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Relevant bibliographies by topics / Mage Processing; Hand Gesture; Human-Machine Interface / Journal articles

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Journal articles on the topic 'Mage Processing; Hand Gesture; Human-Machine Interface'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Priyamvadaa.R, Supriya.G. K. Sai, and Sangappa Mulimani Savita. "WIRELESS CONTROL OF AN AUTOMOBILE USING AIR GESTURES." INTERNATIONAL JOURNAL OF RESEARCH- GRANTHAALAYAH 5, no. 4 RACEEE (2017): 79–84. https://doi.org/10.5281/zenodo.580628.

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In the present world people with disabilities wish to be independent and thus the dependence on automatic machinery has increased drastically. People with physical disabilities and partial paralysis find it difficult to navigate without the assistance of someone. The system proposed will be a boost to the physically challenged people as it will help them to be self-reliable. Air gesture model is being as a key component to derive the benefit and gesture recognition is one obvious way to create a useful, highly adaptive interface between machines and their users. The gestures of the hand are re

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Harale, A. D., K. J. Karande, Sagar S. Bhumkar, Sanjay T. Gaikwad, and Snehal S. Kumbhar. "Wireless Hand Geture Control Robot with Object Detection." June-July 2023, no. 34 (July 13, 2023): 1–10. http://dx.doi.org/10.55529/jipirs.34.1.10.

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This study uses image processing and Internet of Things (IoT) technology to demonstrate a wireless hand gesture control robot with object identification. Users can remotely operate a robot using hand gestures that are photographed by a camera and analyzed using machine learning algorithms and image processing methods for gesture identification. The system has object detection capabilities as well, allowing the robot to find and recognize items in its environment. In order to allow users to construct their own gestures and instructions, the suggested system is made to be adaptive and versatile.

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Kalipu, Ravi Kumar, Harish Kurmana, Divakar Allaboina, Sanjay Kumar Chilla, Bhavish Lakkavarapu, and Ravi Kumar Nubothu. "Virtual Mouse Using Hand Gestures." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43518.

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The Virtual Mouse using Hand Gesture Recognition is an innovative system that allows users to control a computer cursor through hand gestures instead of a traditional mouse. This project utilizes Python, machine learning, and OpenCV to detect and interpret hand movements in real time. A camera captures gestures, which are processed using computer vision techniques to perform mouse actions such as clicking, scrolling, and cursor movement. Additionally, a zoom-in and zoom-out feature enhances user interaction through specific gestures. This touch-free interface provides an intuitive and hygienic

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Alange, Rutwik. "Hand Gesture Controller." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2540–44. http://dx.doi.org/10.22214/ijraset.2024.59395.

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Abstract: The rapid evolution of human-computer interaction has spurred significant progress in gesture recognition technologies, placing a specific emphasis on diverse applications. This paper highlights key advancements in machine learning algorithms tailored for gesture recognition, including deep learning approaches that have notably improved the accuracy and robustness of hand tracking systems. Furthermore, the integration of hand gesture control into wearable devices and its implications for everyday technology usage are thoroughly examined. This paper relies on the formidable capabiliti

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Deo, Aditi, Aishwarya Wankhede, Rutuja Asawa, and Supriya Lohar. "MEMS Accelerometer Based Hand Gesture-Controlled Robot." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 265–67. http://dx.doi.org/10.22214/ijraset.2022.46158.

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Abstract: Gesture detection has gotten a lot of attention from a lot of different research communities, including humancomputer interaction and image processing. User interface technology has become increasingly significant as the number of human-machine interactions in our daily lives has increased. Gestures as intuitive expressions will substantially simplify the interaction process and allow humans to command computers and machines more intuitively. Robots can now be controlled by a remote control, a mobile phone, or a direct wired connection. When considering cost and required hardware, al

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RAUTARAY, SIDDHARTH S., and ANUPAM AGRAWAL. "VISION-BASED APPLICATION-ADAPTIVE HAND GESTURE RECOGNITION SYSTEM." International Journal of Information Acquisition 09, no. 01 (2013): 1350007. http://dx.doi.org/10.1142/s0219878913500071.

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With the increasing role of computing devices, facilitating natural human computer interaction (HCI) will have a positive impact on their usage and acceptance as a whole. For long time, research on HCI has been restricted to techniques based on the use of keyboard, mouse, etc. Recently, this paradigm has changed. Techniques such as vision, sound, speech recognition allow for much richer form of interaction between the user and machine. The emphasis is to provide a natural form of interface for interaction. Gestures are one of the natural forms of interaction between humans. As gesture commands

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Carvalho, Vítor Hugo, and José Eusébio. "Virtual Interface With Kinect 3D Sensor for Interaction With Bedridden People." International Journal of Healthcare Information Systems and Informatics 16, no. 4 (2021): 1–16. http://dx.doi.org/10.4018/ijhisi.294114.

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The human-machine interaction has evolved significantly in the last years, allowing a new range of opportunities for developing solutions for people with physical limitations. Natural user interfaces (NUI) allow bedridden and/or physically disabled people to perform a set of actions trough gestures thus increasing their quality of life and autonomy. This paper presents a solution based on image processing and computer vision using the Kinect 3D sensor for development of applications that recognize gestures made by the human hand. The gestures are then identified by a software application that

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Savić, Srđan, Dunja Pavlović, and Andrej Čilag. "Surface electromyography-based gesture recognition for robot hand control." Journal of Computer and Forensic Sciences 3, no. 2 (2024): 3–27. https://doi.org/10.5937/jcfs3-54725.

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This paper presents a human-machine interaction interface for the teleoperated control of a three-finger robot hand based on surface electromyography. Based on the recorded sEMG signals, motion intention of a human operator is recognized and mapped onto corresponding predefined robot hand grasps. First, the experimental setup and underlying methodology of dataset generation are presented. Namely, 4-channel EMG data were collected from the forearm muscles of 4 healthy subjects as they performed a sequence of predefined grasps. Data processing included data filtering and segmentation, feature ex

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Kalpesh Pimple, Sania. "S.I.G.N. - Sign Interpretation and Gesture Navigation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44476.

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The S.I.G.N (Sign Interpretation using Gesture Navigation) project addresses the communication gap between sign language users and non-users, especially in sectors like education, healthcare, and public services.The project places a strong emphasis on Indian Sign Language (ISL) to cater to the communication needs of the hearing-impaired community in India. By tailoring recognition models and sign databases to ISL, the system ensures higher relevance, usability, and cultural alignment for Indian users. It utilizes machine learning, computer vision, and natural language processing (NLP) to trans

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Ardianti, Cici, Azhar Al Havis, and Munawir Munawir. "Finger Tracker Untuk Penerjemahan Bahasa Isyarat Angka 1-10 Untuk Tunawicara Menggunakan Webcam Dengan Metode Centroid." Jurnal SIFO Mikroskil 19, no. 2 (2018): 49–56. http://dx.doi.org/10.55601/jsm.v19i2.595.

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Visual sensing or machine vision is a process of image data manipulation. The data can be used to interpret many things, one of which is the introduction of gesture. Gesture recognition is an interface that can recognize the gestures of a human being and translate these movements as instructions that can be understood by a computer. Gesture recognition can be used to translate sign language into speech people. This is because there are many people who do not understand the language of the hands of the speechless. So, people with disabilities have difficulty interacting in society. In this fina

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11

Kotari,, Gopinadh. "Augmented Virtual Mouse System with Enhanced Gesture Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31709.

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This paper presents the development of a contactless system designed to serve as an alternative to the traditional physical input device, such as a computer mouse or touchpad, utilized in human-computer interaction. The proposed system aims to offer a more convenient and hygienic way of interacting with computer devices by using hand gestures instead of physical contact with the mouse system or machine interface. The system has potential applications in various fields including healthcare, public interfaces, and gaming. Existing gesture-controlled input systems lack a comprehensive touchless a

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Thorat, Sakshi. "HCI Based Virtual Controlling System." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 630–35. http://dx.doi.org/10.22214/ijraset.2022.43645.

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Abstract: Researchers around the globe are working on making our devices more interactive and making them function with minimal physical contact in this research project. The proposed system is an interactive computer system that can operate without a physical keyboard or mouse. This system will benefit everyone, particularly immobilized people with special needs operating a physical keyboard and mouse. So in the system, they have developed an interface that uses visual hand-gesture analysis. These gestures are used to assist those who are having trouble controlling or operating computers or g

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Varma, Anshal, Sanyukta Pawaskar, Sumedh More, and Ashwini Raorane. "Computer Control Using Vision-Based Hand Motion Recognition System." ITM Web of Conferences 44 (2022): 03069. http://dx.doi.org/10.1051/itmconf/20224403069.

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In our day-to-day communication and expression, gestures play a crucial role. As a result, using them to interact with technical equipment requires small cognitive data processing on our part. Because it creates a large barrier between the user and the machine, using a physical device for human-computer interaction, such as a mouse or keyboard, obstructs the natural interface. In this study, we created a sophisticated marker-free hand gesture detection structure that can monitor both dynamic and static hand gestures. Our system turns motion detection into actions such as opening web pages and

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Kurz, Marc, Robert Gstoettner, and Erik Sonnleitner. "Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition." Applied Sciences 11, no. 5 (2021): 2015. http://dx.doi.org/10.3390/app11052015.

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Since electronic components are constantly getting smaller and smaller, sensors and logic boards can be fitted into smaller enclosures. This miniaturization lead to the development of smart rings containing motion sensors. These sensors of smart rings can be used to recognize hand/finger gestures enabling natural interaction. Unlike vision-based systems, wearable systems do not require a special infrastructure to operate in. Smart rings are highly mobile and are able to communicate wirelessly with various devices. They could potentially be used as a touchless user interface for countless appli

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15

Vijay, More, Sangamnerkar Sanket, Thakare Vaibhav, Mane Dnyaneshwari, and Dolas Rahul. "SIGN LANGUAGE RECOGNITION USING IMAGE PROCESSING." JournalNX - A Multidisciplinary Peer Reviewed Journal QIPCEI2K18 (April 29, 2018): 85–87. https://doi.org/10.5281/zenodo.1411766.

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 we have proposed a method for real time Hand Gesture Recognition and feature extraction using a web camera. In this approach, the image is captured through webcam attached to the system. First the input image is preprocessed and threshold is used to remove noise from image and smoothen the image. After this apply region filling to fill holes in the gesture or the object of interest. This helps in improving the classification and recognition step. https://journalnx.com/journal-article/20150575

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16

Nalavade, Dr Kamini, Dr Pallavi Baviskar, Mayank Katiyara, Hitesh Paighan, Devendra Chaudhari, and Sanketgir Gosavi. "Multi Sign Language Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40539.

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The deaf and hard-of-hearing community often experiences communication barriers due to people who are not well-versed in the use of sign language.[1] This project would address this problem by developing an all-embracing machine learning (ML) model which would be able to interpret and translate the hand movements of American Sign Language (ASL) and Indian Sign Language (ISL) into corresponding spoken or written language in real time.[2] In using data from cameras, the system is designed to precisely predict and translate gestures both ASL and ISL. The solution integrates Natural Language Proce

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17

Vasiliev, A. V., A. O. Melnikov, and S. A. Lesko. "Robust neural network filtering in the tasks of building intelligent interfaces." Russian Technological Journal 11, no. 2 (2023): 7–19. http://dx.doi.org/10.32362/2500-316x-2023-11-2-7-19.

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Objectives. In recent years, there has been growing scientific interest in the creation of intelligent interfaces for computer control based on biometric data, such as electromyography signals (EMGs), which can be used to classify human hand gestures to form the basis for organizing an intuitive human-computer interface. However, problems arising when using EMG signals for this purpose include the presence of nonlinear noise in the signal and the significant influence of individual human characteristics. The aim of the present study is to investigate the possibility of using neural networks to

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18

Zhou, Zhongliang, Xihu Wu, Teck Lip Dexter Tam, et al. "Highly Stable Ladder‐Type Conjugated Polymer Based Organic Electrochemical Transistors for Low Power and Signal Processing‐Free Surface Electromyogram Triggered Robotic Hand Control." Advanced Functional Materials, September 17, 2023. http://dx.doi.org/10.1002/adfm.202305780.

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AbstractOrganic electrochemical transistors (OECTs) based complementary inverters have been considered as promising candidates in electrophysiological amplification, owing to their low power consumption, and high gain. To create complementary inverters, it is important to use highly stable p‐type and n‐type polymers with well‐balanced current. In this study, the electrochemical stability of p‐type ladder‐conjugated polymer‐based OECT is improved through an annealing process that maintains its doped‐state drain current from 76% to 105% after 4,500 cycles in ambient environment. Next an OECT‐bas

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19

Kangas, Sonja. "From Haptic Interfaces to Man-Machine Symbiosis." M/C Journal 2, no. 6 (1999). http://dx.doi.org/10.5204/mcj.1787.

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Until the 1980s research into computer technology was developing outside of a context of media culture. Until the 1970s the computer was seen as a highly effective calculator and a tool for the use in government, military and economic life. Its popular image from the 1940s to 1950s was that of a calculator. At that time the computer was a large machine which only white lab-coated engineers could understand. The computer was studied as a technical instrument, not from the viewpoint of the user. The peculiar communication between the user -- engineers at this point -- and the machine was describ

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20

Dr., Mahesh Kumar Porwal, and Nishant Porwal Mr. "Hand Gesture Sign Language Recognition through Machine Learning." June 7, 2024. https://doi.org/10.5281/zenodo.14633777.

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The research objective is to explore various ways in which machine learning models can be leveraged to detect and interpret sign language, ultimately contributing to real-time communication enhancements for the deaf community. We are excited to present the development and deployment of recognition model for sign language based on range of different models. This innovative approach not only benefits sign language learners by providing a tool for practicing their signing skills, but it also holds immense potential in bridging communication gaps. Throu

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21

Tchantchane, Rayane, Hao Zhou, Shen Zhang, Alexander Dunn, Emre Sariyildiz, and Gursel Alici. "Advancing Human–Machine Interface (HMI) through Development of a Conductive‐Textile Based Capacitive Sensor." Advanced Materials Technologies, November 19, 2024. http://dx.doi.org/10.1002/admt.202401458.

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AbstractSmart wearable sensors in human–machine interfaces (HMI) facilitate communication and interface between humans and robots, as well as among humans. Conventional wearables face significant limitations, including performance degradation under various deformations (e.g., strain and pressure), limited stretchability and flexibility, poor comfort, and breathability, complicating their integration into HMI applications. In response to these limitations, a smart, sewable, high‐precision HMI device based on a soft, textile‐based sensor with machine learning (ML)‐assisted data processing is pro

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Hands, Joss. "Device Consciousness and Collective Volition." M/C Journal 16, no. 6 (2013). http://dx.doi.org/10.5204/mcj.724.

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The article will explore the augmentation of cognition with the affordances of mobile micro-blogging apps, specifically the most developed of these: Twitter. It will ask whether this is enabling new kinds of on-the-fly collective cognition, and in particular what will be referred to as ‘collective volition.’ It will approach this with an address to Bernard Stiegler’s concept of grammatisation, which he defines as as, “the history of the exteriorization of memory in all its forms: nervous and cerebral memory, corporeal and muscular memory, biogenetic memory” (New Critique 33). This will be expl

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