Skinput is a technology developed by Microsoft Research that uses bio-acoustic sensing to detect finger taps on the skin and use the human body as an input surface. It involves wearing a sensor armband that can detect vibrations caused by taps and determine their location. This allows for an "always available" input method without needing to carry a separate device. The document provides background on Skinput and discusses its advantages over other mobile input methods in providing a large, portable input area using the human body and proprioception.
Skinput is an input technology that uses bio-acoustic sensing to localize finger taps on the skin. An armband equipped with acoustic detectors and a pico-projector can project a graphical interface onto the skin and detect taps to provide touch input without direct instrumentation of the skin. Potential applications include controlling mobile devices, gaming, education and accessibility for disabled users. While promising direct manipulation, challenges include cost, health effects, and size of current armband prototypes. Future research aims to improve accuracy, expand capabilities and miniaturize components.
The document summarizes a research project that developed Skinput, a technique for using the human body as an input surface. A wearable armband containing piezoelectric sensors is used to capture vibrations from finger taps on the arm. The sensors are tuned to specific resonant frequencies to detect relevant low-frequency signals transmitted through soft tissues and bones. Machine learning classifiers analyze the acoustic features from the sensors to determine the location and timestamps of finger taps, allowing the arm to serve as a touch input surface. Initial proof-of-concept applications are demonstrated.
Skinput is a technology that uses the human body as an input surface by sensing vibrations through the skin caused by finger taps. An armband with sensors collects these signals to determine the location of taps on the arm and hand, providing a natural and always-available finger input system. A user study assessed the capabilities, accuracy and limitations of using skin as a touch surface.
Hand gesture recognition system(FYP REPORT)Afnan Rehman
This document is a final year project report submitted by three students - Afnan Ur Rehman, Haseeb Anser Iqbal, and Anwaar Ul Haq - for their bachelor's degree in computer science. The report describes the development of a hand gesture recognition system using computer vision and machine learning techniques. Key aspects of the project include image acquisition using a webcam, preprocessing the images using techniques like filtering and noise removal, detecting and cropping the hand region, extracting HU moments features, training a classifier on sample gesture images, and classifying new images using KNN. The system is also able to translate recognized gestures to speech using text-to-speech.
Skinput technology turns the human body into a touchscreen input interface by using sensors to detect vibrations on the skin caused by taps and turns. It consists of an armband with sensors, a Bluetooth connection, and a small projector. When the user taps their skin, sensors detect the acoustic waves and can identify different locations tapped. The projector then displays a virtual keyboard or buttons onto the arm. The system works well but accuracy decreases for obese users or many input locations. Future applications could include texting by tapping on projected keyboards or controlling devices while walking.
The document presents an embedded real-time finger-vein recognition system for mobile devices. The system uses finger vein patterns as a biometric for authentication through image acquisition of the finger veins, processing the images through segmentation, enhancement and feature extraction, and human-machine communication. It was found to have high security, low power consumption, small size, quick response time of 0.8 seconds, and high accuracy with a low equal error rate of 0.07%.
The Blue Eyes technology aims to create machines that have human-like sensory abilities. It uses eye tracking and movement data collected by a data acquisition unit and processed by a central system unit. The technology employs sensors and methods like the Emotion Mouse, MAGIC, speech recognition, and SUITOR to interpret inputs. In the future, devices may be operated through gaze and voice commands enabled by advances in Blue Eyes technology.
This document describes the development of an automatic language translation software to aid communication between Indian Sign Language and spoken English using LabVIEW. The software aims to translate one-handed finger spelling input in Indian Sign Language alphabets A-Z and numbers 1-9 into spoken English audio output, and 165 spoken English words input into Indian Sign Language picture display output. It utilizes the camera and microphone of the device for image and speech acquisition, and performs vision and speech analysis for translation. The software is intended to help communication between deaf or speech-impaired individuals and those who do not understand sign language.
Skinput is an input technology that uses bio-acoustic sensing to localize finger taps on the skin. An armband equipped with acoustic detectors and a pico-projector can project a graphical interface onto the skin and detect taps to provide touch input without direct instrumentation of the skin. Potential applications include controlling mobile devices, gaming, education and accessibility for disabled users. While promising direct manipulation, challenges include cost, health effects, and size of current armband prototypes. Future research aims to improve accuracy, expand capabilities and miniaturize components.
The document summarizes a research project that developed Skinput, a technique for using the human body as an input surface. A wearable armband containing piezoelectric sensors is used to capture vibrations from finger taps on the arm. The sensors are tuned to specific resonant frequencies to detect relevant low-frequency signals transmitted through soft tissues and bones. Machine learning classifiers analyze the acoustic features from the sensors to determine the location and timestamps of finger taps, allowing the arm to serve as a touch input surface. Initial proof-of-concept applications are demonstrated.
Skinput is a technology that uses the human body as an input surface by sensing vibrations through the skin caused by finger taps. An armband with sensors collects these signals to determine the location of taps on the arm and hand, providing a natural and always-available finger input system. A user study assessed the capabilities, accuracy and limitations of using skin as a touch surface.
Hand gesture recognition system(FYP REPORT)Afnan Rehman
This document is a final year project report submitted by three students - Afnan Ur Rehman, Haseeb Anser Iqbal, and Anwaar Ul Haq - for their bachelor's degree in computer science. The report describes the development of a hand gesture recognition system using computer vision and machine learning techniques. Key aspects of the project include image acquisition using a webcam, preprocessing the images using techniques like filtering and noise removal, detecting and cropping the hand region, extracting HU moments features, training a classifier on sample gesture images, and classifying new images using KNN. The system is also able to translate recognized gestures to speech using text-to-speech.
Skinput technology turns the human body into a touchscreen input interface by using sensors to detect vibrations on the skin caused by taps and turns. It consists of an armband with sensors, a Bluetooth connection, and a small projector. When the user taps their skin, sensors detect the acoustic waves and can identify different locations tapped. The projector then displays a virtual keyboard or buttons onto the arm. The system works well but accuracy decreases for obese users or many input locations. Future applications could include texting by tapping on projected keyboards or controlling devices while walking.
The document presents an embedded real-time finger-vein recognition system for mobile devices. The system uses finger vein patterns as a biometric for authentication through image acquisition of the finger veins, processing the images through segmentation, enhancement and feature extraction, and human-machine communication. It was found to have high security, low power consumption, small size, quick response time of 0.8 seconds, and high accuracy with a low equal error rate of 0.07%.
The Blue Eyes technology aims to create machines that have human-like sensory abilities. It uses eye tracking and movement data collected by a data acquisition unit and processed by a central system unit. The technology employs sensors and methods like the Emotion Mouse, MAGIC, speech recognition, and SUITOR to interpret inputs. In the future, devices may be operated through gaze and voice commands enabled by advances in Blue Eyes technology.
This document describes the development of an automatic language translation software to aid communication between Indian Sign Language and spoken English using LabVIEW. The software aims to translate one-handed finger spelling input in Indian Sign Language alphabets A-Z and numbers 1-9 into spoken English audio output, and 165 spoken English words input into Indian Sign Language picture display output. It utilizes the camera and microphone of the device for image and speech acquisition, and performs vision and speech analysis for translation. The software is intended to help communication between deaf or speech-impaired individuals and those who do not understand sign language.
Skinput is a technology developed by Microsoft Research that allows a user's skin to act as an input surface. It uses arrays of highly tuned vibration sensors incorporated into an armband to detect acoustic waves generated by taps on the skin. The sensors are able to classify different inputs and locations of taps on the arm. While the prototype demonstrates the potential of the technology, its commercial viability will depend on Microsoft's commitment to further developing it.
The document discusses the features and design of a smart note taker. A smart note taker is a digital pen that can capture handwritten notes and convert them to digital text. It allows users to write notes in the air or on paper and have them saved digitally. The document outlines the internal components of a smart note taker including its database, block diagram, and how handwriting is recognized and converted to text. Advantages are that it is helpful for those who are blind and a time-saving device. Disadvantages include that smart note takers are expensive and processing can be slower.
This document discusses screenless display technologies, including visual image displays like holograms, retinal displays that project images directly onto the retina, and potential future synaptic interfaces. It describes the working principles of holograms and retinal displays in detail. Applications discussed include using screenless displays in mobile phones to help older or blind users, as well as potential uses in laptops and hologram projection.
Skinput is a technology that uses the human skin as an input surface. It works by using a combination of a pico projector to display a touchscreen interface on the skin, bioacoustic sensors to capture vibrations from touch inputs, and Bluetooth to connect an armband device to a mobile phone. This allows users to interact with their phone or other devices by simply tapping on their arm as if it were a touchscreen, reducing the need for physical buttons or other accessories. While it provides an innovative interactive experience, Skinput also faces challenges including potential degradation of input accuracy and high production costs.
Haptic technology provides tactile feedback through devices that allow users to touch and feel virtual objects. It works by applying forces, vibrations or motions to the user through input/output devices like data gloves. This gives users the sense of touch when interacting with computer-generated environments. Common haptic devices include Phantom, which provides 3D touch feedback of virtual objects, and Cyber Grasp, which fits over the hand and provides force feedback to each finger. Haptics have applications in virtual reality, medicine, video games, mobile devices, arts and robotics. The future may see holographic interaction and remote surgery using haptics.
Digital scent technology allows smells to be digitized and transmitted over the internet. It works by detecting smell molecules, indexing them, digitizing the scent file, and broadcasting it to receivers. Applications include scented movies, games, emails and websites. While it adds realism and immersion, issues include high costs, immaturity of the technology, and potential overuse of scents. Overall, digital scent has potential to enhance experiences once the technology is improved and costs lower.
A Brain-Computer Interface (BCI) provides a new communication channel between the human brain and the computer. The 100 billion neurons communicate via minute electrochemical impulses, shifting patterns sparking like fireflies on a summer evening, that produce movement, expression, words. Mental activity leads to changes of electrophysiological signals.
This document discusses hand gesture recognition using an artificial neural network. It aims to classify hand gestures into five categories (pointing one to five fingers) using a supervised feed-forward neural network and backpropagation algorithm. The objective is to facilitate communication for deaf people by automatically translating hand gestures into text. The system requires software like Pandas, Numpy and Matplotlib as well as hardware with a quad core processor and 16GB RAM. It explains key concepts of neural networks like neurons, weights, biases, activation functions and their advantages in handling large datasets and inferring unseen relationships.
As Digital Still Cameras (DSC) become smaller, cheaper and higher in resolution, photographs are increasingly prone to blurring from shaky hands. Optical image stabilization (OIS) is an effective solution that addresses the quality of images, and is an idea that has been around for at least 30 years. It has only recently made its way into the low-cost consumer camera market, and will soon be migrating to the higher end camera phones. This paper provides an overview of common design practices and considerations for optical image stabilization and how silicon-based MEMS dual-axis gyroscopes with their size, cost and performance advantages are enabling this vital function for image capturing devices
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.
Skinput is a new input technology developed by Microsoft that uses the human body as an input surface. It involves wearing an armband that detects vibrations on the skin from finger taps. This allows a user to control devices by tapping on their arm to browse menus, make calls, or control music players. The armband contains sensors that detect transverse and longitudinal acoustic waves generated by taps. It is a non-invasive way to interact with devices using the body's large interaction area. Skinput has applications for mobile devices, gaming, and assisting disabled individuals. While innovative, it faces challenges related to size, cost, and potential health effects that need further research.
The document summarizes a student project to develop a virtual mouse interface using computer vision and finger tracking. The project is divided into 5 modules: 1) basic video operations in OpenCV, 2) image processing techniques, 3) object tracking, 4) finger-tip detection, and 5) using detected finger motions to control mouse functions. Key functions demonstrated include moving the cursor, left and right clicking, dragging, brightness control, and scrolling. Evaluation of the system found finger tracking accuracy between 60-85% for different gestures. The project aims to provide an alternative input method that reduces hardware needs and workspace.
This presentation is given in (2015) . As the power of modern computers grows alongside our understanding of the human brain, we move ever closer to making some pretty spectacular science fiction into reality.
This document summarizes a seminar report on Blue Eyes Technology submitted by Ms. Roshmi Sarmah. The report describes Blue Eyes Technology, which aims to give computers human-like perceptual abilities such as vision, hearing, and touch. It discusses how this could allow computers to interact with humans more naturally by recognizing emotions, attention, and physical states. The report provides an overview of the Blue Eyes system hardware and its capabilities for monitoring a user's physiological signals, visual attention, and position in real-time using wireless sensors.
Virtual Mouse using hand gesture recognitionMuktiKalsekar
This project is to develop a Virtual Mouse using Hand Gesture Recognition. Hand gestures are the most effortless and natural way of communication. The aim is to perform various operations of the cursor. Instead of using more expensive sensors, a simple web camera can identify the gesture and perform the action. It helps the user to interact with a computer without any physical or hardware device to control mouse operation.
Skinput is an input technology that uses bio-acoustic sensing to localize finger taps on the skin. It works by using sensors in an armband to detect transverse and longitudinal sound waves produced from taps on the skin. These vibrations are detected and used to determine the tap location, allowing the skin to act as a touch interface. A pico-projector can display a virtual screen on the arm to provide visual feedback from the input. Research is ongoing to miniaturize the armband and expand the technology to control more devices just by tapping on the skin.
Haptics is the technology of adding the sense of touch to interactions with virtual objects and environments. It uses tactile feedback and force feedback to allow users to touch and feel virtual objects as if they were real. Some examples of haptic devices include Phantom devices that provide 3D touch sensations and Cyber Grasp systems that allow users to grasp virtual objects. Haptics has applications in gaming, design, robotics, medicine, and more. It provides advantages like reducing work time and increasing confidence in medical applications, but also has challenges with higher costs and limited force precision.
This document discusses the development of electronic skin (e-skin). It provides an overview and introduction to e-skin, which aims to mimic human skin. The objective is to develop flexible, compliant sensors. Key developments include attaching nanowire transistors to flexible substrates in 2010, creating stretchable solar cells to power e-skin in 2011, and developing a self-healing e-skin made of plastic and nickel in 2012. E-skin can measure vital signs, map pressure spatially, and be used in applications like robotics, health monitoring, and interactive devices. Future areas of development include using e-skin in vehicles and to predict medical issues in advance.
Goal Line Technology aims to accurately determine if the ball has crossed the goal line through various methods. It was introduced after controversial calls where goals were missed. Two main types are Hawk-Eye, which uses high-speed cameras to track ball movement, and the Cairos System, which embeds sensors in the ball and goal area that communicate to determine the ball's location. Both systems aim to provide instant notifications to referees of whether a goal has been scored to resolve disputes.
The document describes a new input technique called Skinput that allows a user's skin and body to be used as an input surface. It uses a wearable armband with small vibration sensors to detect finger taps on the arm based on the unique acoustic patterns generated. When a finger taps the skin, acoustic waves are produced and transmitted through the soft tissues and bones of the arm. The armband sensors are tuned to different resonant frequencies to pick up on these frequency signals. Experiments showed the system could accurately detect taps on different areas of the arm and distinguish individual fingers. This provides an "always available" input that does not require the user to hold or touch a device.
The document describes Skinput technology, which uses the surface of the skin as an input device. Skinput was developed by researchers at Microsoft to allow users to control devices by tapping on their skin. It works by using sensors in an armband to detect vibrations and acoustic signals caused by taps and gestures on the skin. This allows the user to perform tasks like making calls or controlling music just by tapping on projected interfaces on their arm, without directly touching a device. Potential applications include use by paralyzed individuals, in education, and for gaming. However, issues remain regarding cost, health effects, and wearability of the armband sensor.
Skinput is a technology developed by Microsoft Research that allows a user's skin to act as an input surface. It uses arrays of highly tuned vibration sensors incorporated into an armband to detect acoustic waves generated by taps on the skin. The sensors are able to classify different inputs and locations of taps on the arm. While the prototype demonstrates the potential of the technology, its commercial viability will depend on Microsoft's commitment to further developing it.
The document discusses the features and design of a smart note taker. A smart note taker is a digital pen that can capture handwritten notes and convert them to digital text. It allows users to write notes in the air or on paper and have them saved digitally. The document outlines the internal components of a smart note taker including its database, block diagram, and how handwriting is recognized and converted to text. Advantages are that it is helpful for those who are blind and a time-saving device. Disadvantages include that smart note takers are expensive and processing can be slower.
This document discusses screenless display technologies, including visual image displays like holograms, retinal displays that project images directly onto the retina, and potential future synaptic interfaces. It describes the working principles of holograms and retinal displays in detail. Applications discussed include using screenless displays in mobile phones to help older or blind users, as well as potential uses in laptops and hologram projection.
Skinput is a technology that uses the human skin as an input surface. It works by using a combination of a pico projector to display a touchscreen interface on the skin, bioacoustic sensors to capture vibrations from touch inputs, and Bluetooth to connect an armband device to a mobile phone. This allows users to interact with their phone or other devices by simply tapping on their arm as if it were a touchscreen, reducing the need for physical buttons or other accessories. While it provides an innovative interactive experience, Skinput also faces challenges including potential degradation of input accuracy and high production costs.
Haptic technology provides tactile feedback through devices that allow users to touch and feel virtual objects. It works by applying forces, vibrations or motions to the user through input/output devices like data gloves. This gives users the sense of touch when interacting with computer-generated environments. Common haptic devices include Phantom, which provides 3D touch feedback of virtual objects, and Cyber Grasp, which fits over the hand and provides force feedback to each finger. Haptics have applications in virtual reality, medicine, video games, mobile devices, arts and robotics. The future may see holographic interaction and remote surgery using haptics.
Digital scent technology allows smells to be digitized and transmitted over the internet. It works by detecting smell molecules, indexing them, digitizing the scent file, and broadcasting it to receivers. Applications include scented movies, games, emails and websites. While it adds realism and immersion, issues include high costs, immaturity of the technology, and potential overuse of scents. Overall, digital scent has potential to enhance experiences once the technology is improved and costs lower.
A Brain-Computer Interface (BCI) provides a new communication channel between the human brain and the computer. The 100 billion neurons communicate via minute electrochemical impulses, shifting patterns sparking like fireflies on a summer evening, that produce movement, expression, words. Mental activity leads to changes of electrophysiological signals.
This document discusses hand gesture recognition using an artificial neural network. It aims to classify hand gestures into five categories (pointing one to five fingers) using a supervised feed-forward neural network and backpropagation algorithm. The objective is to facilitate communication for deaf people by automatically translating hand gestures into text. The system requires software like Pandas, Numpy and Matplotlib as well as hardware with a quad core processor and 16GB RAM. It explains key concepts of neural networks like neurons, weights, biases, activation functions and their advantages in handling large datasets and inferring unseen relationships.
As Digital Still Cameras (DSC) become smaller, cheaper and higher in resolution, photographs are increasingly prone to blurring from shaky hands. Optical image stabilization (OIS) is an effective solution that addresses the quality of images, and is an idea that has been around for at least 30 years. It has only recently made its way into the low-cost consumer camera market, and will soon be migrating to the higher end camera phones. This paper provides an overview of common design practices and considerations for optical image stabilization and how silicon-based MEMS dual-axis gyroscopes with their size, cost and performance advantages are enabling this vital function for image capturing devices
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.
Skinput is a new input technology developed by Microsoft that uses the human body as an input surface. It involves wearing an armband that detects vibrations on the skin from finger taps. This allows a user to control devices by tapping on their arm to browse menus, make calls, or control music players. The armband contains sensors that detect transverse and longitudinal acoustic waves generated by taps. It is a non-invasive way to interact with devices using the body's large interaction area. Skinput has applications for mobile devices, gaming, and assisting disabled individuals. While innovative, it faces challenges related to size, cost, and potential health effects that need further research.
The document summarizes a student project to develop a virtual mouse interface using computer vision and finger tracking. The project is divided into 5 modules: 1) basic video operations in OpenCV, 2) image processing techniques, 3) object tracking, 4) finger-tip detection, and 5) using detected finger motions to control mouse functions. Key functions demonstrated include moving the cursor, left and right clicking, dragging, brightness control, and scrolling. Evaluation of the system found finger tracking accuracy between 60-85% for different gestures. The project aims to provide an alternative input method that reduces hardware needs and workspace.
This presentation is given in (2015) . As the power of modern computers grows alongside our understanding of the human brain, we move ever closer to making some pretty spectacular science fiction into reality.
This document summarizes a seminar report on Blue Eyes Technology submitted by Ms. Roshmi Sarmah. The report describes Blue Eyes Technology, which aims to give computers human-like perceptual abilities such as vision, hearing, and touch. It discusses how this could allow computers to interact with humans more naturally by recognizing emotions, attention, and physical states. The report provides an overview of the Blue Eyes system hardware and its capabilities for monitoring a user's physiological signals, visual attention, and position in real-time using wireless sensors.
Virtual Mouse using hand gesture recognitionMuktiKalsekar
This project is to develop a Virtual Mouse using Hand Gesture Recognition. Hand gestures are the most effortless and natural way of communication. The aim is to perform various operations of the cursor. Instead of using more expensive sensors, a simple web camera can identify the gesture and perform the action. It helps the user to interact with a computer without any physical or hardware device to control mouse operation.
Skinput is an input technology that uses bio-acoustic sensing to localize finger taps on the skin. It works by using sensors in an armband to detect transverse and longitudinal sound waves produced from taps on the skin. These vibrations are detected and used to determine the tap location, allowing the skin to act as a touch interface. A pico-projector can display a virtual screen on the arm to provide visual feedback from the input. Research is ongoing to miniaturize the armband and expand the technology to control more devices just by tapping on the skin.
Haptics is the technology of adding the sense of touch to interactions with virtual objects and environments. It uses tactile feedback and force feedback to allow users to touch and feel virtual objects as if they were real. Some examples of haptic devices include Phantom devices that provide 3D touch sensations and Cyber Grasp systems that allow users to grasp virtual objects. Haptics has applications in gaming, design, robotics, medicine, and more. It provides advantages like reducing work time and increasing confidence in medical applications, but also has challenges with higher costs and limited force precision.
This document discusses the development of electronic skin (e-skin). It provides an overview and introduction to e-skin, which aims to mimic human skin. The objective is to develop flexible, compliant sensors. Key developments include attaching nanowire transistors to flexible substrates in 2010, creating stretchable solar cells to power e-skin in 2011, and developing a self-healing e-skin made of plastic and nickel in 2012. E-skin can measure vital signs, map pressure spatially, and be used in applications like robotics, health monitoring, and interactive devices. Future areas of development include using e-skin in vehicles and to predict medical issues in advance.
Goal Line Technology aims to accurately determine if the ball has crossed the goal line through various methods. It was introduced after controversial calls where goals were missed. Two main types are Hawk-Eye, which uses high-speed cameras to track ball movement, and the Cairos System, which embeds sensors in the ball and goal area that communicate to determine the ball's location. Both systems aim to provide instant notifications to referees of whether a goal has been scored to resolve disputes.
The document describes a new input technique called Skinput that allows a user's skin and body to be used as an input surface. It uses a wearable armband with small vibration sensors to detect finger taps on the arm based on the unique acoustic patterns generated. When a finger taps the skin, acoustic waves are produced and transmitted through the soft tissues and bones of the arm. The armband sensors are tuned to different resonant frequencies to pick up on these frequency signals. Experiments showed the system could accurately detect taps on different areas of the arm and distinguish individual fingers. This provides an "always available" input that does not require the user to hold or touch a device.
The document describes Skinput technology, which uses the surface of the skin as an input device. Skinput was developed by researchers at Microsoft to allow users to control devices by tapping on their skin. It works by using sensors in an armband to detect vibrations and acoustic signals caused by taps and gestures on the skin. This allows the user to perform tasks like making calls or controlling music just by tapping on projected interfaces on their arm, without directly touching a device. Potential applications include use by paralyzed individuals, in education, and for gaming. However, issues remain regarding cost, health effects, and wearability of the armband sensor.
Skinput is a technology developed by Microsoft that allows a user's skin to be used as an input surface. It uses a combination of a pico-projector, bioacoustic sensors, and Bluetooth. The pico-projector displays interfaces on the user's arm. When the user taps their arm, vibrations are detected by bioacoustic sensors in an armband. The sensors convert the vibrations to signals sent via Bluetooth to a mobile device where software matches the signals to determine the tap location and perform the corresponding operation. Skinput provides an always available, on-body input system without requiring the user's visual attention.
Skinput is a technology developed by researchers at Carnegie Mellon University that allows a user's skin to serve as a touch interface. It uses sensors in an armband to detect vibrations on the skin caused by taps and turns those inputs into commands. The tapping locations are identified using the different acoustic signatures of longitudinal and transverse waves. While promising, current prototypes of Skinput technology have limitations including bulkiness of the armband and accuracy that depends on the user's body composition. However, it has potential applications for mobile devices, gaming, media playback and more. Future iterations aim to shrink the size of the hardware and expand its capabilities.
Skinput is a technology that allows a user's skin to serve as an interactive input surface. It works by using a pico-projector to display interfaces on a user's arm, and bio-acoustic sensors in an armband to detect finger taps on the skin. Vibrations from taps are captured by the sensors and sent to a mobile device via Bluetooth. The device analyzes the signals using machine learning algorithms to determine the tap location and perform the corresponding action. Skinput provides an input method that is more natural and accessible than traditional touchscreens, transforming the body into an interactive surface without needing to look at a device. It could enable new types of ubiquitous and eyes-free interaction with technology.
The Microsoft company have developed Skinput, a technology that appropriates the human body for acoustic transmission, allowing the skin to be used as an input surface. In particular, we resolve the location of finger taps on the arm and hand by analyzing mechanical vibrations that propagate through the body. We collect these signals using a novel array of sensors worn as an armband. This approach provides an always available, naturally portable, and on-body finger input system. We assess the capabilities, accuracy and limitations of our technique through a two-part, twenty-participant user study. To further illustrate the utility of our approach, we conclude with several proof-of-concept applications we developed.
Skinput is an input technology developed by researchers at Microsoft that uses bio-acoustic sensing to detect finger taps on a user's skin. It consists of an armband with sensors that can detect the unique sounds made when different parts of the skin are tapped. The armband is augmented with a pico-projector to project a graphical user interface directly onto the skin, allowing the arm to function as a touchscreen. The motivation was to enable touch-based input on mobile devices with smaller screens. The prototype armband accurately detected taps at different locations on the forearm and wrist through analysis of acoustic signal features.
This document summarizes a seminar presentation on using the human body as a touchscreen interface called "Skinput". Skinput uses bio-acoustic sensors and a pico-projector to turn the skin into an interactive surface. It works by sensing vibrations from finger taps on different parts of the body which are mapped to actions. Potential applications include controlling mobile devices, games, and smart home devices by tapping on the skin without looking at a screen. The technology aims to advance interactive capabilities in more flexible environments.
1) This document discusses a technology that can convert sound energy into electrical energy using a piezoelectric effect. Researchers from Texas and the University of Houston developed this concept using piezoelectric materials around 21 nanometers thick.
2) The technology works by using piezoelectric nanogenerators that convert mechanical energy from vibrating sound absorbing pads and zinc electrodes into electrical energy to charge batteries.
3) This sound-powered technology could allow devices to be charged using ambient sounds and has applications to capture wasted noise energy from traffic or other sources for power generation.
Skinput is an input technology developed by researchers at Microsoft that uses bio-acoustic sensing to detect finger taps on the skin and determine their location, allowing the human body to serve as a touchscreen interface. It can provide a graphical user interface projected directly onto the skin through augmentation with a pico-projector. The system allows users to control applications by tapping their fingers on their arm or other body parts like a touchscreen.
The document describes a seminar report submitted by Yogesh Sharma on nanobotics to fulfill requirements for a Bachelor of Technology degree. It includes an abstract that discusses nanorobotics as the field of creating robots at the microscopic nanometer scale and potential applications in medical technology and environmental monitoring. The report also provides acknowledgements, table of contents, introduction to nanorobotics concepts, and planned chapters on topics like biochips, fractal robots, and challenges of nanobotics.
Skinput is a technology that uses the skin's surface as an input device. It works by having a wearable armband with acoustic detectors that can sense vibrations when the user taps their skin. This allows the user to control devices by tapping designated areas on their arm that have virtual buttons projected onto them. Some potential applications include using it to control mobile phones, music players, games, or to help disabled individuals interact with technology. While innovative, it still faces limitations such as wearability of the armband and lack of extensive safety testing.
This seminar report discusses dual clutch transmission systems. It begins with an introduction to transmission systems in general and their components. It then discusses different types of transmission systems like manual, automatic, and torque converter systems. The bulk of the report focuses on dual clutch transmission systems, including their history, overview, components like the clutches used, and comparisons to automatic and manual systems. It provides details on how dual clutch transmissions work and the benefits they provide over traditional automatic transmissions.
Wearable technology devices that can be worn by consumers include smartwatches, fitness trackers, smart glasses, and more. Google Glass is an augmented reality smart glasses developed by Google that displays information hands-free via voice commands. The Air Umbrella concept replaces the plastic umbrella top with a windshield and uses air to mimic a standard canopy. The Lark sleep sensor tracks sleep patterns and quality through a wristband and app, using gentle vibrations as an alarm to avoid stress responses. Key challenges for wearable devices include short battery life, large size, poor aesthetics turning off consumers, and the need to demonstrate clear value beyond smartphones.
Project Glass is an augmented reality head-mounted display developed by Google. The glasses allow hands-free access to information and allow users to interact with the internet via voice commands. Key features include a small video display, front-facing camera, speaker, and a single button. The glasses operate using Google's Android platform and can access information from Google services and the internet through a 4G or WiFi connection.
The document provides an overview of a seminar report submitted by Prakhar Gupta on Google Glass. The report includes an introduction to concepts like virtual reality and augmented reality. It discusses the key technologies powering Google Glass like wearable computing, ambient intelligence and 4G. The report also covers the design and working of Google Glass and analyzes its advantages and disadvantages. It concludes with the future scope of augmented reality devices like Google Glass.
This is a Seminar Report on a computer security mechanism named Honeypot. In this I've included Honeypot Basics, Types, Value, Implementation, Merits & Demerits, Legal issues and Future of Honeypots.
Project Glass is a Google research project to develop smart glasses featuring a head-mounted display and allowing hands-free access to information via natural language voice commands. The glasses are being developed by Google X Lab and will communicate with mobile phones via WiFi to display notifications and respond to voice commands. Some key features of Google Glass include a small video display, camera, speaker, microphone and touchpad. [/SUMMARY]
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Virtual Mouse Control Using Hand GesturesIRJET Journal
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MONIKA S V.pptx skin put technology guidenc ofRavikiranaVS
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Skinput technology appropriating the body as an input surfaceVarsha Rajput
Skinput is a technology developed by Microsoft that allows the human body to be used as an input surface. It works by using sensors in an armband to detect vibrations on the skin caused by touch inputs. These inputs are classified and can then be used to control a virtual interface projected onto the skin. Some potential applications include controlling mobile devices, gaming, and assisting disabled individuals. However, challenges remain regarding accuracy and miniaturizing the sensor armband technology.
The document provides information about an industrial training report submitted by Rajesh Kumar to fulfill the requirements for a Bachelor of Technology degree. It includes a declaration by Rajesh Kumar, an acknowledgement of those who provided guidance and support, and an introduction to CSIO (Central Scientific Instruments Organisation) where the training took place. CSIO is described as a laboratory that works on research, design and development of scientific and industrial instruments across various fields.
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This document describes a proposed sign language interpreter system that uses machine learning and computer vision techniques. It aims to enable deaf and mute users to communicate through computers and the internet by recognizing static hand gestures from camera input and translating them to text. The proposed system extracts features from captured images of signs and uses a support vector machine model to classify the gestures by comparing to a dataset of labeled images. If implemented, this system could help overcome communication barriers for deaf users in an increasingly digital world.
EVALUATION & TRENDS OF SURVEILLANCE SYSTEM NETWORK IN UBIQUITOUS COMPUTING EN...Eswar Publications
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in our environments.
The document describes a capstone project for the fabrication of a human controlled robotic hand. It was submitted by four students - Prashant Anand Ranjan, Akshay Kumar, Akshay Saini, and Hitesh Jyoti - in partial fulfillment of their Bachelor of Technology degree in Mechanical Engineering at Lovely Professional University, under the guidance of Puneet Kumar Dawer. The project involved designing and building a robotic hand that can be controlled by human input to mimic the movement of a real human hand.
An HCI Principles based Framework to Support Deaf CommunityIJEACS
Sign language is a communication language preferred and used by a deaf person to converse with the common people in the community. Even with the existence of the sign language, there exist a communication gap between the normal and the disable/deaf person. Some solutions such as sensor gloves already are in place to address this problem area of communication, but they are limited and are not covering all parts of the language as required by the deaf person for the ordinary person to understand what is said and wanted? Due to the lack of credibility of the existing solutions for sign language translation, we have proposed a system that aims to assist the deaf people in communicating with the common people of the society and helping, in turn, the disabled people to understand the healthy (normal people) easily. Knowing the needs of the users will help us in focusing on the Human Computer Interaction technologies for deaf people to make it further more a user-friendly and a better alternative to the existing technologies that are in place. The Human Computer Interface (HCI) concept of usability, empirical measurement and simplicity are the key consideration in the development of our system. The proposed Kinect System removes the need for physical contact to operate by using Microsoft Kinect for Windows SDK beta. The result shows that the It has a strong, positive and emotional impact on persons with physical disabilities and their families and friends by giving them the ability to communicate in an easy manner and non-repetitive gestures.
Hand Motion Gestures For Mobile Communication Based On Inertial Sensors For O...IJERA Editor
This project presents system based on inertial sensors and gesture recognition algorithm for SMS or calling for old age people. Users hold the device to make hand gestures with their preferred handheld style. Hand motions generate inertial signals, which are wirelessly transmitted to a computer for recognition. Here DTW recognition algorithm is used for recognition of hand gestures. Zigbee is used at the transmission section of inertial device to transmit sensor values and at the receiver section of PC to receive values. Recognized gesture is send to the microcontroller for further processing which gives AT commands to GSM to selects the SMS or calling option to the person. GSM model is used for the SMS or calling. An accelerometer-based gestures recognition systems that uses only a single 3-axis accelerometer. 3-axis accelerometer recognizes gestures, where gestures here are hand movements. DTW algorithm is used in this project for recognition. The proposed DTW-based recognition algorithm includes the procedures of inertial signal acquisition, motion detection, template selection, and recognition. Here „a‟, „b‟, „c‟, „d‟, „e‟, „f‟, „g‟, „h‟, „o‟, „v‟ letters are recognized in this system . This system can be used for the emergency calling or emergency SMS by the old age people or blind people from the home.
GSM Mobile Phone Based LCD Message Display SystemManish Kumar
This document is a project report submitted by students to fulfill the requirements for a bachelor's degree in electronics instrumentation and control engineering. It describes the development of a GSM mobile phone based LCD scrolling message display system. The system allows text messages to be sent via GSM and displayed on an LCD screen. The report includes chapters on introduction, literature survey, problem definition, system requirements, system modeling and design, implementation, testing, and conclusion. It provides details on the components used, software requirements, system design, and testing results.
A Survey Paper on Controlling Computer using Hand GesturesIRJET Journal
This document summarizes a survey paper on controlling computers using hand gestures. It discusses various techniques that have been used for hand gesture recognition in previous research papers. The paper reviews literature on hand gesture recognition methods based on sensor technology and computer vision. It describes applications of hand gesture recognition such as controlling media playback, scrolling web pages, and presenting slides. Common challenges with hand gesture recognition are also mentioned, such as dealing with complex backgrounds and lighting conditions. The goal of the paper is to perform a literature review on prominent techniques, applications, and difficulties in controlling computers using hand gestures.
The document describes the components and working of Sixth Sense technology, which is a wearable gestural interface. It consists of a camera, projector, mirror, smartphone, and color markers on the fingertips. The camera captures images and tracks hand gestures via the color markers. The smartphone processes the data and searches the internet. It projects information onto surfaces using the projector and mirror. The technology bridges the physical and digital world by recognizing objects and displaying related information using hand gestures.
Controlling Computer using Hand GesturesIRJET Journal
This document describes a research project on controlling a computer using hand gestures. The researchers created a real-time gesture recognition system using convolutional neural networks (CNNs). They developed a dataset of 3000 training images of 10 different hand gestures for tasks like opening apps. A CNN model was trained to detect hands in images and recognize gestures. The model achieved 80.4% validation accuracy and was able to successfully perform operations like opening WhatsApp, PowerPoint and other apps based on detected gestures in real-time. The system provides a cost-effective and contactless way of interacting with computers using hand gestures only.
College of Engineering and Technology.docxMisganDagnew
This internship report summarizes the experiences of three computer science students from Wollega University during their two-month internship at the Debre Tabor University Internet of Things (DTU_IOT) Lab. The report describes the structure and workflow of DTU_IOT Lab, the various projects and tasks the interns worked on, including creating Arduino programs and hardware projects. It also outlines the many benefits gained from the internship, such as improving practical skills, applying theoretical knowledge, and developing teamwork, leadership, and entrepreneurship abilities.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
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The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
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Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
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People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
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Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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End-to-end overview of CI/CD pipeline with Azure devops
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In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Generative AI Deep Dive: Advancing from Proof of Concept to Production
Skinput
1. Skinput
1 IIMT College of Engineering
SKINPUT
Seminar Report submitted in partial fulfilment of the
Requirement for the degree of
Bachelor of Technology
In
Computer science & engineering
Under the supervision of
Mr. Vipin Rai
Ms. Kritika Goel
By
HIMANSHU SINGH SAJWAN
To
Department of Computer Science & Engineering
IIMT COLLEGE OF ENGINEERING, GR. NOIDA
Utter Pradesh Technical University,
Lucknow
Session: 2014-15
2. Skinput
2 IIMT College of Engineering
DECLARATION
This is to certify that Report entitled “SKINPUT ” submitted by Himanshu Singh Sajwan
which is submitted by me in partial fulfillment of the requirement for the award of degree
B.Tech. in Computer Sc. & Engineering/Information Technology to Deptt of Computer Sc &
Engg,IIMT College of Engg,Gr Noida, Uttar Pradesh Technical University, Lucknow
comprises only my own work and due acknowledgement has been made in the text to all other
material used.
Date: 20-04-2015 Name of Student : Himanshu Singh Sajwan
Approved By : Head Of Department, CSE, IIMT, Gr Noida
3. Skinput
3 IIMT College of Engineering
ACKNOWLEDGEMENT
At the very outset, I take this opportunity to convey my heartfelt gratitude to those persons
whose co-operation, suggestions and support helped me to accomplish the project successfully.
I take immense pleasure to express my sincere thanks and profound gratitude to our respected
Dr. Prabhat Kr. Vishwakarma, H.O.D. and Mr. Vipin Rai, Mrs. Kirtika Goel, Department of
Computer Sciences Engineering, IIMT College of Engg., Gr. Noida for his kind co-operation and
able guidance, valuable suggestions and encouragement he rendered for completing the Seminar
topic.
I express my sincere thanks to all the faculty members of the Department of Computer Sciences
Engineering, for providing the encouragement and environment for the success of my topic.
In the end, I would be failing in my duties if I do not express my heartfelt gratitude to my family
whose constant inspiration and patience have helped me to complete this work. And last but not
the least I would like to thank God for all he has given me till today.
4. Skinput
4 IIMT College of Engineering
CERTIFICATE
This is to certify that Report entitled “Modern Operating System” which is
submitted by Karan Panjwani in partial fulfillment of the requirement for the
award of degree B.Tech. in Computer Sc. & Engineering in IIMT College of
Engg,Gr Noida is a record of the candidate own work carried out by him under
my/our supervision. The matter embodied in this work is original and has not been
submitted for the award of any other degree.
Date: 20-04-2015
Mr. Vipin Rai
Ms. Kirtika Goel -------------------------------
Dr. Prabhat Kr. Vishwakarma
Seminar Guide H.O.D of CSE Dept.
5. Skinput
5 IIMT College of Engineering
INDEX
1.INTRODUCTION…………………………………………………………….. ….7
2.WHAT IS SKINPUT?..............................................................................................8
2.1 Always available input…………………………………………………….9
2.2 Bio-Sensing………………………………………………………….…….10
2.3 Principles of Skinput……………………………………………………..12
3. TECHNOLOGIES IN SKINPUT……………………………………………… 13
3.1 Pico projector……………………………………………………………..14
3.2 Bluetooth………………………………………………………………….15
3.3 Bio-Acoustics and Sensors………………………………………………..16
4.HOW DOES IT WORK……………………………………………………...........18
4.1 Processing………………………………………………………………….19
5.ADVANTAGES OF SKINPUT………………………………………………......20
6.DISADVANTAGES OF SKINPUT……………………………………………. ..21
7.APPLICATIONS OF SKINPUT………………………………………………… 22
8.FUTURE IMPLICATIONS…………………………………………………….. .23
9.CONCLUSION…………………………………………………………………… 24
10.REFERENCES………………………………………………………………….. 25
6. Skinput
6 IIMT College of Engineering
ABSTRACT
Skinput is an input technology that uses bio-acoustic sensing to localize finger
taps on the skin. When augmented with a Pico-projector, the device can provide a
direct manipulation, graphical user interface on the body. The technology was
developed by Chris Harrison, Desney Tan, and Dan Morris, at MicrosoftResearch's
Computational User Experiences Group. Skinput represents one way to decouple
input from electronic devices with the aim of allowing devices to become smaller
without simultaneously shrinking the surface area on which input can be
performed. While other systems, like Sixth Sense have attempted this with
computer vision, Skinput employs acoustics, which take advantage of the human
body's natural sound conductive properties (e.g., bone conduction). This allows
the body to be annexed as an input surface without the need for the skin to be
invasively instrumented with sensors, tracking markers, or other items.
7. Skinput
7 IIMT College of Engineering
1.INTRODUCTION
Devices with significant computational power and capabilities can now be easily
carried on our bodies. However, their small size typically leads to limited
interaction space (e.g. diminutive screens, buttons, and jog wheels) and
consequently diminishes their usability and functionality. Since it cannot simply
make buttons and screens larger without losing the primary benefit of small size,
consider alternative approaches that enhance interactions with small mobile
systems. One option is to opportunistically appropriate surface area from the
environmentfor interactive purposes. Thereis one surfacethat has been previous
overlooked as an input canvas, and one that happens to always travelwith us: our
skin. Appropriating the human body as an input device is appealing not only
because we have roughly two square meters of external surface area, but also
because much of it is easily accessible by our hands (e.g., arms, upper legs, torso).
Furthermore, proprioception – our sense of how our body is configured in three-
dimensional space – allows us to accurately interact with our bodies in an eyes-
free manner. For example, we can readily flick each of our fingers, touch the tip of
our nose, and clap our hands together without visual assistance. Few external
input devices can claim this accurate, eyes-free input characteristic and provide
such a large interaction area. In this paper, we present our work on Skinput – a
method that allows the body to be appropriated for finger input using a novel,
non-invasive, wearable bio-acoustic sensor.
8. Skinput
8 IIMT College of Engineering
2.What is Skinput?
The Microsoft company have developed Skinput , a technology that appropriates
the human body for acoustic transmission, allowing the skin to be used as an input
surface. In particular, we resolve the location of finger taps on the arm and hand by
analyzing mechanical vibrations that propagate through the body. We collect these
signals using a novel array of sensors worn as an armband. This approach provides
an always available, naturally portable, and on-body finger input system. We
assess the capabilities, accuracy and limitations of our technique through a two-
part, twenty-participant user study. To further illustrate the utility of our approach,
we conclude with several proof-of-concept applications we developed
9. Skinput
9 IIMT College of Engineering
2.1 Always-Available Input
The primary goal of Skinput is to provide an always available mobile input system
– that is, an input system that does not require a user to carry or pick up a device. A
number of alternative approaches have been proposedthat operate in this space.
Techniques based on computer vision are popular . These, however, are
computationally expensive and error prone in mobile scenarios (where, e.g., non-
input optical flow is prevalent). Speechinput is a logical choice for always-
available input, but is limited in its precision in unpredictable acoustic
environments, and suffers from privacy and scalability issues in shared
environments. Other approaches have taken the form of wearable computing. This
typically involves a physical input device built in a form considered to be part of
one’s clothing. Forexample, glove-based input systems allow users to retain most
of their natural hand movements, but are cumbersome, uncomfortable, and
disruptive to tactile sensation. A “smart fabric” system that embeds sensors and
conductors into a brick, but taking this approachto always-available input
necessitates embedding technology in all clothing, which would be prohibitively
complex and expensive. The Sixth-Sense project proposesa mobile, always
available input/output capability by combining projected information with a
colormarker-based vision tracking system. This approachis feasible, but suffers
from serious occlusion and accuracy limitations. Forexample, determining
whether, e.g., a finger has tapped a button, or is merely hovering above it, is
extraordinarily difficult. In the present work, we briefly explore the combination of
on-bodysensing with on-bodyprojection.
10. Skinput
10 IIMT College of Engineering
2.2 Bio-Sensing
Skinput leverages the natural acoustic conduction properties of the human body to
provide an input system, and is thus related to previous work in the use of biological
signals for computer input. Signals traditionally used for diagnostic medicine, such as
heart rate and skin resistance, have been appropriated for assessing a user's emotional
state. These features are generally subconsciouslydriven and cannot be controlled with
sufficient precision for direct input. Similarly, brain sensing technologies such as
electroencephalography (EEG) & functional near-infrared spectroscopy (fNIR) have
been used by HCI researchers to assess cognitive and emotional state; this work also
primarily looked at involuntary signals. In contrast, brain signals have been harnessed
as a direct input for use by paralyzed patients, but direct brain computer interfaces
(BCIs) still lack the bandwidth requiredfor everyday computing tasks, and require
levels of focus, training, and concentration that are incompatible with typical
computer interaction.
There has been less work relating to the intersection of finger input and biological
signals. Researchers have harnessed the electrical signals generated by muscle
activation during normal hand movement through electromyography (EMG). At
present, however, this approach typically requires expensive amplification systems
and the application of conductive gel for effective signal acquisition, which would
limit the acceptability of this approach for most users. The input technology most
related to our own is that of Amento et al who placed contact microphones on a user's
wrist to assess finger movement. However, this work was never formally evaluated, as
is constrained to finger motions in one hand.
The Hambone system employs a similar setup, and through an HMM, yields
classification accuracies around 90% for four gestures (e.g., raise heels, snap fingers).
Performance of false positive rejection remains untested in both systems at present.
Moreover, both techniques required the placement of sensors near the area of
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interaction (e.g., the wrist), increasing the degree of invasiveness and visibility.
Finally, bone conduction microphones and headphones - now common consumer
technologies - represent an additional bio-sensing technology that is relevant to the
present work. These leverage the fact that sound frequencies relevant to human speech
propagate well through bone.
Bone conduction microphones are typically worn near the ear, where they can sense
vibrations propagating from the mouth and larynx during speech. Bone conduction
headphones send sound through the bones of the skull and jaw directly to the inner
ear, bypassing transmission of sound through the air and outer ear, leaving an
unobstructed path for environmental sounds
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2.3Principles of Skinput
It listens to vibrations in your body.
Skinput also responds to the various hand gestures.
The arm is an instrument.
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3.Technologies in Skinput
There are Three technologies used for Skinput.
1. Pico-Projector
2. Bluetooth
3. Bio-Acoustics and Sensors
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3.1Pico-Projector
Pico-Projector is employed as Output device that show menu. It’s employed in
mobile and camera to show the project. Pico-projectors are small, but they can
show large displays (sometimes up to 100"). While great for mobility and content
sharing, pico-projectors offer low brightness and resolution compared to larger
projectors. It is a new innovation, but pico-projectors are already selling at a rate of
about a million units a year (in 2010), and the market is expected to continue
growing quickly.
pico projector in mobile phone
How do pico projectors work?
There are several companies developing and producing pico projectors, and there
are 3 major technologies: DLP, LCoS and Laser-Beam-Steering (LBS).
DLP and LCoS use a white light source, and some sort of filtering technique to
create a different brightness and color on each pixel:
DLP (Digital Light Processing) the idea behind DLP is to use tiny mirrors on a
chip that direct the light. Each mirror controls the amount of light each pixel on the
target picture gets (the mirror has two states, on and off. It refreshes many times in
a second - and if 50% of the times it is on, then the pixel appears at 50% the
brightness). Color is achieved by a using a color wheel between the light source
and the mirrors - this splits the light in red/green/blue, and each mirror controls all
thee light beams for its designated pixel.
LCoS (Liquid Crystal on Silicon): an LCoS projector uses a small liquid-crystal
display (LCD) to control how much light each pixel gets. There are two basic
designs to get color: Color-Filter (CF-LCoS) which uses 3 subpixels, each with its
own color (RGB) and a Field-Sequential-Color (FSC) which uses a faster LCD
with a color filter - so you split the image for the 3 main colors (RGB) sequentially
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and you refresh the LCD 3 times (once for each color). The light source for the
LCoS can be LED or diffused laser.
Laser-Beam-Steering (LBS) projectors are different, creating the image
one pixel at a time, using a directed laser beam. You start with 3
different lasers (Red/Green/Blue), each at its required brightness, which
are combined using optics, and guided using a mirror (or two mirrors in
some designs). If you scan the image fast enough (usually at over 60Hz),
you do not notice this pixel-by-pixel design.
3.2Bluetooth
It’s used to connectthe Bio-Acoustic sensing element for mobile in order so that
information will be transferred to many being controlled devices like mobile, iPod
or laptop
Bluetoothis a wireless technology standard for exchanging data over short
distances (using short-wavelength UHF radio waves in the ISM band from 2.4 to
2.485 GHzfrom fixed and mobile devices, and building personal area
networks (PANs). Invented by telecom vendor Ericsson in 1994, it was originally
conceived as a wireless alternative to RS-232 data cables. It can connect several
devices, overcoming problems of synchronization.
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3.3Bio-Acoustics and Sensors:
When a finger taps the skin, several distinct forms of acoustic energy are produced.
Some energy is radiated into the air as sound waves; this energy is not captured by
the Skinput system. Among the acoustic energy transmitted through the arm, the
most readily visible are transverse waves, created by the displacement of the skin
from a finger impact (Figure 1).
Figure 1
When shot with a high-speed camera, these appear as ripples, which propagate
outward from the point of contact. The amplitude of these ripples is correlated to
both the tapping force and to the volume and compliance of softtissues under the
impact area. In general, tapping on soft regions of the arm creates higher amplitude
transverse waves than tapping on boney areas (e.g., wrist, palm, fingers), which
have negligible compliance. In addition to the energy that propagates on the
surface of the arm, some energy is transmitted inward, toward the skeleton. These
longitudinal (compressive) waves(Figure 2). travel through the softtissues of the
arm, exciting the bone, which is much less deformable then the softtissue but can
respond to mechanical excitation by rotating and translating as a rigid body.
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Figure 2
Bio-Acoustics: Sensing
These signals need to be sensed and worked upon.
This is done by wearing the wave sensorarm band.
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4.HOW DOES IT WORK
The operating of this Skinput Technology depends on the show and detects
principle that uses all 3 parts to producethe result.
Step 1. Oncea user faucets on skin surface then Armband Bio-Acoustics sensing
element detects the activated or touched part of the skin surface by measure the
sound frequency variations in bodydensity, size, mass and impact of sentimental
tissues and joints. These variations are then reborn into a digital signal kind.
Step 2. Currently a wireless property technology Bluetooth is employed to attach
the Armband Bio-Acoustics sensing element to Mobile, iPod and computers in
order that the data/command will be transmitted to those devices that are being
controlled. For this software system that matches the sound frequencies of a
particular skin surface location is employed. Different correspondingoperations
are distributed within the device to producethe result.
Step 3. The final step involves the purposeof the show. A Pico-projector is
employed during this step as output shows devices operating as a projector to show
the menu. This sort of the projectors is employed in mobiles and cameras to show
the project.
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5.ADVANTAGES OF SKINPUT
No need to interact with the gadget directly. Easy to access when your phone
is not available
Don’t have to worry about keypad.
People with larger fingers get trouble in navigating tiny buttons and keypads
on mobile phones. With skinput this problem disappears.
The projected interface can appear much larger than it ever could on a
device’s screen. One can also bring his arm closer to the face (or vice versa)
to see the display close up. Dimming the lights creates an even greater color
contrast if skin and the text are too similar in color during daylight.
Allows users to interact more personally with their device
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6.DISADVANTAGES OF SKINPUT
If the user has more than a 30% Body Mass Index Skinput is reduced to 80%
accuracy
The arm band is currently bulky
The visibility of the projection of the buttons on the skin can be reduced if
the user has a tattoo located on their arm
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7.APPLICATIONS OF SKINPUT
Mobile
Gaming
I-pods
An aid to paralyzed persons.
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8.Future Implications
With small sized Pico-projectors, Skinput oriented systems, are an emerging
trend.
Research is carried out for smaller wrist watch sized sensor arm band.
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9.CONCLUSION
Through Skinput, a technological approach to use human body as an input surface
is achieved. The wearable bioacoustic sensor array used in the skinput plays a fine
role here. The Skinput approach is proved to be useful and better for different
gestures when the body is in motion. As a future work, many features like taps
with different parts of the finger, single handed gestures and differentiating
between objects and materials are being explored and researched with Skinput.
Last but not the least, the different applications of Skinput helps us to give a clear
idea at what extent we can use this technology effectively. Likewise, Sixth
sense also projects information on varied surfaces thus extending the limits of
projection from screen to the physical world.
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10.REFERENCES
"Skinput:Appropriating the Body as an Input Surface". Microsoft Research
Computational User Experiences Group
Skinput “Wikipedia”
"Skinput: Appropriating the Body as an Input Surface". www.Youtube.com
www.google.com