Audio Management: Enhancing Wireless Sound Control through Hand Gestures

In this paper we have implemented a wireless vision based mobile robot control through hand gesture recognition based on perceptual color space such as HIS, HSV/HSB, HSL. Vision-based hand gesture recognition is an important problem in the field of human-computer interaction, since hand motions and gestures could potentially be used to interact with computers in more natural ways. The robot control was done purely based on the orientation histograms a simple and fast algorithm on the system which would recognize static hand gestures with HSV color spaces as major parameters. The wireless based mobile robot system using hand gestures is a new innovative user interface that resolves the complications of using numerous remote controls for various applications. Based on one unified set of hand gestures, this system interprets the user hand gestures into pre-defined commands to control the remote robot. The experimental results are very encouraging as the system produces real-time responses and highly accurate recognition towards various gestures under different lighting conditions.

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

With the escalating role of computers in educational system, human computer interaction, is becoming gradually more important part of it. The general believe is that with the progress in computing speed, communication technologies, and display techniques the existing HCI techniques may become a constraint in the effectual utilization of the existing information flow. The development of user interfaces influences the changes in the Human-Computer Interaction (HCI). Human hand gestures have been a mode of non verbal interaction widely used. The vocabulary of hand gesture communication has many variations. It ranges from simple action of using our finger to point at and using hands to move objects around for more complex expressions for the feelings and communicating with others. Also the hand gestures play a prominent role in teaching considering the explanations and exemplifications being highly dependent on hand gestures. Naturalistic and intuitiveness of the hand gesture has been an immense motivating aspect for the researchers in the field of Human Computer Interaction to put their efforts to research and develop the more promising means of interaction involving human and computers. The pursuance for the Human Computer Interaction research is moved by the central dogma of removing the complex and cumbersome interaction devices and replacing them with more obvious and expressive means of interaction which easily comes to the users with least cognitive burden like hand gestures. This paper designs a simple, natural system for gestural interaction between the user and computer for providing a dynamic user interface. The gesture recognition system uses image processing techniques for detection, segmentation, tracking and recognition of hand gestures for converting it to a meaningful command. This hand gesture recognition system has been proposed, designed and developed with the intensions to make it a substitute for mouse while making dynamic user interface between human and machine. Hence instead of making effort to develop a new vocabulary of hand gesture we have matched control instruction set of mouse to subset of most discriminating hand gestures, so that we get a robust interface. The interface being proposed here can be substantially applied towards different applications like image browser, games etc.

With new changes seen in computer technology day by day, it has become quite essential for us to find specific new ways of interaction with computer systems as its need is increasing in society every day. Today, every device is making the use of touch screen technology on its systems, which isn't affordable to be used in all applications. A specific interactive module like a virtual mouse that makes use of Hand Tracking and Gestures that will help us to interact can be an alternative way for the traditional touch screen and the physical mouse. The objective is to create a Hand Tracking application that interacts with the system. This system proposed is a Computer Vision-based mouse cursor control system, which uses hand gestures that are being captured from a webcam through a Hand detection technique. This system allows the user to navigate the system cursor using their hand gestures that the computer webcam tracks and perform mouse operations like left- click, right-click, and double click using different hand gestures. Python and OpenCV library are used for real- time computer vision to implement the system. The camera output is displayed on the monitor.

Hand gesture acquisition refers to the process whereby a computer is able to detect and record human hand movements. Vision-based hand gesture detection and a data glove-based approach can do this. In the realm of vision-based methods, we find both static hand and real-time hand gesture detection approaches. Five flex sensors are sewn onto the glove for the data glove method. Compared to the data glove-based method, the vision-based method is more consistent and robust. Designing a user-friendly vision-based interface, however, is a formidable challenge. The collected hand motions will need to be manually categorised by the user once again. So, it's no surprise that deciphering data is a laborious process that eats up plenty of time. This analysis should ideally be done mechanically. Relating the electrical nature of the human nervous system to computers presents a significant challenge in the field of Human Computer Interaction (HCI). Electromyographic (EMG) impulses, which change depending on the sort of hand motion utilised, are used to establish a brain link with computers. Because of this, an EMG-based system for recognising hand gestures can be created. The goal of this research is to create signal processing methods and Artificial Neural Networks (ANN) that can automatically recognise hand motions. The recorded Electromyographic (EMG) signals for a wide variety of hand motions are analysed using signal processing techniques, from which a number of features are extracted. Characteristics are extracted using Wavelet Transform (WT) based techniques including Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT). Better performance was achieved with the recommended methods, as shown by the simulation results.

For the Face database system by Web mining of science and technology experts, the Web mining data include the faces of science and technology experts, name, gender, title, schools and other properties. The Python programming language and the establishment of the Django framework are used in the proposed system. According to the Web extracting, the basic information of science and technology experts is extracted by the URL filtering. The face data of science and technology experts extracting is obtained by OpenCV libraries. As a result, it can detect face and cut them out. The extraction also adopts the semantic analysis method which uses predefined keywords segmented in the web page text to calculate the shortest distance. The face date of science and technical experts downloaded are deposited in local folder, and then the absolute path got by means of folder traversal is stored in the MySQL database system which can achieve the query and statistics of basic information of science and technical experts.

In this project we've discussed a system which uses some dynamic hand gesture recognition technique to manage the media players like VLC media player. This project consist of certain modules which segments the foreground a part of the frame using detection of skin colour and approximate median technique. the popularity of hand gesture is finished by creating a call Tree, that uses certain features extracted from the segmented part. This hand gesture recognition technique introduces a replacement, natural thanks to interact with computers and is beneficial to several folks in our day-to-day life. Hand gestures are associated with the human hands in hand gesture recognition. This project is employed for controlling certain operations (Pause, Play, Volume up, Volume Down, Mute) on video player by mere hand gestures without getting in the rigmarole of pressing buttons or tapping onto the screen. this may be directly associated with our day to life like in presentations. We have considered hand gestures and their directional motion defines a gesture for the applying. during this application image retrieving is finished employing a Webcam. Some functions in VLC media players are used most often and these functions uses some predefined gestures. Shows the defined gestures in keeping with the VLC player control operation. We created a VLC Media Player Controller using Hand Gesture Recognition System to form ‘HUMAN LIFE EASY AND BETTER’. This project is implemented using two steps: (1.) Creation of Hand Gesture Recognition System: this is often done using image processing using OpenCV library. (2.) Controlling VLC Media Player using hand gestures: during this step we controlled the player using shell commands which were recalled using python commands through OS library

Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without any movement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.

Recently much attention has been paid to the design of intelligent and natural user-computer interfaces. Hand Gesture Recognition systems has been developed continuously as its ability to interact with the machines. Now-a-days the news of metaverse ecosystem has increased the number of system in gesture recognition. Gestures are used to communicate with the PCs in a virtual environment. In this project Hand gestures are used to communicate information non-verbally which are free of expression to do a particular task. Here the hand gestures are recognized by using hand skeleton recognition using mediapipe library in python. In this project, the PC camera will record live video and recognizes the hand gestures based on which a particular functionality will take place. This project will present virtual keyboard, calculator and control system’s volume using hand gestures recognition technique by coding in Python using the OpenCV library.

In human-computer interaction or sign language interpretation, recognizing hand gestures and face detection become predominant in computer vision research. The primary goal of this proposed system is to create a system, which can identify hand gestures and facial detection to convey information for controlling media player. For those who are deaf and dumb sign language is a common, efficient and alternative way for talking, by using the hand and facial gestures we can easily understand them. Here hand and face are directly use as the input to the device for effective communication purpose of gesture identification there is no need of an intermediate medium.

This conference paper addresses gaps in scholarship and pedagogy surrounding the phenomenon of “dark patterns” in digital marketing and interface design by showcasing three curriculum-building projects. Dark patterns refer to a set of design strategies that co-opt the human-centred values advocated for in the fields of user experience (UX) design and human-computer interaction (HCI) to manipulate users into taking actions contrary to their personal interests. Recent dark patterns research has clustered within the fields of HCI, media studies, and game studies, with a focus on e-commerce and online gambling platforms. The presented projects put this established research into conversation with scholarship from business and marketing, science and technology studies, cognitive neuroscience, and disability studies to both create a more holistic definition of dark patterns and implement this expanded definition into university course curricula. These include $2 , focused on contextualizing dark patterns within historical market segmentation and merchandising strategies; $2 , on broadening the definition of dark patterns to include non-screen interfaces; and $2 , on analyzing how dark patterns have a disproportionate effect on individuals with certain cognitive disabilities. Collectively, these projects aimed to grant a greater historicity and social context to the phenomenon of dark patterns and introduce them as a utilizable pedagogical concept within the disciplines of communications, technology, and design. The findings of these projects are presented through the sharing of pedagogical materials, informal and formal feedback, and planned curriculum revisions.

Human-Computer Interaction (HCI) grew enormously over the years. Hand gesture recognition technology brought a brand-new era to the artificial intelligence branch of human-computer interaction. Hand Gesture-based communication is one of the most effortless and natural methods. Identifying and interpreting hand gestures from a continuous sequential stream of input data is called gesture recognition. Amateur or aged people find it hard to identify and press the exact alphabet that they need. Overcoming this difficulty stands as the ultimate goal for our proposed system. Typing has taken many forms, first using a keyboard, gradually changing to touch screens, and now too much easier finger motion tracking systems. The gesture is made by the user is detected by the machine via the image processing techniques and the operation unique to the machine is carried out, thereby minimizing the requirement of any hardware input device. This paper proposes a virtually controlled system that uses hand gestures and voice to perform operations. The system will allow the user to manage cursor, keyboard, and writing functions using hand gestures, and also provides the user with an additional feature to convert the user’s voice to text. The proposed system utilizes a webcam as an input device. This application will add a brand-new era of HCI implementation for educational purposes.

Computer usage is increasing rapidly day by day but the input devices are limited and to access them, we need to be near the screen. To overcome this problem and control the screen, we can use hand gestures. For every operation, we used different hand gestures. We proposed a python program to control the media player through hand gestures. In this method, we used libraries like OpenCV, Media Pipe, PyAuto GUI, and other libraries to capture the video, provide ready-to-use ML solutions and automate your GUI and programmatically control your keyboard and mouse. Hand gestures will be used as the input for providing natural interaction by reducing external hardware interaction. The whole process is divided into two steps. Firstly, gesture recognition through the camera is done by OpenCV and media Pipe helps to identify the gesture b its position, and the respective command is executed. Secondly, PyAuto GUI is used to automate the keyboard and controls the media player.

The research in the dynamic hand gestures recognition field has been promoted thanks to the recent success of the emerging Leap Motion controller (LMC) sensor, especially in the human-computer interaction field. During surgery, surgeons need to make more effective use of computers through a touchless human-computer interaction. In this paper, we present a set of gestures that are about commands to manipulate medical images. The public availability of hand gesture datasets for dynamic hand gestures, in particular with the LMC as an acquisition device, is currently quite poor. Therefore, we collect a more challenging dataset, called “LeapGestureDB”. The dataset is presented as text files, which contains 6,600 samples of 11 different dynamic hand gestures performed by over 120 participants. It is hosted online for the public interest, and especially to enhance research results. We suggest an approach of three-dimensional dynamic hand gesture recognition. We extract spatial, frequential and spatio-frequential features from the three-dimensional positions of the fingertips and the palm center delivered from the input sensor. These descriptors are fed into a support vector machine classifier to determine the performed gestures. We first evaluate our work on an existing dataset, namely “RIT dataset”, and then we use our collected dataset. We achieved a better accuracy rate of 93% comparing to results obtained from the “RIT dataset”.

Relevance. Artificial intelligence tools are playing an increasingly important role in food technology and biotechnology, significantly accelerating and improving various processes. Operational control of colorimetric parameters of cherry extracts using computer vision allows for a quick assessment of the content of bioactive substances without the cost of experimental studies. Methods. The objects of the study are samples of cherry extracts. Dilutions from 2 to 0.25% were prepared to obtain images of the test samples. The color characteristics of the extracts were determined using an NR60CP colorimeter. Quantitative determination of the total content of polyphenols in solutions of extracts of different concentrations was carried out using the Folin — Ciocalteu method. RGB images from a digital camera (50 MP) were obtained as input data for classifying objects and compiling a database. The Python programming language, OpenCV library and TensorFlow were used to develop image processing software. TensorFlow extracts features from photographs and adds them to the database. Results. The research results showed that with an increase in the content of bioactive substances — polyphenols — the color of the extracts changes and becomes darker, which is confirmed by the results of the evaluation of color characteristics with a colorimeter: with an increase in concentration, the redness increases and the lightness of the samples decreases. A database was created by extracting features using the TensorFlow library from ready-made sample images. A program for assessing the content of polyphenols in extracts was developed in the Python programming language. The described model of the system for monitoring the quality of extracts using computer vision can facilitate the determination of the content of bioactive substances on an industrial scale, which requires significant time and resources.