. "Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data." Algorithms 12, no. 5 (2019): 108. http://dx.doi.org/10.3390/a12050108.

Full text

Abstract:

In this paper we present an approach towards real-time hand gesture recognition using the Kinect sensor, investigating several machine learning techniques. We propose a novel approach for feature extraction, using measurements on joints of the extracted skeletons. The proposed features extract angles and displacements of skeleton joints, as the latter move into a 3D space. We define a set of gestures and construct a real-life data set. We train gesture classifiers under the assumptions that they shall be applied and evaluated to both known and unknown users. Experimental results with 11 classi

APA, Harvard, Vancouver, ISO, and other styles

30

Dunai, Larisa, Isabel Seguí Verdú, Dinu Turcanu, and Viorel Bostan. "Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network." Technologies 13, no. 1 (2025): 21. https://doi.org/10.3390/technologies13010021.

Full text

Abstract:

Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then,

APA, Harvard, Vancouver, ISO, and other styles

31

Wang, Yong, Di Wang, Yunhai Fu, Dengke Yao, Liangbo Xie, and Mu Zhou. "Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor." Remote Sensing 14, no. 10 (2022): 2374. http://dx.doi.org/10.3390/rs14102374.

Full text

Abstract:

With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-h

APA, Harvard, Vancouver, ISO, and other styles

32

Ewe, Edmond Li Ren, Chin Poo Lee, Lee Chung Kwek, and Kian Ming Lim. "Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier." Applied Sciences 12, no. 15 (2022): 7643. http://dx.doi.org/10.3390/app12157643.

Full text

Abstract:

Gesture recognition has been studied for a while within the fields of computer vision and pattern recognition. A gesture can be defined as a meaningful physical movement of the fingers, hands, arms, or other parts of the body with the purpose to convey information for the environment interaction. For instance, hand gesture recognition (HGR) can be used to recognize sign language which is the primary means of communication by the deaf and mute. Vision-based HGR is critical in its application; however, there are challenges that will need to be overcome such as variations in the background, illum

APA, Harvard, Vancouver, ISO, and other styles

33

Nagadeepa.Ch, Dr.N.Balaji, and Dr.V.Padmaja. "ANALYSIS OF INERTIAL SENSOR DATA USING TRAJECTORY RECOGNITION ALGORITHM." International Journal on Cybernetics & Informatics (IJCI) 5, no. 4 (2017): 101–7. https://doi.org/10.5121/ijci.2016.5412.

Full text

Abstract:

This paper describes a digital pen based on IMU sensor for gesture and handwritten digit gesture trajectory recognition applications. This project allows human and Pc interaction. Handwriting Recognition is mainly used for applications in the field of security and authentication. By using embedded pen the user can make hand gesture or write a digit and also an alphabetical character. The embedded pen contains an inertial sensor, microcontroller and a module having Zigbee wireless transmitter for creating handwriting and trajectories using gestures. The propound trajectory recognition algorithm

APA, Harvard, Vancouver, ISO, and other styles

34

IZADPANAHI, SHIMA, and ÖNSEN TOYGAR. "HUMAN AGE CLASSIFICATION WITH OPTIMAL GEOMETRIC RATIOS AND WRINKLE ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 02 (2014): 1456003. http://dx.doi.org/10.1142/s0218001414560035.

Full text

Abstract:

This paper presents geometric feature-based model for age group classification of facial images. The feature extraction is performed considering significance of the effects that age has on facial anthropometry. Particle Swarm Optimization (PSO) technique is used to find optimized subset of geometric features. The relevance and importance of age differentiation capability of the features are evaluated using support vector classifier. The facial images are categorized in seven major age groups. The effectiveness and accuracy of the proposed feature extraction is demonstrated with the experiments

APA, Harvard, Vancouver, ISO, and other styles

35

Kaushik, Kartik. "Hand Gestures for Personal Computer Control." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5183–92. http://dx.doi.org/10.22214/ijraset.2024.60894.

Full text

Abstract:

Abstract: Hand gestures have emerged as a promising modality for enhancing personal computer (PC) control, offering intuitive and natural interaction methods. This research paper explores the design, implementation, and evaluation of a hand gesture recognition system for PC interaction. We review existing methods of PC control and discuss the limitations of traditional input modalities. The paper outlines the process of hand gesture recognition, including data acquisition, preprocessing, feature extraction, and classification. We describe the design considerations and implementation details of

APA, Harvard, Vancouver, ISO, and other styles

36

Lou, Xinyue. "Vision-based Hand Gesture Recognition Technology." Applied and Computational Engineering 141, no. 1 (2025): 54–59. https://doi.org/10.54254/2755-2721/2025.21696.

Full text

Abstract:

Human-computer interaction has a wide range of application prospects in many fields such as medicine, entertainment, industry and education. Gesture recognition is one of the most important technologies for gesture interaction between humans and robots, and visual gesture recognition increases the user's comfort and freedom compared with data glove recognition. This paper summarizes the general process of visual gesture recognition based on the literature, including three steps: pre-processing, feature extraction, and gesture classification. It also defines static and dynamic gestures and make

APA, Harvard, Vancouver, ISO, and other styles

37

Ismail, Mohammad H., Shefa A. Dawwd, and Fakhradeen H. Ali. "Static hand gesture recognition of Arabic sign language by using deep CNNs." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 178. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp178-188.

Full text

Abstract:

An Arabic sign language recognition using two concatenated deep convolution neural network models DenseNet121 &amp; VGG16 is presented. The pre-trained models are fed with images, and then the system can automatically recognize the Arabic sign language. To evaluate the performance of concatenated two models in the Arabic sign language recognition, the red-green-blue (RGB) images for various static signs are collected in a dataset. The dataset comprises 220,000 images for 44 categories: 32 letters, 11 numbers (0:10), and 1 for none. For each of the static signs, there are 5000 images collec

APA, Harvard, Vancouver, ISO, and other styles

38

Ismail, Mohammad H., Shefa A. Dawwd, and Fakhradeen H. Ali. "Static hand gesture recognition of Arabic sign language by using deep CNNs." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 178–88. https://doi.org/10.11591/ijeecs.v24.i1.pp178-188.

Full text

Abstract:

An Arabic sign language recognition using two concatenated deep convolution neural network models DenseNet121 & VGG16 is presented. The pre-trained models are fed with images, and then the system can automatically recognize the Arabic sign language. To evaluate the performance of concatenated two models in the Arabic sign language recognition, the red-green-blue (RGB) images for various static signs are collected in a dataset. The dataset comprises 220,000 images for 44 categories: 32 letters, 11 numbers (0:10), and 1 for none. For each of the static signs, there are 5000 images collected

APA, Harvard, Vancouver, ISO, and other styles

39

Aljumaily, Mustafa S., and Ghaida A. Al-Suhail. "Towards ubiquitous human gestures recognition using wireless networks." International Journal of Pervasive Computing and Communications 13, no. 4 (2017): 408–18. http://dx.doi.org/10.1108/ijpcc-d-17-00005.

Full text

Abstract:

Purpose Recently, many researches have been devoted to studying the possibility of using wireless signals of the Wi-Fi networks in human-gesture recognition. They focus on classifying gestures despite who is performing them, and only a few of the previous work make use of the wireless channel state information in identifying humans. This paper aims to recognize different humans and their multiple gestures in an indoor environment. Design/methodology/approach The authors designed a gesture recognition system that consists of channel state information data collection, preprocessing, features ext

APA, Harvard, Vancouver, ISO, and other styles

40

Sharma, Naina, Vaishali Nirgude, Tanya Shah, Chirag Bhagat, Amithesh Gupta, and Yash Gupta. "GESTURE RECOGNITION FOR TOUCH-FREE PC CONTROL USING A NEURAL NETWORK APPROACH." ICTACT Journal on Data Science and Machine Learning 5, no. 4 (2024): 690–97. https://doi.org/10.21917/ijdsml.2024.0142.

Full text

Abstract:

In the pursuit of advancing the field of touch-free human-computer interaction, this paper is focused on developing a gesture enabled PC control system that aims for enhancing user engagement and providing intuitive and flexible control methods, across various applications, particularly those benefiting individuals with mobility impairments. This system has expanding potential use in virtual and augmented reality environments. This study describes a unique method for temporal gesture identification that employs gesture kinematics for feature extraction and classification. Real-time hand tracki

APA, Harvard, Vancouver, ISO, and other styles

41

Magrofuoco, Nathan, Paolo Roselli, and Jean Vanderdonckt. "Two-dimensional Stroke Gesture Recognition." ACM Computing Surveys 54, no. 7 (2021): 1–36. http://dx.doi.org/10.1145/3465400.

Full text

Abstract:

The expansion of touch-sensitive technologies, ranging from smartwatches to wall screens, triggered a wider use of gesture-based user interfaces and encouraged researchers to invent recognizers that are fast and accurate for end-users while being simple enough for practitioners. Since the pioneering work on two-dimensional (2D) stroke gesture recognition based on feature extraction and classification, numerous approaches and techniques have been introduced to classify uni- and multi-stroke gestures, satisfying various properties of articulation-, rotation-, scale-, and translation-invariance.

APA, Harvard, Vancouver, ISO, and other styles

42

Priya, Sathiya, and Sumathi B. "Feature Extraction Based on Cellular Particle Swarm Optimization Algorithm for American Sign Language Images." International Journal of Innovative Research in Information Security 09, no. 03 (2023): 204–11. http://dx.doi.org/10.26562/ijiris.2023.v0903.27.

Full text

Abstract:

Image Recognition is becoming a critical task and many problem-solving systems and approaches for image detection, analysis and classification are introduced by many modern researcher. The techniques should be user friendly and ease to interpret for both normal people and special people, it they should be analysed. The sign language act as a mode to transfer and exchange the message, information, knowledge and ideas from deaf to common people. The gaining information and responses to the pattern or gesture is called as sign. Sign Language is only mode to communication between the hearing-impai

APA, Harvard, Vancouver, ISO, and other styles

43

Akhtar, Zain Ul Abiden, and Hongyu Wang. "WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach." Applied Sciences 9, no. 24 (2019): 5268. http://dx.doi.org/10.3390/app9245268.

Full text

Abstract:

In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-

APA, Harvard, Vancouver, ISO, and other styles

44

Freitas, Melissa La Banca, José Jair Alves Mendes, Thiago Simões Dias, Hugo Valadares Siqueira, and Sergio Luiz Stevan. "Surgical Instrument Signaling Gesture Recognition Using Surface Electromyography Signals." Sensors 23, no. 13 (2023): 6233. http://dx.doi.org/10.3390/s23136233.

Full text

Abstract:

Surgical Instrument Signaling (SIS) is compounded by specific hand gestures used by the communication between the surgeon and surgical instrumentator. With SIS, the surgeon executes signals representing determined instruments in order to avoid error and communication failures. This work presented the feasibility of an SIS gesture recognition system using surface electromyographic (sEMG) signals acquired from the Myo armband, aiming to build a processing routine that aids telesurgery or robotic surgery applications. Unlike other works that use up to 10 gestures to represent and classify SIS ges

APA, Harvard, Vancouver, ISO, and other styles

45

Alabdullah, Bayan Ibrahimm, Hira Ansar, Naif Al Mudawi, et al. "Smart Home Automation-Based Hand Gesture Recognition Using Feature Fusion and Recurrent Neural Network." Sensors 23, no. 17 (2023): 7523. http://dx.doi.org/10.3390/s23177523.

Full text

Abstract:

Gestures have been used for nonverbal communication for a long time, but human–computer interaction (HCI) via gestures is becoming more common in the modern era. To obtain a greater recognition rate, the traditional interface comprises various devices, such as gloves, physical controllers, and markers. This study provides a new markerless technique for obtaining gestures without the need for any barriers or pricey hardware. In this paper, dynamic gestures are first converted into frames. The noise is removed, and intensity is adjusted for feature extraction. The hand gesture is first detected

APA, Harvard, Vancouver, ISO, and other styles

46

Zhou, Qizhen, Jianchun Xing, Wei Chen, Xuewei Zhang, and Qiliang Yang. "From Signal to Image: Enabling Fine-Grained Gesture Recognition with Commercial Wi-Fi Devices." Sensors 18, no. 9 (2018): 3142. http://dx.doi.org/10.3390/s18093142.

Full text

Abstract:

Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in wireless sensing hold promise for a ubiquitous, non-invasive and low-cost system with existing Wi-Fi infrastructures. In this paper, we propose DeepNum, which enables fine-grained finger gesture recognition with only a pair of commercial Wi-Fi devices. The key insight of DeepNum is to incorporate the quintessence of deep learning-based image process

APA, Harvard, Vancouver, ISO, and other styles

47

Bhuiyan, Rasel Ahmed, Abdul Matin, Md Shafiur Raihan Shafi, and Amit Kumar Kundu. "A Bag-of-Words Based Feature Extraction Scheme for American Sign Language Number Recognition from Hand Gesture Images." International Journal of Machine Learning and Computing 11, no. 1 (2021): 85–91. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1018.

Full text

Abstract:

Human Computer Interaction (HCI) focuses on the interaction between humans and machines. An extensive list of applications exists for hand gesture recognition techniques, major candidates for HCI. The list covers various fields, one of which is sign language recognition. In this field, however, high accuracy and robustness are both needed; both present a major challenge. In addition, feature extraction from hand gesture images is a tough task because of the many parameters associated with them. This paper proposes an approach based on a bag-of-words (BoW) model for automatic recognition of Ame

APA, Harvard, Vancouver, ISO, and other styles

48

Remya PK, Rajkumar KK. "DHGNet: Devatha Hastha Gesture Network with Advanced Graph Enhancement for Gesture Identification and Recognition." Journal of Information Systems Engineering and Management 10, no. 28s (2025): 520–32. https://doi.org/10.52783/jisem.v10i28s.4353.

Full text

Abstract:

This study aims to develop an AI-powered system to classify and interpret Devatha Hasthas in Indian classical dance. By combining cultural preservation with modern technology, the system enhances accessibility and supports effective learning and documentation of intricate hand gestures, contributing to the promotion and understanding of traditional art forms. The study utilized a dataset of 16 Devatha Hasthas, MediaPipe hand tracking for segmentation, and feature extraction combining Hu moments and VGG19. Dimensionality reduction was performed using an ExtraTree classifier, followed by gesture

APA, Harvard, Vancouver, ISO, and other styles

49

Mopidevi, Suneetha, Shivananda Biradhar, Neha Bobberla, and Kiran Sai Buddati. "Hand gesture recognition and voice conversion for deaf and Dumb." E3S Web of Conferences 391 (2023): 01060. http://dx.doi.org/10.1051/e3sconf/202339101060.

Full text

Abstract:

In this paper, we purpose a Hand gesture recognition model which can be used in real time application. This model is based on the mediapipe frame work of the google, Tensor flow in openCv and python and classification using feed forward neural network with keras model. The structure of the proposed work consists of 3 modules: Grabbing the frames, detecting hand landmarks and classification. The proposed model has the accuracy 95.7% at recognizing 10 kinds of hand gestures(Thumbs up, Thumbs down, Peace, Smile, Rock, Ok, Fist, livelong, call me, stop). A hand gesture recognition model that react

APA, Harvard, Vancouver, ISO, and other styles

50

Niu, Yinxi, Wensheng Chen, Hui Zeng, Zhenhua Gan, and Baoping Xiong. "Optimizing sEMG Gesture Recognition: Leveraging Channel Selection and Feature Compression for Improved Accuracy and Computational Efficiency." Applied Sciences 14, no. 8 (2024): 3389. http://dx.doi.org/10.3390/app14083389.

Full text

Abstract:

In the task of upper-limb pattern recognition, effective feature extraction, channel selection, and classification methods are crucial for the construction of an efficient surface electromyography (sEMG) signal classification framework. However, existing deep learning models often face limitations due to improper channel selection methods and overly specific designs, leading to high computational complexity and limited scalability. To address this challenge, this study introduces a deep learning network based on channel feature compression—partial channel selection sEMG net (PCS-EMGNet). This

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!