The Experts below are selected from a list of 8607 Experts worldwide ranked by ideXlab platform
Thad Starner - One of the best experts on this subject based on the ideXlab platform.
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an underwater Wearable Computer for two way human dolphin communication experimentation
International Symposium on Wearable Computers, 2013Co-Authors: Daniel Kohlsdorf, Thad Starner, Scott Gilliland, Peter Presti, Denise HerzingAbstract:Research in dolphin cognition and communication in the wild is still a challenging task for marine biologists. Most problems arise from the uncontrolled nature of field studies and the challenges of building suitable underwater research equipment. We present a novel underwater Wearable Computer enabling researchers to engage in an audio-based interaction between humans and dolphins. The design requirements are based on a research protocol developed by a team of marine biologists associated with the Wild Dolphin Project.
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an underwater Wearable Computer for two way human dolphin communication experimentation
ISWC Conference, 2009Co-Authors: Daniel Kohlsdorf, Thad Starner, Scott Gilliland, Peter Presti, Denise HerzingAbstract:Research in dolphin cognition and communication in the wild is still a challenging task for marine biologists. Most problems arise from the uncontrolled nature of field studies and the challenges of building suitable underwater research equipment. We present a novel underwater Wearable Computer enabling researchers to engage in an audio-based interaction between humans and dolphins. The design requirements are based on a research protocol developed by a team of marine biologists associated...
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mobile capture for Wearable Computer usability testing
International Symposium on Wearable Computers, 2001Co-Authors: Kent Lyons, Thad StarnerAbstract:The-mobility of Wearable Computers makes usability-testing; difficult. In order to fully understand how a user interacts with the Wearable, the researcher must examine, both the user's direct interactions, with the, Computer, as well as the external context the user perceives during their interaction. We present, a tool that augments a Wearable Computer with additional hardware and software to capture the information needed to perform a usability study in the field under realistic conditions. We examine the challenges in doing the capture and present our implementation. We also describe VizWear a tool for examining the captured data. Finally, we present our experiences using the system for a sample user study.
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Heat dissipation in Wearable Computers aided by thermal coupling with the user
Mobile Networks and Applications, 1999Co-Authors: Thad Starner, Yael MaguireAbstract:Wearable Computers and PDA's are physically close to, or are in contact with, the user during most of the day. This proximity would seemingly limit the amount of heat such a device may generate, conflicting with user demands for increasing processor speeds and wireless capabilities. However, this paper explores significantly increasing the heat dissipation capability per unit surface area of a mobile Computer by thermally coupling it to the user. In particular, a heat dissipation model of a forearm-mounted Wearable Computer is developed, and the model is verified experimentally. In the process, this paper also provides tools and novel suggestions for heat dissipation that may influence the design of a Wearable Computer.
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real time american sign language recognition using desk and Wearable Computer based video
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998Co-Authors: Thad Starner, Joshua Weaver, Alex PentlandAbstract:We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American sign language (ASL) using a single camera to track the user's unadorned hands. The first system observes the user from a desk mounted camera and achieves 92 percent word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.
Alex Pentland - One of the best experts on this subject based on the ideXlab platform.
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real time american sign language recognition using desk and Wearable Computer based video
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998Co-Authors: Thad Starner, Joshua Weaver, Alex PentlandAbstract:We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American sign language (ASL) using a single camera to track the user's unadorned hands. The first system observes the user from a desk mounted camera and achieves 92 percent word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.
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visual contextual awareness in Wearable computing
International Symposium on Wearable Computers, 1998Co-Authors: Thad Starner, Bernt Schiele, Alex PentlandAbstract:Small, body-mounted video cameras enable a different style of Wearable computing interface. As processing power increases, a Wearable Computer can spend more time observing its user to provide serendipitous information, manage interruptions and tasks, and predict future needs without being directly commanded by the user. This paper introduces an assistant for playing the real-space game Patrol. This assistant tracks the wearer's location and current task through Computer vision techniques and without off-body infrastructure. In addition, this paper continues augmented reality research, started in 1995, for binding virtual data to physical locations.
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a Wearable Computer based american sign language recognizer
Lecture Notes in Computer Science, 1998Co-Authors: Thad Starner, Joshua Weaver, Alex PentlandAbstract:Modern Wearable Computer designs package workstation level performance in systems small enough to be worn as clothing. These machines enable technology to be brought where it is needed the most for the handicapped: everyday mobile environments. This paper describes a research effort to make a Wearable Computer that can recognize (with the possible goal of translating) sentence level American Sign Language (ASL) using only a baseball cap mounted camera for input. Current accuracy exceeds 97% per word on a 40 word lexicon.
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ISWC - A Wearable Computer based American sign language recognizer
1997Co-Authors: Thad Starner, Joshua Weaver, Alex PentlandAbstract:Modern Wearable Computer designs package work-station level performance in systems small enough to be worn as clothing. These machines enable technology to be brought where it is needed the most for the handicapped: everyday mobile environments. This paper describes a research effort to make a Wearable Computer that can recognize (with the possible goal of translating) sentence level American Sign Language (ASL) using only a baseball cap mounted camera for input. Current accuracy exceeds 97% per word on a 40 word lexicon.
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A Wearable Computer-based American sign Language Recogniser
Personal Technologies, 1997Co-Authors: Thad Starner, Joshua Weaver, Alex PentlandAbstract:Modern Wearable Computer designs package workstation-level performance in systems small enough to be worn as clothing. These machines enable technology to be brought where it is needed most for the handicapped: everyday mobile environments. This paper describes a research effort to make a Wearable Computer that can recognise (with the possible goal of translating) sentence-level American Sign Language (ASL) using only a baseball cap mounted camera for input. Current accuracy exceeds 97% per word on a 40-word lexicon.
Ali H. Sayed - One of the best experts on this subject based on the ideXlab platform.
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a robust finger tracking method for multimodal Wearable Computer interfacing
IEEE Transactions on Multimedia, 2006Co-Authors: Sylvia M. Dominguez, Trish Keaton, Ali H. SayedAbstract:Mobile Wearable Computers are intended to provide users with real-time access to information in a natural and unobtrusive manner. Computing and sensing in these devices must be reliable, easy to interact with, transparent, and configured to support different needs and complexities. This paper presents a vision-based robust finger tracking algorithm combined with audio-based control commands that is integrated into a multimodal unobtrusive user interface, wherein the interface may be used to segment out objects of interest in the environment by encircling them with the user's pointing fingertip. In order to quickly extract the objects encircled by the user from a complex scene, this unobtrusive interface uses a single head-mounted camera to capture color images, which are then processed using algorithms to perform: color segmentation, fingertip shape analysis, perturbation model learning, and robust fingertip tracking. This interface is designed to be robust to changes in the environment and user's movements by incorporating a state-space estimation with uncertain models algorithm, which attempts to control the influence of uncertain environment conditions on the system's fingertip tracking performance by adapting the tracking model to compensate for the uncertainties inherent in the data collected with a Wearable Computer
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A multimodal Wearable Computer interface using state-space estimation with uncertain models
2006Co-Authors: Ali H. Sayed, Sylvia Margarita Dominguez-aguayoAbstract:Mobile Wearable Computers are intended to provide users with real-time access to information in a natural and unobtrusive manner. Computing and sensing in these devices must be reliable, easy to interact with, transparent, and configured to support different needs and complexities. Therefore, one critical factor for the success of a Wearable Computer is its user interface. This dissertation presents a real-time robust multimodal unobtrusive user interface comprised of a vision-based robust finger tracking algorithm combined with audio-based control commands, wherein the interface is used to segment out objects of interest in the environment by encircling them with the user's pointing fingertip. In order to quickly extract the objects encircled by the user, this unobtrusive interface uses a single head-mounted camera to capture color images, which are then processed using algorithms to perform: color segmentation, fingertip shape analysis, perturbation model learning, and robust fingertip tracking. Then, a Wearable Computer system may use object recognition algorithms to identify the object segmented by the user's hand gesture, and may return an audio narration, telling the user information concerning the object's classification, historical facts, usage, etc. This interface is designed to be robust to changes in the environment and user's movements by incorporating a state-space estimation with uncertain models algorithm, which attempts to control the influence of uncertain environment conditions on the system's tracking performance by adapting the tracking model to compensate for the uncertainties inherent in the data collected with a Wearable Computer. For a Wearable Computer system, these uncertainties arise from the camera moving along with the user's head motion, the background and object of interest moving independently of each other, the user standing still or randomly walking, and the user's pointing finger abruptly changing directions at variable speeds. The robust unobtrusive multimodal interface developed in this dissertation has been tested on a real Wearable Computer system, and the performance results obtained during these tests are presented in this dissertation.
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Browsing the environment with the SNAP&TELL Wearable Computer system
Personal and Ubiquitous Computing, 2005Co-Authors: Trish Keaton, Sylvia M. Dominguez, Ali H. SayedAbstract:This paper provides an overview of a multi-modal Wearable Computer system, SNAP&TELL. The system performs real-time gesture tracking, combined with audio-based control commands, in order to recognize objects in an environment, including outdoor landmarks. The system uses a single camera to capture images, which are then processed to perform color segmentation, fingertip shape analysis, robust tracking, and invariant object recognition, in order to quickly identify the objects encircled and SNAPped by the user’s pointing gesture. In addition, the system returns an audio narration, TELLing the user information concerning the object’s classification, historical facts, usage, etc. This system provides enabling technology for the design of intelligent assistants to support “Web-On-The-World” applications, with potential uses such as travel assistance, business advertisement, the design of smart living and working spaces, and pervasive wireless services and internet vehicles.
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snap tell a multi modal Wearable Computer interface for browsing the environment
International Symposium on Wearable Computers, 2002Co-Authors: Trish Keaton, Sylvia M. Dominguez, Ali H. SayedAbstract:This paper gives an overview of a multi-modal Wearable Computer system 'SNAP&TELL', which performs real-time gesture tracking combined with audio-based system control commands to recognize objects in the environment including outdoor landmarks. Our system uses a single camera to capture images which are then processed using several algorithms to perform segmentation based on color fingertip shape analysis, robust tracking, and invariant object recognition, in order to quickly identify the objects encircled (SNAPshot) by the user's pointing gesture. In turn, the system returns an audio narration, telling the user information concerning the object's classification, historical facts, usage, etc. This system provides enabling technology for the design of intelligent assistants to support "Web-On-The-World" applications, with potential uses such as travel assistance, business advertisement, the design of smart living and working spaces, and pervasive wireless services and internet vehicles.
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robust finger tracking for Wearable Computer interfacing
Workshop on Perceptive User Interfaces, 2001Co-Authors: Sylvia M. Dominguez, Trish Keaton, Ali H. SayedAbstract:Key to the design of human-machine gesture interface applications is the ability of the machine to quickly and efficiently identify and track the hand movements of its user. In a Wearable Computer system equipped with head-mounted cameras, this task is extremely difficult due to the uncertain camera motion caused by the user's head movement, the user standing still then randomly walking, and the user's hand or pointing finger abruptly changing directions at variable speeds. This paper presents a tracking methodology based on a robust state-space estimation algorithm, which attempts to control the influence of uncertain environment conditions on the system's performance by adapting the tracking model to compensate for the uncertainties inherent in the data. Our system tracks a user's pointing gesture from a single head mounted camera, to allow the user to encircle an object of interest, thereby coarsely segmenting the object. The snapshot of the object is then passed to a recognition engine for identification, and retrieval of any pre-stored information regarding the object. A comparison of our robust tracker against a plain Kalman tracker showed a 15% improvement in the estimated position error, and exhibited a faster response time.
Bruce H Thomas - One of the best experts on this subject based on the ideXlab platform.
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ISWC - Have We Achieved the Ultimate Wearable Computer
2012 16th International Symposium on Wearable Computers, 2012Co-Authors: Bruce H ThomasAbstract:This paper provides a provocative view of Wearable Computer research over the years, starting with the first IEEE International Symposium on Wearable Computers in 1997. The goal of this paper is to reflect on the original research challenges from the first few years. With this goal in mind, two questions can be examined: 1) have we achieved the goals we set out? and 2) how has the direction of research changed in the past fifteen years? This is not a survey paper, but a platform to stimulate discussion.
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evaluation of three Wearable Computer pointing devices for selection tasks
International Symposium on Wearable Computers, 2005Co-Authors: Joanne E Zucco, Bruce H Thomas, Karen GrimmerAbstract:This paper presents the results of an experiment comparing three commercially available pointing devices (a trackball, gyroscopic mouse and Twiddler2 mouse) performing selection tasks for use with Wearable Computers. The study involved 30 participants performing selection tasks with the pointing devices while wearing a Wearable Computer on their back and using a head-mounted display. The error rate and time to complete the selection of the circular targets was measured. When examining the results, the gyroscopic mouse showed the fastest mean time for selecting the targets, while the trackball performed with the lowest error rate.
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tinmith metro new outdoor techniques for creating city models with an augmented reality Wearable Computer
International Symposium on Wearable Computers, 2001Co-Authors: Wayne Piekarski, Bruce H ThomasAbstract:This paper presents new techniques for capturing and viewing on site 3D graphical models for large outdoor objects. Using an augmented reality Wearable Computer, we have developed a software system, known as Tinmith-Metro. Tinmith-Metro allows users to control a 3D constructive solid geometry modeller for building graphical objects of large physical artefacts, for example buildings, in the physical world. The 3D modeller is driven by a new user interface known as Tinmith-Hand, which allows the user to control the modeller using a set of pinch gloves and hand tracking. These techniques allow user to supply their AR renderers with models that would previously have to be captured with manual, time-consuming, and/or expensive methods.
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a Wearable Computer system with augmented reality to support terrestrial navigation
International Symposium on Wearable Computers, 1998Co-Authors: Bruce H Thomas, Victor Demczuk, Wayne Piekarski, D Hepworth, B GuntherAbstract:To date augmented realities are typically operated in only a small defined area, in the order of a large room. This paper reports on our investigation into expanding augmented realities to outdoor environments. The project entails providing visual navigation aids to users. A Wearable Computer system with a see-through display, digital compass, and a differential GPS are used to provide visual cues while performing a standard orienteering task. This paper reports the outcomes of a set of trials using an off the shelf Wearable Computer, equipped with a custom built navigation software package, "map-in-the-hat".
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Evaluation of text input mechanisms for Wearable Computers
Virtual Reality, 1998Co-Authors: Bruce H Thomas, Susan P. Tyerman, Karen GrimmerAbstract:This paper reports on an experiment investigating the functionality and usability of novel input devices on a Wearable Computer for text entry tasks. Over a 3-week period, 12 subjects used three different input devices to create and save short textual messages. The virtual keyboard, forearm keyboard, and Kordic keypad input devices were assessed for their efficiency and usability in simple text-entry tasks. Results collected included the textual data created by the subjects, the duration of activities, the survey data and observations made by supervisors. The results indicated that the forearm keyboard is the best performer for accurate and efficient text entry while other devices may benefit from more work on designing specialist graphical user interfaces (GUIs) for the Wearable Computer.
Joshua Weaver - One of the best experts on this subject based on the ideXlab platform.
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real time american sign language recognition using desk and Wearable Computer based video
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998Co-Authors: Thad Starner, Joshua Weaver, Alex PentlandAbstract:We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American sign language (ASL) using a single camera to track the user's unadorned hands. The first system observes the user from a desk mounted camera and achieves 92 percent word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.
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a Wearable Computer based american sign language recognizer
Lecture Notes in Computer Science, 1998Co-Authors: Thad Starner, Joshua Weaver, Alex PentlandAbstract:Modern Wearable Computer designs package workstation level performance in systems small enough to be worn as clothing. These machines enable technology to be brought where it is needed the most for the handicapped: everyday mobile environments. This paper describes a research effort to make a Wearable Computer that can recognize (with the possible goal of translating) sentence level American Sign Language (ASL) using only a baseball cap mounted camera for input. Current accuracy exceeds 97% per word on a 40 word lexicon.
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ISWC - A Wearable Computer based American sign language recognizer
1997Co-Authors: Thad Starner, Joshua Weaver, Alex PentlandAbstract:Modern Wearable Computer designs package work-station level performance in systems small enough to be worn as clothing. These machines enable technology to be brought where it is needed the most for the handicapped: everyday mobile environments. This paper describes a research effort to make a Wearable Computer that can recognize (with the possible goal of translating) sentence level American Sign Language (ASL) using only a baseball cap mounted camera for input. Current accuracy exceeds 97% per word on a 40 word lexicon.
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A Wearable Computer-based American sign Language Recogniser
Personal Technologies, 1997Co-Authors: Thad Starner, Joshua Weaver, Alex PentlandAbstract:Modern Wearable Computer designs package workstation-level performance in systems small enough to be worn as clothing. These machines enable technology to be brought where it is needed most for the handicapped: everyday mobile environments. This paper describes a research effort to make a Wearable Computer that can recognise (with the possible goal of translating) sentence-level American Sign Language (ASL) using only a baseball cap mounted camera for input. Current accuracy exceeds 97% per word on a 40-word lexicon.
