Gesture recognition is the process of understanding and
interpreting meaningful movements of the hands, arms, face,
or sometimes head. It is of great need in designing an
efficient human-computer interface. The technology has
been in study in recent years because of its potential for
application in user interfaces
This document discusses gesture technology and gesture recognition. It defines different types of gestures including iconic, deictic, metaphoric, and beat gestures. It explains that gesture recognition allows interface with computers using human body movements, especially hand gestures. Some applications of gesture recognition mentioned include control of smart TVs and laptops, sign language recognition, and assistance for physically challenged individuals through technologies like gesture-based wheelchairs. The document also outlines different approaches to gesture recognition technology including device sensors, vision-based techniques, and thermal cameras.
The Gesture Recognition Technology is rapidly growing technology and this PPT describes about the working of gesture recognition technology,the sub fields in it, its applications and the challenges it faces.
Natural Hand Gestures Recognition System for Intelligent HCI: A SurveyEditor IJCATR
Gesture recognition is to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head,
and/or body. Hand Gestures have greater importance in designing an intelligent and efficient human–computer interface. The applications
of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper a survey on
various recent gesture recognition approaches is provided with particular emphasis on hand gestures. A review of static hand posture
methods are explained with different tools and algorithms applied on gesture recognition system, including connectionist models, hidden
Markov model, and fuzzy clustering. Challenges and future research directions are also highlighted.
Day by day lots of efforts are been taken towards
developing an intelligent and natural interface between computer
system and users. And looking at the technologies now a day’s it
has become possible by means of variety of media information like
visualization, audio, paint etc. Gesture has become important part
of human communication to convey the information. Thus In this
paper we proposed a method for HAND GESTURE
RECOGNIZATION which includes Hand Segmentation, Hand
Tracking and Edge Traversal Algorithm. We have designed a
system which is limited to the hardware parts such as computer
and webcam. The system consists of four modules: Hand
Tracking and Segmentation, Feature Extraction, Neural
Training, and Testing. The objective of this system to explore the
utility of a neural network-based approach to the recognition of
the hand gestures that create a system that will easily identify the
gesture and use them for device control and convey information
instead of normal inputs devices such as mouse and keyboard.
Type of interfaces Touch and air based-gestureAminah Min
Touch interfaces allow users to interact with machines by touching the screen with their hands or a stylus. Users see graphical icons to touch for selections. Some touch interfaces include tactile or Braille input for visual impairments. Air-based gesture interfaces use cameras and algorithms to interpret human gestures originating from the face or hands as input, providing a way for computers to begin understanding body language.
This document discusses gesture recognition, including what gestures are, types of gesture recognition like facial, hand, and sign language recognition. It covers the basic working of gesture technology and types of gesture sensing technologies such as device, electrical field, and vision-based sensing. Some applications of gesture recognition discussed include controlling devices, sign language translation, and assisting with patient rehabilitation. Challenges to gesture recognition are also mentioned such as lack of standard gesture languages and issues with robustness due to lighting and noise factors.
Gesture recognition is the process of understanding and
interpreting meaningful movements of the hands, arms, face,
or sometimes head. It is of great need in designing an
efficient human-computer interface. The technology has
been in study in recent years because of its potential for
application in user interfaces
This document discusses gesture technology and gesture recognition. It defines different types of gestures including iconic, deictic, metaphoric, and beat gestures. It explains that gesture recognition allows interface with computers using human body movements, especially hand gestures. Some applications of gesture recognition mentioned include control of smart TVs and laptops, sign language recognition, and assistance for physically challenged individuals through technologies like gesture-based wheelchairs. The document also outlines different approaches to gesture recognition technology including device sensors, vision-based techniques, and thermal cameras.
The Gesture Recognition Technology is rapidly growing technology and this PPT describes about the working of gesture recognition technology,the sub fields in it, its applications and the challenges it faces.
Natural Hand Gestures Recognition System for Intelligent HCI: A SurveyEditor IJCATR
Gesture recognition is to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head,
and/or body. Hand Gestures have greater importance in designing an intelligent and efficient human–computer interface. The applications
of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper a survey on
various recent gesture recognition approaches is provided with particular emphasis on hand gestures. A review of static hand posture
methods are explained with different tools and algorithms applied on gesture recognition system, including connectionist models, hidden
Markov model, and fuzzy clustering. Challenges and future research directions are also highlighted.
Day by day lots of efforts are been taken towards
developing an intelligent and natural interface between computer
system and users. And looking at the technologies now a day’s it
has become possible by means of variety of media information like
visualization, audio, paint etc. Gesture has become important part
of human communication to convey the information. Thus In this
paper we proposed a method for HAND GESTURE
RECOGNIZATION which includes Hand Segmentation, Hand
Tracking and Edge Traversal Algorithm. We have designed a
system which is limited to the hardware parts such as computer
and webcam. The system consists of four modules: Hand
Tracking and Segmentation, Feature Extraction, Neural
Training, and Testing. The objective of this system to explore the
utility of a neural network-based approach to the recognition of
the hand gestures that create a system that will easily identify the
gesture and use them for device control and convey information
instead of normal inputs devices such as mouse and keyboard.
Type of interfaces Touch and air based-gestureAminah Min
Touch interfaces allow users to interact with machines by touching the screen with their hands or a stylus. Users see graphical icons to touch for selections. Some touch interfaces include tactile or Braille input for visual impairments. Air-based gesture interfaces use cameras and algorithms to interpret human gestures originating from the face or hands as input, providing a way for computers to begin understanding body language.
This document discusses gesture recognition, including what gestures are, types of gesture recognition like facial, hand, and sign language recognition. It covers the basic working of gesture technology and types of gesture sensing technologies such as device, electrical field, and vision-based sensing. Some applications of gesture recognition discussed include controlling devices, sign language translation, and assisting with patient rehabilitation. Challenges to gesture recognition are also mentioned such as lack of standard gesture languages and issues with robustness due to lighting and noise factors.
Hand Segmentation Techniques to Hand Gesture Recognition for Natural Human Co...Waqas Tariq
This work is the part of vision based hand gesture recognition system for Natural Human Computer Interface. Hand tracking and segmentation are the primary steps for any hand gesture recognition system. The aim of this paper is to develop robust and efficient hand segmentation algorithm where three algorithms for hand segmentation using different color spaces with required morphological processing have were utilized. Hand tracking and segmentation algorithm (HTS) is found to be most efficient to handle the challenges of vision based system such as skin color detection, complex background removal and variable lighting condition. Noise may contain, sometime, in the segmented image due to dynamic background. An edge traversal algorithm was developed and applied on the segmented hand contour for removal of unwanted background noise.
hand gesture based interactive photo sildersampada muley
This document summarizes a seminar presentation on hand gesture based interactive photo slider. It discusses what gestures are, types of gestures, and the objectives and introduction of the project. It describes the history of hand gesture recognition, preprocessing techniques, and the three stages of recognizing gestures. It provides block diagrams and discusses approaches, algorithms, advantages and disadvantages of the technology. The conclusion states that hand gestures allow natural interaction from a distance without other input devices and could be applied to control games.
Part 1 - Gesture Recognition TechnologyPatel Saunak
Gesture recognition technology allows humans to interface with computers using body movements detected by cameras. Cameras read gestures like hand movements and send that data to computers, which can then use the gestures for control or input. Current research focuses on emotion recognition from facial expressions and interpreting sign language through computer vision algorithms. Gesture recognition has applications in assistive robotics, gaming, alternative interfaces, and more.
Hand gesture recognition system for human computer interaction using contour ...eSAT Journals
This document describes a hand gesture recognition system that allows users to control computer operations using hand gestures captured by a webcam. The system involves four main phases: 1) image acquisition using a webcam, 2) image pre-processing to extract the hand and reduce noise, 3) feature extraction by detecting hand contours, and 4) gesture recognition by comparing contour features to stored templates and assigning computer commands. The system was able to recognize various gestures like opening programs or pressing keys with an average recognition rate of 95%. Future work could involve reducing constraints on the user environment and allowing both hands to perform more operations.
It is the best and attractive ppt of Gesture Recognition Technology...This is the TOUCHLESS technology...and will surely hit the market...in coming days.
Gesture based computing uses gestures as a form of human-computer interaction. It can be used to replace mice and keyboards by allowing users to navigate interfaces and interact with 3D environments through gestures detected by cameras. Common technologies for gesture recognition include depth cameras, controllers, and single visible light cameras. Gestures can be used for applications in entertainment, gaming, communications for disabled individuals, and as an alternative computer interface.
Gesture recognition is a topic in computer science and language technology which interpret human gestures via mathematical algorithms.
Gestures can originate from any bodily motion or state but commonly originate from the face or hand.
Gesture recognition enables humans to communicate with the machine (HMI) and interact naturally without any mechanical devices.
Gesture recognition technology uses cameras to read human body movements and gestures as a form of input to control devices and applications. A camera captures gestures like hand movements and facial expressions and sends that data to a computer for interpretation. Gesture recognition allows humans to interact with machines naturally without physical devices by using gestures to control cursors, activate menus, or control games and other applications. There are different methods for capturing and interpreting gestures including using wired gloves, depth cameras, stereo cameras, single cameras, or motion controllers.
This document discusses gesture recognition. It begins by introducing gesture recognition and its evolution from graphical user interfaces using mice and keyboards. It then defines different types of gestures including iconic, deictic, metaphoric, and beat gestures. The document outlines the basic working of a gesture recognition system and different types of gesture sensing technologies like hand gesture recognition, facial gesture recognition, sign language recognition, and vision-based techniques. It discusses input devices used for gesture tracking and various applications of gesture recognition like socially assistive robotics, sign language translation, virtual controllers, and remote control. Finally, it addresses challenges in gesture recognition like lack of a universal gesture language and issues with robustness.
Gesture recognition technology uses mathematical algorithms to interpret human gestures and enable interaction with machines without physical devices. It has various applications including sign language recognition, interpreting facial expressions, and electrical field sensing of body proximity. Vision-based and device-based techniques use cameras, gloves, or other sensors to detect gestures. Challenges include varying lighting and background items that can reduce accuracy. The future potential is vast across entertainment, home automation, education, medicine and security.
This document discusses gesture recognition. It defines a gesture as a form of non-verbal communication using bodily movements. The document then provides examples of gestures and discusses how gesture recognition works by using computer vision and image processing techniques. It outlines different types of gestures including hand gestures, sign language, and gestures detected using electrical fields. The document discusses advantages such as more natural human-computer interaction and disadvantages including issues with ambient light and object detection. It concludes by discussing future trends in gesture recognition technology.
This document provides an overview of gesture recognition technology, including what gestures are, the history and basic workings of gesture recognition, different types of gesture recognition and sensing technologies, algorithms used, applications, and challenges. It discusses hand, facial, and sign language recognition and technologies like wired gloves, cameras, and controllers. Benefits include interacting without mouse/keyboard and with 3D environments without physical contact. Applications include rehabilitation, sign language, gaming, and assisting those with disabilities.
The document discusses gesture recognition technology. It describes how cameras can read human body movements and communicate that data to computers to interpret gestures. Gestures can be used as inputs to control devices or applications. The document outlines different types of gestures, image processing techniques used, input devices like gloves and cameras, challenges, and potential uses like sign language recognition and immersive gaming.
Gesture recognition technology allows for control of devices through hand and body motions. It works by using cameras, sensors and algorithms to interpret gestures and movements. Key applications include controlling smart TVs with hand motions, sign language translation, and assisting disabled individuals. Challenges include variations between individuals, reading motions accurately due to lighting and noise, and lack of standardized gesture languages.
This document is a final report on gesture recognition submitted by three students. It contains an abstract, introduction, background information on gesture recognition including American Sign Language and object recognition techniques. It discusses digital image processing and neural networks. It outlines the approach, modules, flowcharts, results and conclusions of the project, which developed a method to recognize static hand gestures using a perceptron neural network trained on orientation histograms of the input images. Source code and applications are also discussed.
Gestures are an important form of non-verbal communication that involve visible bodily motions. The document discusses the history and development of gesture recognition technologies, describing early data gloves and videoplace systems as well as current technologies like Cepal and ADITI that help people with disabilities control devices with gestures. It also outlines the key components of a gesture recognition system including modeling, analysis, and recognition of gestures and discusses classification methods like HMMs and MLPs. Applications discussed include virtual keyboards, navigaze, and Sixth Sense technology.
This document discusses finger tracking techniques. It begins with an introduction to finger tracking and its uses in technology. It then discusses different types of finger tracking, including those that use interfaces like gloves and those that track fingers without interfaces. The document outlines an algorithm for finger tracking and describes test sequences used to evaluate the algorithm. It concludes by discussing applications of finger tracking and its future potential to replace devices like mice.
This presentation explains how to use hand gestures recognized by accelerometers or digital image processing to control devices in a simple way without physical contact. Applications include sending text messages, making phone calls, gaming, controlling computers, and virtually controlling robots. Future enhancements could allow gesture control on flights or for security systems.
Gestures are expressive, meaningful body motions, i.e., physical movements of the fingers, hands, arms, head, face, or body with the intent to convey information or interact with the environment.
This document presents a mobile device for electronic eye gesture recognition. It consists of an infrared sensor to detect eye movements and gestures. The device allows users to control applications and home appliances through eye gestures alone. It operates in two modes: application interface mode to control operations through recognized gestures, and standalone mode where gestures are mapped to pre-programmed commands. The embedded software includes layers for communication, middleware, and hardware abstraction. In conclusion, this low-cost eye gesture recognition device provides a useful human-machine interface for both able-bodied and disabled users.
The EyeRing is a finger-worn device that allows users to access digital information about objects using pointing gestures or touch. It consists of a camera, processor, Bluetooth module, battery, and button attached to a plastic ring. When the button is pressed, the camera takes a snapshot that is sent to a mobile phone via Bluetooth. Computer vision and speech processing on the phone then analyze the image and read out information to the user through a headset. The EyeRing was invented to help both visually impaired and sighted people access information about their environment through an intuitive pointing interaction.
Hand Segmentation Techniques to Hand Gesture Recognition for Natural Human Co...Waqas Tariq
This work is the part of vision based hand gesture recognition system for Natural Human Computer Interface. Hand tracking and segmentation are the primary steps for any hand gesture recognition system. The aim of this paper is to develop robust and efficient hand segmentation algorithm where three algorithms for hand segmentation using different color spaces with required morphological processing have were utilized. Hand tracking and segmentation algorithm (HTS) is found to be most efficient to handle the challenges of vision based system such as skin color detection, complex background removal and variable lighting condition. Noise may contain, sometime, in the segmented image due to dynamic background. An edge traversal algorithm was developed and applied on the segmented hand contour for removal of unwanted background noise.
hand gesture based interactive photo sildersampada muley
This document summarizes a seminar presentation on hand gesture based interactive photo slider. It discusses what gestures are, types of gestures, and the objectives and introduction of the project. It describes the history of hand gesture recognition, preprocessing techniques, and the three stages of recognizing gestures. It provides block diagrams and discusses approaches, algorithms, advantages and disadvantages of the technology. The conclusion states that hand gestures allow natural interaction from a distance without other input devices and could be applied to control games.
Part 1 - Gesture Recognition TechnologyPatel Saunak
Gesture recognition technology allows humans to interface with computers using body movements detected by cameras. Cameras read gestures like hand movements and send that data to computers, which can then use the gestures for control or input. Current research focuses on emotion recognition from facial expressions and interpreting sign language through computer vision algorithms. Gesture recognition has applications in assistive robotics, gaming, alternative interfaces, and more.
Hand gesture recognition system for human computer interaction using contour ...eSAT Journals
This document describes a hand gesture recognition system that allows users to control computer operations using hand gestures captured by a webcam. The system involves four main phases: 1) image acquisition using a webcam, 2) image pre-processing to extract the hand and reduce noise, 3) feature extraction by detecting hand contours, and 4) gesture recognition by comparing contour features to stored templates and assigning computer commands. The system was able to recognize various gestures like opening programs or pressing keys with an average recognition rate of 95%. Future work could involve reducing constraints on the user environment and allowing both hands to perform more operations.
It is the best and attractive ppt of Gesture Recognition Technology...This is the TOUCHLESS technology...and will surely hit the market...in coming days.
Gesture based computing uses gestures as a form of human-computer interaction. It can be used to replace mice and keyboards by allowing users to navigate interfaces and interact with 3D environments through gestures detected by cameras. Common technologies for gesture recognition include depth cameras, controllers, and single visible light cameras. Gestures can be used for applications in entertainment, gaming, communications for disabled individuals, and as an alternative computer interface.
Gesture recognition is a topic in computer science and language technology which interpret human gestures via mathematical algorithms.
Gestures can originate from any bodily motion or state but commonly originate from the face or hand.
Gesture recognition enables humans to communicate with the machine (HMI) and interact naturally without any mechanical devices.
Gesture recognition technology uses cameras to read human body movements and gestures as a form of input to control devices and applications. A camera captures gestures like hand movements and facial expressions and sends that data to a computer for interpretation. Gesture recognition allows humans to interact with machines naturally without physical devices by using gestures to control cursors, activate menus, or control games and other applications. There are different methods for capturing and interpreting gestures including using wired gloves, depth cameras, stereo cameras, single cameras, or motion controllers.
This document discusses gesture recognition. It begins by introducing gesture recognition and its evolution from graphical user interfaces using mice and keyboards. It then defines different types of gestures including iconic, deictic, metaphoric, and beat gestures. The document outlines the basic working of a gesture recognition system and different types of gesture sensing technologies like hand gesture recognition, facial gesture recognition, sign language recognition, and vision-based techniques. It discusses input devices used for gesture tracking and various applications of gesture recognition like socially assistive robotics, sign language translation, virtual controllers, and remote control. Finally, it addresses challenges in gesture recognition like lack of a universal gesture language and issues with robustness.
Gesture recognition technology uses mathematical algorithms to interpret human gestures and enable interaction with machines without physical devices. It has various applications including sign language recognition, interpreting facial expressions, and electrical field sensing of body proximity. Vision-based and device-based techniques use cameras, gloves, or other sensors to detect gestures. Challenges include varying lighting and background items that can reduce accuracy. The future potential is vast across entertainment, home automation, education, medicine and security.
This document discusses gesture recognition. It defines a gesture as a form of non-verbal communication using bodily movements. The document then provides examples of gestures and discusses how gesture recognition works by using computer vision and image processing techniques. It outlines different types of gestures including hand gestures, sign language, and gestures detected using electrical fields. The document discusses advantages such as more natural human-computer interaction and disadvantages including issues with ambient light and object detection. It concludes by discussing future trends in gesture recognition technology.
This document provides an overview of gesture recognition technology, including what gestures are, the history and basic workings of gesture recognition, different types of gesture recognition and sensing technologies, algorithms used, applications, and challenges. It discusses hand, facial, and sign language recognition and technologies like wired gloves, cameras, and controllers. Benefits include interacting without mouse/keyboard and with 3D environments without physical contact. Applications include rehabilitation, sign language, gaming, and assisting those with disabilities.
The document discusses gesture recognition technology. It describes how cameras can read human body movements and communicate that data to computers to interpret gestures. Gestures can be used as inputs to control devices or applications. The document outlines different types of gestures, image processing techniques used, input devices like gloves and cameras, challenges, and potential uses like sign language recognition and immersive gaming.
Gesture recognition technology allows for control of devices through hand and body motions. It works by using cameras, sensors and algorithms to interpret gestures and movements. Key applications include controlling smart TVs with hand motions, sign language translation, and assisting disabled individuals. Challenges include variations between individuals, reading motions accurately due to lighting and noise, and lack of standardized gesture languages.
This document is a final report on gesture recognition submitted by three students. It contains an abstract, introduction, background information on gesture recognition including American Sign Language and object recognition techniques. It discusses digital image processing and neural networks. It outlines the approach, modules, flowcharts, results and conclusions of the project, which developed a method to recognize static hand gestures using a perceptron neural network trained on orientation histograms of the input images. Source code and applications are also discussed.
Gestures are an important form of non-verbal communication that involve visible bodily motions. The document discusses the history and development of gesture recognition technologies, describing early data gloves and videoplace systems as well as current technologies like Cepal and ADITI that help people with disabilities control devices with gestures. It also outlines the key components of a gesture recognition system including modeling, analysis, and recognition of gestures and discusses classification methods like HMMs and MLPs. Applications discussed include virtual keyboards, navigaze, and Sixth Sense technology.
This document discusses finger tracking techniques. It begins with an introduction to finger tracking and its uses in technology. It then discusses different types of finger tracking, including those that use interfaces like gloves and those that track fingers without interfaces. The document outlines an algorithm for finger tracking and describes test sequences used to evaluate the algorithm. It concludes by discussing applications of finger tracking and its future potential to replace devices like mice.
This presentation explains how to use hand gestures recognized by accelerometers or digital image processing to control devices in a simple way without physical contact. Applications include sending text messages, making phone calls, gaming, controlling computers, and virtually controlling robots. Future enhancements could allow gesture control on flights or for security systems.
Gestures are expressive, meaningful body motions, i.e., physical movements of the fingers, hands, arms, head, face, or body with the intent to convey information or interact with the environment.
This document presents a mobile device for electronic eye gesture recognition. It consists of an infrared sensor to detect eye movements and gestures. The device allows users to control applications and home appliances through eye gestures alone. It operates in two modes: application interface mode to control operations through recognized gestures, and standalone mode where gestures are mapped to pre-programmed commands. The embedded software includes layers for communication, middleware, and hardware abstraction. In conclusion, this low-cost eye gesture recognition device provides a useful human-machine interface for both able-bodied and disabled users.
The EyeRing is a finger-worn device that allows users to access digital information about objects using pointing gestures or touch. It consists of a camera, processor, Bluetooth module, battery, and button attached to a plastic ring. When the button is pressed, the camera takes a snapshot that is sent to a mobile phone via Bluetooth. Computer vision and speech processing on the phone then analyze the image and read out information to the user through a headset. The EyeRing was invented to help both visually impaired and sighted people access information about their environment through an intuitive pointing interaction.
Project Report on Hand gesture controlled robot part 2Pragya
A gesture is a form of non-verbal communication in which visible bodily actions
communicate particular messages, either in place of speech or together and in parallel
with words. Gestures include movement of the hands, face, or other parts of the body.
Gestures differ from physical non-verbal communication that does not communicate
specific messages, such as purely expressive displays, proxemics, or displays of joint
attention. Gestures allow individuals to communicate a variety of feelings and
thoughts, from contempt and hostility to approval and affection, often together with
body language in addition towards when they speak.
Gesture Controlled Robot is a robot which can be controlled by simple gestures. The
user just needs to wear a gesture device which includes a sensor. The sensor will
record the movement of hand in a specific direction which will result in the
movement of the robot in the respective direction. The robot and the Gesture device
are connected wirelessly via radio waves. The wireless communication enables the
user to interact with the robot in a more friendly way.
For more assistance, mail me at pragyakulshresth@gmail.com
This document describes a hand gesture vocalizer system that aims to help deaf, blind, and speech impaired people communicate more easily. The system uses flex sensors on a glove to detect finger bending gestures and an accelerometer to detect hand tilting gestures. A microcontroller identifies the gestures and sends the output to an LCD display and via Bluetooth to an Android phone to vocalize the gesture as speech. The system was designed and developed by students to address communication barriers for people with disabilities by translating common sign language gestures into audio and text outputs. It achieved gesture detection and translation but had limitations in vocabulary size and accuracy. Future work could explore expanding its capabilities for more advanced communication.
This document summarizes a presentation on touchless touchscreen technology. It describes how touchless touchscreens use optical sensors to detect hand motions and gestures near the screen to allow control of devices without physical touch. Applications include touchless monitors, gesture-based interfaces, and touchless SDKs. Advantages are that it provides an easier interface and satisfies use cases where touching the screen is difficult. The technology is inspired by interfaces shown in the film Minority Report.
Blue Eyes is a technology that aims to give computers human-like perceptual and sensory abilities such as sight. It monitors and records physiological data from human operators such as eye movement. Wireless Bluetooth technology is used to acquire this data without restricting operator mobility. This allows real-time user-defined alarm triggering and recorded data playback to avoid human errors from factors like fatigue. Potential applications include industrial facilities, transportation centers, and assistance for disabled individuals.
IRJET- Robotic Vehicle Movement and Arm Control Through Hand Gestures using A...IRJET Journal
This document describes a system for controlling a robotic vehicle and arm through hand gestures using an Arduino microcontroller. An accelerometer and flex sensor attached to the user's hand capture gesture data, which is sent wirelessly via nRF modules to an Arduino on the receiving end. This Arduino controls a robotic arm and vehicle. The arm can pick and place objects using a soft gripper, and the vehicle can move in four directions. The goal is to help physically impaired users interact with and manipulate their environment through intuitive hand gestures.
Blue Eyes is a technology that aims to give computers human-like perceptual and sensory abilities. It uses sensors to monitor physiological data like eye movement of human operators to detect fatigue or other issues. A wireless Bluetooth system is used to acquire and transmit this physiological data in real-time without restricting operator mobility. The data is analyzed and can trigger alarms. Potential applications include monitoring operators in control rooms for power plants or aircraft to help prevent human errors from leading to accidents.
Gesture recognition using artificial neural network,a technology for identify...NidhinRaj Saikripa
The presentation contains a technology for identifying any type of body motions commonly originating from hand and face using artificial neural network.This include identifying sign language also.This technology is for speech impaired individuals.
// I have shared an IJCSE standard paper in this topic
This document discusses gesture recognition technology. It begins with an introduction that defines gestures as forms of non-verbal communication involving body movement and defines gesture recognition as interpreting human gestures through mathematical algorithms. It then discusses the motivation for gesture recognition technology, including its naturalness and applications in overcoming interaction problems with mice or for people with limited mobility. The document outlines different gesture types, input devices like gloves and cameras, challenges like developing standard gesture languages and ensuring robustness, and applications such as sign language recognition, virtual controllers, remote control, and assistance for the physically challenged.
Gesture Recognition Technology-Seminar PPTSuraj Rai
This document provides an overview of gesture recognition technology. It begins with introducing gestures as a form of non-verbal communication and defines gesture recognition as interpreting human gestures through mathematical algorithms. It then discusses the motivation for gesture recognition, including its naturalness and applications in overcoming interaction problems with traditional input devices. The document outlines different types of gestures, input devices like gloves and cameras, challenges like developing standardized gesture languages, and uses like sign language recognition, virtual controllers, and assisting disabled individuals. It concludes with references for further reading.
This document summarizes a survey on detecting hand gestures to be used as input for computer interactions. The introduction discusses how graphical user interfaces are being upgraded to provide more efficient visual interfaces using touchscreen technologies. However, these technologies are still too expensive for laptops and desktops. The paper then proposes developing a virtual mouse system using a webcam to capture hand movements and perform mouse functions like left and right clicks. The methodology section outlines the key steps of the proposed system which includes skin detection, contour extraction from images, and mapping detected hand gestures to cursor movements and controls. Finally, the conclusion discusses the goal of making this technology cheaper and more accessible to use as a standard input device without additional hardware requirements.
IRJET- Robotic Hand Controlling using Flex Sensors and Arduino UNOIRJET Journal
This document describes a robotic hand that is controlled using flex sensors and an Arduino Uno microcontroller. Flex sensors are placed on each finger of a glove to sense finger movement. The flex sensor data is sent to the Arduino Uno which processes the data and sends signals to servo motors controlling each finger of the robotic hand. The robotic hand is able to replicate movements of the human hand wearing the flex sensor glove up to 50 meters away using a wireless module. The design provides a low-cost way to control a robotic hand using flex sensors and microcontroller processing to map human finger motions.
IRJET- Hand Movement Recognition for a Speech Impaired PersonIRJET Journal
This document describes a system to recognize hand gestures from a speech-impaired person and convert them to speech using a flex sensor glove and microcontroller. The system uses flex sensors attached to a glove to detect hand movements and gestures. The microcontroller matches the gestures to a database of templates and outputs the corresponding speech signal through a speaker. This allows speech-impaired individuals to communicate through natural hand gestures that are translated to audio speech in real-time. The system aims to help overcome communication barriers for those unable to speak.
Blue Eyes is a technology that aims to give computers human-like perceptual and sensory abilities such as vision. It uses sensors to monitor physiological signals like eye movement of human operators. Originally, wiring was used to connect the sensors to the system but wireless Bluetooth technology now allows for mobility. Blue Eyes can gather information about users through techniques like facial recognition and speech recognition. It is used to help detect human errors and aid disabled individuals in various applications like vehicle and machinery operation.
The document describes the process of developing an application on Raspberry Pi that uses computer vision and Java APIs to detect hand gestures and control home appliances via IR signals. The application continuously captures camera input, detects hand gestures using OpenCV, and passes appropriate signals to an IR device to control devices like a TV. Key steps include connecting the IR device to the application via SmartConfig, teaching gestures during a learning mode, and executing gestures during operation to control devices in real-time. The goal is to implement intelligent home automation through contactless hand gesture recognition.
This document describes a system for controlling a computer cursor using eye movement. The system uses a Raspberry Pi connected to a camera and PIR sensor. It detects the pupil center in images to determine eye position. OpenCV is used for pupil detection and a support vector machine classifies eye movements. Eye movements control cursor direction while blinks emulate mouse clicks. This allows hands-free computer use for people with disabilities. The system was found to accurately track eye position and enable cursor movement with high efficiency levels. Overall, the document presents a method for an eye-controlled computer cursor using low-cost hardware and software.
This document discusses touchless touchscreen technology. It describes how touchless touchscreens use optical pattern recognition and a solid state optical matrix sensor connected to a digital image processor to interpret hand motions and gestures in 3D space without physical touch. Examples are given of companies developing this technology and potential applications like touchless monitors, gesture-based user interfaces, and 3D navigation. Advantages include an easier and more satisfactory user experience without drivers compared to traditional touchscreens.
This document discusses touchless touchscreen technology. It describes how touchless touchscreens use optical pattern recognition and a solid state optical matrix sensor connected to a digital image processor to interpret hand motions and gestures in 3D space without physical touch. Examples are given of companies developing this technology and how it could be used for touchless monitors, graphical user interfaces controlled by gestures, and 3D navigation on screens through hand movements alone. Potential applications are in medical settings where gloves are worn or for more intuitive user experiences.
IRJET- Survey on Sign Language and Gesture Recognition SystemIRJET Journal
This document summarizes several research papers on sign language and gesture recognition systems. It discusses various techniques that have been used to convert sign language and gestures into understandable formats for hearing people. Vision-based and sensor-based approaches are described. Specific papers summarized include those using 7Hu moments and KNN classification achieving 82% accuracy, a system using gloves with flex and inertial sensors recognizing Taiwanese sign language with 94% accuracy, and a vision-based system using convex hull and defects to control computer functions. The document concludes by describing a system using a sensor glove to detect gestures from British and Indian sign languages and output text and audio.
Similar to Mobile Device For Electronic Eye Gesture Recognition (20)
Building a Raspberry Pi Robot with Dot NET 8, Blazor and SignalRPeter Gallagher
In this session delivered at NDC Oslo 2024, I talk about how you can control a 3D printed Robot Arm with a Raspberry Pi, .NET 8, Blazor and SignalR.
I also show how you can use a Unity app on an Meta Quest 3 to control the arm VR too.
You can find the GitHub repo and workshop instructions here;
https://bit.ly/dotnetrobotgithub
Mobile Device For Electronic Eye Gesture Recognition
1. Mobile Device for Electronic Eye
Gesture Recognition
Presented By:
J Srikanth Vishwakarma,
12885A0430
Guide:
Ms. U.Shravani,
Assistant Professor.
1
Technical Seminar
2. Contents
• Introduction
• What is Gesture & Gesture Recognition
• Types of Gesture Recognition
• Basic working of Gesture technology
• What is Eye Gesture Recognition
• Block Diagram
• Operation
• Embedded Software
• Conclusion
• References
2
Mobile Device For Electronic Eye Gesture Recognition
3. Introduction
• This is a low-cost mobile device for electronic
gesture recognition as a human-machine
interface.
• This enables the control of different applications
by user’s eye gesture recognition.
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5. What is Gesture & Gesture Recognition
• A gesture is a form of non-verbal
communication. Gestures include movement
of the hands, face, or other parts of the body.
• Gestures are an important aspect of human
interaction, both interpersonally and in the
context of man-machine interfaces.
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Mobile Device For Electronic Eye Gesture Recognition
6. What is Gesture & Gesture Recognition
• Military air marshals use hand and body
gestures to direct flight operations aboard
aircraft carriers.
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Mobile Device For Electronic Eye Gesture Recognition
7. What is Gesture & Gesture Recognition
• Interface with computers using gestures of the
human body.
• Gesture recognition is an important skill for
robots that work closely with humans.
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Mobile Device For Electronic Eye Gesture Recognition
8. Types of Gesture Recognition
• Hand Gesture Recognition
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Mobile Device For Electronic Eye Gesture Recognition
9. Types of Gesture Recognition
• Facial Gesture Recognition
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Mobile Device For Electronic Eye Gesture Recognition
10. Types of Gesture Recognition
• Eye Gesture Recognition
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Mobile Device For Electronic Eye Gesture Recognition
11. Basic working of Gesture technology
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Mobile Device For Electronic Eye Gesture Recognition
12. What is Eye Gesture Recognition
• The eye gesture recognition refers to
controlling of computers by recognizing the
eye gestures.
• The mobile device for electronic eye gesture
recognition allows the control of different
applications and home appliances by user’s eye
gestures by the IR and Bluetooth wireless
technology
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Mobile Device For Electronic Eye Gesture Recognition
15. Operation
• The present device can be operated in two
primary modes:
▫ 1.Application interface mode
▫ 2.Stand alone mode
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Mobile Device For Electronic Eye Gesture Recognition
16. Operation
• Application Interface Mode:
In this mode the application interface is
exposed at device side that enables the control of
operation and data transmitting.
There are two sub-modes in this mode.
1. Gesture recognition sub-mode
2. Raw sub-mode
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Mobile Device For Electronic Eye Gesture Recognition
18. Operation
• Stand alone mode:
The successfully recognized eye gestures are
mapped into a previously chosen set of pre-
learned commands and transmitted over the one-
way IR communication unit in order to trigger
pre-programmed actions on the external device.
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Mobile Device For Electronic Eye Gesture Recognition
19. Embedded Software
• The embedded software can be divided into
three different layers
▫ 1.Communication layer
▫ 2.Middleware layer
▫ 3.Hardware abstraction layer
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Mobile Device For Electronic Eye Gesture Recognition
20. Conclusion
• Finally we can say that this mobile device for
electronic eye gesture recognition is not only
used for infirm persons but also useful for
interacting a computer with eye gestures.
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Mobile Device For Electronic Eye Gesture Recognition