Relevant bibliographies by topics / Hand gesture detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Hand gesture detection'

Author: Grafiati
Published: 3 June 2025
Last updated: 20 July 2025

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Journal articles on the topic "Hand gesture detection"

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Shaha, Arya Ashish, Sanika Ashok Salunkhe, Milind Mahadeo Gargade, and Rutuja Vijay Kumbhar. "Hand Gesture Detection with Indication." International Journal of Engineering Research for Sustainable Development 1, no. 1 (2025): 1–4. https://doi.org/10.5281/zenodo.15332400.

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AbstractThis paper presents a method for hand gesture detection and indication using a flex sensor and Arduino. Hand gesture detection using flexible sensors and Arduino platforms offers an innovative approach to human-computer interaction, enabling intuitive control systems in various applications such as robotics, gaming, and assistive technology. This paper presents a system that utilizes flex sensors integrated with Arduino microcontrollers to detect hand gestures and provide real-time indications based on the recognized gestures. Flex sensors, which measure the degree of bending in finger
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Shaha, Arya Ashish, Sanika Ashok Salunkhe, Milind Mahadeo Gargade, and Rutuja Vijay Kumbhar. "Hand Gesture Detection with Indication." International Journal of Engineering Research for Sustainable Development 1, no. 1 (2025): 1–4. https://doi.org/10.5281/zenodo.15332725.

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<em>Abstract</em> <em>This paper presents a method for hand gesture detection and indication using a flex sensor and Arduino. </em><em>Hand gesture detection using flexible sensors and Arduino platforms offers an innovative approach to human-computer interaction, enabling intuitive control systems in various applications such as robotics, gaming, and assistive technology. This paper presents a system that utilizes flex sensors integrated with Arduino microcontrollers to detect hand gestures and provide real-time indications based on the recognized gestures. Flex sensors, which measure the degr
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Prananta, Gidion Bagas, Hagi Azzam Azzikri, and Chaerur Rozikin. "REAL-TIME HAND GESTURE DETECTION AND RECOGNITION USING CONVOLUTIONAL ARTIFICIAL NEURAL NETWORKS." METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi 9, no. 2 (2023): 30–34. http://dx.doi.org/10.46880/mtk.v9i2.1911.

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Real-time hand gesture detection is an interesting topic in pattern recognition and computer vision. In this study, we propose the use of a Convolutional Neural Network (CNN) to detect and recognize hands in real-time. Our goal is to develop a system that can accurately identify and interpret user gestures in real-time. The proposed approach involves two main stages, namely hand gesture recognition and gesture recognition. For stage detection, we use the CNN architecture to recognize hands in the video. We train the CNN model using a dataset containing various hand gestures. Once a hand is det
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Nyirarugira, Clementine, Hyo-rim Choi, and TaeYong Kim. "Hand Gesture Recognition Using Particle Swarm Movement." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1919824.

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We present a gesture recognition method derived from particle swarm movement for free-air hand gesture recognition. Online gesture recognition remains a difficult problem due to uncertainty in vision-based gesture boundary detection methods. We suggest an automated process of segmenting meaningful gesture trajectories based on particle swarm movement. A subgesture detection and reasoning method is incorporated in the proposed recognizer to avoid premature gesture spotting. Evaluation of the proposed method shows promising recognition results: 97.6% on preisolated gestures, 94.9% on stream gest
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Yu, Myoungseok, Narae Kim, Yunho Jung, and Seongjoo Lee. "A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar." Sensors 20, no. 8 (2020): 2321. http://dx.doi.org/10.3390/s20082321.

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In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the co
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USHA, M. NITHIN GOWDA H. SANTHOSH M. NANDA KUMAR S. NUTHAN PAWAR E. "REAL-TIME HAND SIGN TRAINING AND DETECTION." International Journal For Technological Research In Engineering 11, no. 5 (2024): 149–51. https://doi.org/10.5281/zenodo.10554229.

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Real-time hand sign recognition and detection are major for applications in human computer interaction, sign language interpretation, and gesture-based control systems.This project focuses on creating realtime hand gesture and finger gesture annotations using the MediaPipe framework in Python.The &nbsp;hand gestures and finger gestures using keypoints and finger coordinates found by the MediaPipe framework.The system offers two machine learning models: one for recognizing hand signs and another for detecting finger gestures. It provides resources, including sample programs, model files, and tr
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Herbert, Oswaldo Mendoza, David Pérez-Granados, Mauricio Alberto Ortega Ruiz, Rodrigo Cadena Martínez, Carlos Alberto González Gutiérrez, and Marco Antonio Zamora Antuñano. "Static and Dynamic Hand Gestures: A Review of Techniques of Virtual Reality Manipulation." Sensors 24, no. 12 (2024): 3760. http://dx.doi.org/10.3390/s24123760.

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This review explores the historical and current significance of gestures as a universal form of communication with a focus on hand gestures in virtual reality applications. It highlights the evolution of gesture detection systems from the 1990s, which used computer algorithms to find patterns in static images, to the present day where advances in sensor technology, artificial intelligence, and computing power have enabled real-time gesture recognition. The paper emphasizes the role of hand gestures in virtual reality (VR), a field that creates immersive digital experiences through the Ma blend
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Aryan, Gupta, Narula Rachna, Garg Parth, Joshi Diksha, and Upadhyay Navneet. "Touchless Operations Using Hand Gestures Detection." Recent Innovations in Wireless Network Security 5, no. 3 (2023): 31–40. https://doi.org/10.5281/zenodo.8285242.

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<em>Humans have only recently begun using hand gestures to interact with computers. The integration of the real and digital worlds is the aim of gesture recognition. It is considerably simpler to convey our intentions and ideas to the computer via hand gestures. A simple and efficient touchless method of interacting with computer systems is through hand gestures. However, the limited end-user adoption of hand gesture-based systems is mostly caused by the significant technical challenges involved in successfully identifying in-air movements. Image recognition is one of the many ways that a comp
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Meng, Yuting, Haibo Jiang, Nengquan Duan, and Haijun Wen. "Real-Time Hand Gesture Monitoring Model Based on MediaPipe’s Registerable System." Sensors 24, no. 19 (2024): 6262. http://dx.doi.org/10.3390/s24196262.

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Hand gesture recognition plays a significant role in human-to-human and human-to-machine interactions. Currently, most hand gesture detection methods rely on fixed hand gesture recognition. However, with the diversity and variability of hand gestures in daily life, this paper proposes a registerable hand gesture recognition approach based on Triple Loss. By learning the differences between different hand gestures, it can cluster them and identify newly added gestures. This paper constructs a registerable gesture dataset (RGDS) for training registerable hand gesture recognition models. Addition
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Naveen, Y., and Ch Navya Sree. "GestureFlow: Advanced Hand Gesture Control System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44540.

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Our project "GestureFlow: Advanced Hand Gesture Control System" leverages real-time computer vision and deep learning techniques to create a robust, touchless control interface using hand gestures. The system utilizes MediaPipe Hands for efficient hand landmark detection and processes dynamic hand movements and finger configurations to identify a wide range of intuitive gestures such as swipes, pinches, and specific finger patterns. These gestures are mapped to actions like mouse control, clicks, volume adjustment, media playback, screenshot capture, window management, and many more. The syste
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Dissertations / Theses on the topic "Hand gesture detection"

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Khan, Muhammad. "Hand Gesture Detection & Recognition System." Thesis, Högskolan Dalarna, Datateknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6496.

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The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algori
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Gingir, Emrah. "Hand Gesture Recognition System." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612532/index.pdf.

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This thesis study presents a hand gesture recognition system, which replaces input devices like keyboard and mouse with static and dynamic hand gestures, for interactive computer applications. Despite the increase in the attention of such systems there are still certain limitations in literature. Most applications require different constraints like having distinct lightning conditions, usage of a specific camera, making the user wear a multi-colored glove or need lots of training data. The system mentioned in this study disables all these restrictions and provides an adaptive, effort free envi
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Jia, Jia. "Interactive Imaging via Hand Gesture Recognition." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4259.

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With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. As a sub-field, Interactive Image Processing emphasizes particularly on the communications between machine and human. The basic flowchart is definition of object, analysis and training phase, recognition and feedback. Generally speaking, the core issue is how we define the interesting object and track them more accurately in order to complete the interaction process successfully. This thesis
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Saremi, Shahrzad. "Evolutionary Hand Posture Estimation for Image-based Gesture Detection Systems." Thesis, Griffith University, 2018. http://hdl.handle.net/10072/374748.

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Hand posture estimation is one of the main phases of hand gesture estimation. It refers to the process of estimating a real hand image obtained from an acquisition device in a computer. The accuracy of the estimated hand model has a direct impact on feature selection and classi cation. The current hand posture estimation techniques are divided into three classes: generative, discriminative, and hybrid methods. In the rst, a model of the hand is generated and improved to model a hand in a computer accurately. In the second class, a database of di erent hand images is created and used to estima
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Dardas, Nasser Hasan Abdel-Qader. "Real-time Hand Gesture Detection and Recognition for Human Computer Interaction." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23499.

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This thesis focuses on bare hand gesture recognition by proposing a new architecture to solve the problem of real-time vision-based hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bare hand in a cluttered background using face subtraction, skin detection and contour comparison. The second stage allows recognizing hand gestures using bag-of-features and multi-class Support Vector Machine (SVM) algorithms. Finally, a grammar has been developed to generate gesture commands for ap
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Abedan, Kondori Farid. "Bring Your Body into Action : Body Gesture Detection, Tracking, and Analysis for Natural Interaction." Doctoral thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-88508.

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Due to the large influx of computers in our daily lives, human-computer interaction has become crucially important. For a long time, focusing on what users need has been critical for designing interaction methods. However, new perspective tends to extend this attitude to encompass how human desires, interests, and ambitions can be met and supported. This implies that the way we interact with computers should be revisited. Centralizing human values rather than user needs is of the utmost importance for providing new interaction techniques. These values drive our decisions and actions, and are e
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Jaroň, Lukáš. "Ovládání počítače gesty." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236609.

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This masters thesis describes possibilities and principles of gesture-based computer interface. The work describes general approaches for gesture control.  It also deals with implementation of the selected detection method of the hands and fingers using depth maps loaded form Kinect sensor. The implementation also deals with gesture recognition using hidden Markov models. For demonstration purposes there is also described implementation of a simple photo viewer that uses developed gesture-based computer interface. The work also focuses on quality testing and accuracy evaluation for selected ge
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Medeiros, Rafael Sachett. "Detecção de pele humana utilizando modelos estocásticos multi-escala de textura." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/70193.

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A detecção de gestos é uma etapa importante em aplicações de interação humanocomputador. Se a mão do usuário é detectada com precisão, tanto a análise quanto o reconhecimento do gesto de mão se tornam mais simples e confiáveis. Neste trabalho, descrevemos um novo método para detecção de pele humana, destinada a ser empregada como uma etapa de pré-processamento para segmentação de gestos de mão em sistemas que visam o seu reconhecimento. Primeiramente, treinamos os modelos de cor e textura de pele (material a ser identificado) a partir de um conjunto de treinamento formado por imagens de pele.
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Mráz, Stanislav. "Rozpoznání gest ruky v obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219059.

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This master’s thesis is dealing with recognition of an easy static gestures in order to computer controlling. First part of this work is attended to the theoretical review of methods used to hand segmentation from the image. Next methods for hang gesture classification are described. The second part of this work is devoted to choice of suitable method for hand segmentation based on skin color and movement. Methods for hand gesture classification are described in next part. Last part of this work is devoted to description of proposed system.
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Tan, Jason, and Jake O'Donnell. "Hand gestures as a trigger for system initialisation." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20876.

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Biometriska lösningar för åtkomstkontroll är ett blomstrande koncept. Precise Biometrics är ett företag som fokuserar på just biometriska lösningar relaterade till åtkomstkontroll. YOUNiQ är en produkt som fokuserar på ansiktsigenkänning. Denna produkt använder ansiktsigenkännig för att ge åtkomst till registrerade användare i systemet. Ett problem som uppstår vid att använda ansiktsigenkänning är att alla som befinner sig tillräckligt nära kameran blir skannade, även de som inte är registrerade. Denna avhandlingen har som mål att implementera ett avsiktsmedvetet system som använder en utlösar
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Book chapters on the topic "Hand gesture detection"

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Chaudhary, Ankit. "Fingertips Detection." In Robust Hand Gesture Recognition for Robotic Hand Control. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4798-5_6.

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Alashhab, Samer, Antonio-Javier Gallego, and Miguel Ángel Lozano. "Hand Gesture Detection with Convolutional Neural Networks." In Distributed Computing and Artificial Intelligence, 15th International Conference. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94649-8_6.

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Viejo-López, Roberto, Virginia Riego del Castillo, and Lidia Sánchez-González. "Hand SOS Gesture Detection by Computer Vision." In International Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022) 13th International Conference on EUropean Transnational Education (ICEUTE 2022). Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18409-3_3.

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Yang, Ming-Hsuan, and Narendra Ahuja. "Recognizing Hand Gestures Using Motion Trajectories." In Face Detection and Gesture Recognition for Human-Computer Interaction. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1423-7_3.

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Bhuyan, M. K., D. Ghosh, and P. K. Bora. "Continuous Hand Gesture Segmentation and Co-articulation Detection." In Computer Vision, Graphics and Image Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_50.

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Nemati, Hassan Mashad, Yuantao Fan, and Fernando Alonso-Fernandez. "Hand Detection and Gesture Recognition Using Symmetric Patterns." In Recent Developments in Intelligent Information and Database Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31277-4_32.

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Strutz, Tilo, Alexander Leipnitz, and Björn Senkel. "Robust Webcam-Based Hand Detection for Initialisation of Hand-Gesture Communication." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99582-3_27.

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Nölker, Claudia, and Helge Ritter. "Detection of fingertips in human hand movement sequences." In Gesture and Sign Language in Human-Computer Interaction. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0053001.

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Liu, Tao, Fei Jiang, and Ruimin Shen. "Fast and Accurate Hand-Raising Gesture Detection in Classroom." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63820-7_26.

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Singh, Krishna Kumar, and Konkona Baruah. "Hand Gesture Detection Using Deep Learning in Bharat Natyam." In Proceedings of the 6th International Conference on Communications and Cyber Physical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7137-4_42.

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Conference papers on the topic "Hand gesture detection"

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Madaan, Vijay, Neha Sharma, Deepak Gupta, and Srinivas Aluvala. "Hand Gesture Detection Using VGG-16." In 2024 International Conference on Information Science and Communications Technologies (ICISCT). IEEE, 2024. https://doi.org/10.1109/icisct64202.2024.10956830.

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Bondarets, Ivan, Veronika Prokhorchuk, Andrii Kozyr, and Oleksandr Trunov. "On-device hand model for robust gesture detection." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888950.

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Subramanian, R. Raja, V. Devamani, S. Prasanna, U. Sravanthi, V. Pranitha, and M. Sunkireddy Hamsika. "Automated Real-Time Hand Gesture Detection for Bharatanatyam Dance." In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). IEEE, 2025. https://doi.org/10.1109/iccrtee64519.2025.11053091.

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Gupta, Sumita, Sapna Gambhir, Rana Majumdar, Susmita Sen, Soumen Chatterjee, and Supriya Kangsabanik. "Real Time Hand Gesture Detection and Recognition to Control PowerPoint Slides." In 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI). IEEE, 2025. https://doi.org/10.1109/iccsai64074.2025.11064135.

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Setyaji, Rizko Trinanda, Rudy Dikairono, and Totok Mujiono. "Applying YOLO Model in iCAR for Multi-Detection: Object Detection and Hand Gesture Recognition." In 2024 5th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON). IEEE, 2024. http://dx.doi.org/10.1109/times-icon61890.2024.10630766.

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Yang, Yu-Yu, Hsu-Han Yang, and Jung-Kuei Yang. "YOLO-LRHG: Long Range Hand Gesture detection using YOLO with attention mechanism." In 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceict61637.2024.10671140.

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Kulkarni, Manas Girish, Revati Tushar Aute, Rajlakshmi Nilesh Desai, Atharva Vishwas Deshpande, and Dipti Pratik Pandit. "Real-Time Gender Classification Using MiniXception and Hand Gesture Detection Using MediaPipe Framework." In 2025 International Conference on Computational, Communication and Information Technology (ICCCIT). IEEE, 2025. https://doi.org/10.1109/icccit62592.2025.10928023.

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Shaaban, Ahmed, Zeineb Chaabouni, Maximilian Strobel, Wolfgang Furtner, Robert Weigel, and Fabian Lurz. "Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach." In 2024 Smart Systems Integration Conference and Exhibition (SSI). IEEE, 2024. http://dx.doi.org/10.1109/ssi63222.2024.10740507.

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Gupta, Sunil, Sohan Lal Gupta, Rahul Chauhan, Kireet Joshi, Ashish K. Sharma, and Chander Prabha. "Detection of Moving Objects in Two Dimensional Plane By Hand Gesture Recognition Using Convolution Neural Networks." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10687923.

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Hernandez, Alexander A., Arlene R. Caballero, and Porsche L. Amo. "Performance Analysis of Deep Learning Approaches on Traffic Controller Hand Gesture Detection for Autonomous Driving Cars." In 2024 IEEE 15th Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2024. http://dx.doi.org/10.1109/icsgrc62081.2024.10691259.

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