The Experts below are selected from a list of 4407 Experts worldwide ranked by ideXlab platform
Asim Smailagic - One of the best experts on this subject based on the ideXlab platform.
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Wearable Computers: a new paradigm in computer systems and their applications
IEEE Transactions on Computers, 2003Co-Authors: Asim SmailagicAbstract:THE convergence of a variety of technologies makes possible an entirely new way of using information processing. Continued advances in semiconductor technology produce high performance microprocessors requiring less power and less space. Decades of research in computer science have provided the technology for hands-free computing using speech and gesturing for input. Miniature heads-up displays weighing less than a few ounces have been introduced. Combined with mobile communication technology, it is possible for users to access information anywhere and anytime. Body-worn Computers providing hands-free operation offer compelling advantages in many applications. Wearable Computers deal in information rather than programs, becoming tools in the user’s environment much like a pencil or a reference book. The Wearable computer provides portable access to information. Furthermore, the information can be automatically accumulated by the system as the user interacts with and modifies the environment, thereby eliminating the costly and errorprone process of information acquisition. Much as personal Computers allow accountants and bookkeepers to merge their information space with their workspace (i.e., a sheet of paper), Wearable Computers allow mobile processing and the superposition of information on the users workspace. When combined with pervasive computing, Wearable Computers will provide access to the right information at the right place and at the right time. Distractions are even more of a problem when they occur in mobile environments than desktop environments since the user is often preoccupied with walking, driving, or other real-world interactions. A pervasive computing environment that minimizes distraction has to be context aware. Context-aware computing describes the situation where a mobile computer is aware of its user’s state and surroundings and modifies its behavior based on this information. A user’s context can be quite rich, consisting of attributes such as physical location, physiological state (such as body temperature, heart rate, and skin resistance), emotional state (such as angry, distraught, or calm), personal history, daily behavioral patterns, etc. If a human assistant were given such context, he or she would make decisions in a proactive fashion, anticipating user needs. In making these decisions, the assistant would typically not disturb the user at inopportune moments except in an emergency. The goal is to enable mobile Computers to play an analogous role, exploiting context information to significantly reduce demands on human attention. Combined with inferences about users’ intentions, context-aware computing would allow improvement in user-perceived network and application performance and reliability. Context-aware intelligent agents can deliver relevant information when a user needs that information. These data make possible many exciting new applications, such as augmented reality, context aware collaboration, Wearable assisted living, augmented manufacturing, and maintenance. Wearable computing brings the power of a pervasive computing environment to a person by placing computing and sensory resources on the user in an unobtrusive way. These Computers can be specialized and modular, like items of clothing. Unlike laptops or handheld Computers, Wearable Computers offer many new models to interact beyond keyboards and touch screens, in a natural, intuitive way, such as sound and tactile feedback. Also, Wearables can be easily reconfigured to meet specific needs of applications. Every Wearable computer system must be viewed from three different axes: the human, the computer, and the application. Within each of these axes there are difficult problems that must be solved and there are problems that arise from the fact that there are three axes. The human axis emphasizes wearability, which is defined as the interaction between the human body and the Wearable object. Dynamic wearability includes the human body in motion. Design for wearability considers the physical shape of objects and their active relationship with the human form. Researchers explored history and cultures, including topics such as clothing, costumes, protective Wearables, and carried devices. These studies of physiology, biomechanics, and movement were codified into guidelines for designing Wearable systems. User comfort is a critical design consideration in many applications. New technologies such as smart textiles will significantly improve the functionality and ergonomics of Wearable Computers. The computer axis deals with the problems related to construction of a system with particular fabrics, size, power consumption, and user interface software. The application axis emphasizes mobile application design challenges and efficient mapping of problem solving capabilities to application requirements. Wearable Computers have established their first foothold in several application domains, such as vehicle and aircraft maintenance and manufacturing, inspection procedures, augmented reality, context aware collaboration, language translation, etc. IEEE TRANSACTIONS ON Computers, VOL. 52, NO. 8, AUGUST 2003 977
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cmu Wearable Computers for real time speech translation
International Symposium on Wearable Computers, 1999Co-Authors: Asim Smailagic, Daniel P. Siewiorek, R Martin, D ReillyAbstract:Carnegie Mellon's Wearable Computers Laboratory has built four generations of real time speech translation Wearable Computers, culminating in the Speech Translator Smart Module. Smart Modules are a family of interoperable modules supporting real time speech recognition, language translation, and speech synthesis. We examine the effect of various design factors on performance with emphasis on modularity and scalability. A system level approach to power/performance optimization is described that improved the metric of (performance/(weight*volume*power)) by over a factor of 300 through the four generations.
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ISWC - CMU Wearable Computers for real-time speech translation
1999Co-Authors: Asim Smailagic, Daniel P. Siewiorek, R Martin, D ReillyAbstract:Carnegie Mellon's Wearable Computers Laboratory has built four generations of real time speech translation Wearable Computers, culminating in the Speech Translator Smart Module. Smart Modules are a family of interoperable modules supporting real time speech recognition, language translation, and speech synthesis. We examine the effect of various design factors on performance with emphasis on modularity and scalability. A system level approach to power/performance optimization is described that improved the metric of (performance/(weight*volume*power)) by over a factor of 300 through the four generations.
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An evaluation of audio-centric CMU Wearable Computers
Mobile Networks and Applications, 1999Co-Authors: Asim SmailagicAbstract:Carnegie Mellon's Wearable Computers project is defining the future for not only computing technologies but also for the use of Computers in daily activities. Fifteen generations of CMU's Wearable Computers are evolutionary steps in the quest for new ways to improve and augment the integration of information in the mobile environment. The complexity of their architectures has increased by a factor of over 200, and the complexity of the applications has also increased significantly. In this paper, we provide a taxonomy of their capabilities and evaluate the performance of audio-centric CMU Wearable Computers.
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System level design as applied to CMU Wearable Computers
The Journal of VLSI Signal Processing, 1999Co-Authors: Asim Smailagic, Daniel P. SiewiorekAbstract:The paper describes a system level design approach to the Wearable Computers project at the Carnegie Mellon University (CMU). The project is an unique example of a cross-disciplinary project, drawing students from mechanical engineering, electrical and computer engineering, computer science, and industrial design. The students learn about design theory and practice, participate in research, create and deliver Wearable computer products to sponsors. Over the last five and half years that the course has been taught teams of undergraduate and graduate students have designed and fabricated fourteen new generations of Wearable Computers, using an evolving artifact-specific, multidisciplinary design methodology. Between the first and last generation, the electronic functionality has increased by a factor of three, the number of mechanical features has increased by a factor of 10, and the software complexity has increased by a factor of 25 while the total design effort measured in hours has increased by less than a factor of two.
Daniel P. Siewiorek - One of the best experts on this subject based on the ideXlab platform.
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cmu Wearable Computers for real time speech translation
International Symposium on Wearable Computers, 1999Co-Authors: Asim Smailagic, Daniel P. Siewiorek, R Martin, D ReillyAbstract:Carnegie Mellon's Wearable Computers Laboratory has built four generations of real time speech translation Wearable Computers, culminating in the Speech Translator Smart Module. Smart Modules are a family of interoperable modules supporting real time speech recognition, language translation, and speech synthesis. We examine the effect of various design factors on performance with emphasis on modularity and scalability. A system level approach to power/performance optimization is described that improved the metric of (performance/(weight*volume*power)) by over a factor of 300 through the four generations.
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ISWC - CMU Wearable Computers for real-time speech translation
1999Co-Authors: Asim Smailagic, Daniel P. Siewiorek, R Martin, D ReillyAbstract:Carnegie Mellon's Wearable Computers Laboratory has built four generations of real time speech translation Wearable Computers, culminating in the Speech Translator Smart Module. Smart Modules are a family of interoperable modules supporting real time speech recognition, language translation, and speech synthesis. We examine the effect of various design factors on performance with emphasis on modularity and scalability. A system level approach to power/performance optimization is described that improved the metric of (performance/(weight*volume*power)) by over a factor of 300 through the four generations.
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System level design as applied to CMU Wearable Computers
The Journal of VLSI Signal Processing, 1999Co-Authors: Asim Smailagic, Daniel P. SiewiorekAbstract:The paper describes a system level design approach to the Wearable Computers project at the Carnegie Mellon University (CMU). The project is an unique example of a cross-disciplinary project, drawing students from mechanical engineering, electrical and computer engineering, computer science, and industrial design. The students learn about design theory and practice, participate in research, create and deliver Wearable computer products to sponsors. Over the last five and half years that the course has been taught teams of undergraduate and graduate students have designed and fabricated fourteen new generations of Wearable Computers, using an evolving artifact-specific, multidisciplinary design methodology. Between the first and last generation, the electronic functionality has increased by a factor of three, the number of mechanical features has increased by a factor of 10, and the software complexity has increased by a factor of 25 while the total design effort measured in hours has increased by less than a factor of two.
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very rapid prototyping of Wearable Computers a case study of vuman 3 custom versus off the shelf design methodologies
Design Automation for Embedded Systems, 1998Co-Authors: Asim Smailagic, Daniel P. Siewiorek, R Martin, John M StivoricAbstract:The Wearable Computer Project is a testbed integrating research on rapid design and prototyping. Based on representative examples from six generations of Wearable Computers, the paper focuses on the differences in rapid prototyping using custom design versus off-the-shelf components. The attributes characterizing these two design styles are defined and illustrated by experimental measurements. The off-the-shelf approach required ten times the overhead, 30% more cost, fifty times the storage resources, 20% more effort, five times more power, but 30% less effort to port software than the embedded approach. An evaluation of the VuMan 3 design is presented to show its superior advantages in comparison to the off-the-shelf approach.
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CHI Extended Abstracts - Research issues in Wearable Computers
CHI '97 extended abstracts on Human factors in computing systems looking to the future - CHI '97, 1997Co-Authors: Len Bass, Daniel P. Siewiorek, Steve Mann, Christopher H. ThompsonAbstract:Wearable Computers are becoming more common. A recent workshop on Wearable computing in Seattle attracted more than 200 attendees. For the most part, however, Wearable Computers are being treated as small Computers with attempts to provide the same range of input and output devices as on a desktop and to utilize the same applications. We believe, however, that Wearable computing is a new paradigm introducing new issues. It is not just mobile desktop computing. The purpose of this workshop is to discuss this position and to identify those research issues that are specific to Wearable Computers. The goal of the workshop is to produce a white paper identifying research issues in Wearable computing and we expect this white paper to be seminal in the oncoming era of Wearable computing.
Roozbeh Jafari - One of the best experts on this subject based on the ideXlab platform.
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Transfer learning for Wearable Computers
Wearable Sensors, 2021Co-Authors: Ali Akbari, Parastoo Alinia, Hassan Ghasemzadeh, Roozbeh JafariAbstract:Abstract Wearable sensors are taking a bold stance in becoming the principal component of monitoring systems with wide applications in health monitoring, assisted living, sport studies, entertainment, and diet monitoring. Various Wearable sensors with different sensing capabilities including smartwatches, smartphones, wrist-band sensors, sports shoes, and sensors embedded in clothing have been used for the aforementioned applications. However, in the presence of the user's diverse preference and requirement of various environments, changes in the configuration and type of sensors are highly possible. For example, a user who has been using a smartphone for a while may acquire a new smartwatch. Besides changes in the sensor modalities and configurations, the changes in the user characteristics are highly possible. A system trained on the data of specific users should be able to work for a new user with different characteristics such as different body mass index, skin tone, physiological attributes, and even sensor placement preferences. As a result of such changes in the environment, the machine learning and signal processing components should be updated or retrained. Otherwise, the performance of the models designed on the old training data will be significantly degraded when those models are used with the data collected under a new configuration. The main challenge with retraining the underlying signal processing and machine learning models is that it requires collecting a sufficiently large amount of labeled training data. The process of collecting such data is very challenging and overwhelming if the users are asked to provide them. Therefore, it is of paramount interest to transfer the knowledge from an old domain to a new domain, with minimum effort required by the users. In dynamic environments, where the configuration of the Wearable systems can constantly change over time, the notion of transfer learning for these devices becomes tremendously important.
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inertial measurement unit based Wearable Computers for assisted living applications a signal processing perspective
IEEE Signal Processing Magazine, 2016Co-Authors: Terrell R Bennett, Nasser Kehtarnavaz, Roozbeh JafariAbstract:There has been a very rapid growth in Wearable Computers over the past few years. Assisted living applications leveraging Wearable Computers will enable a healthier lifestyle and independence in a variety of target populations, including those suffering from neurological disorders, patients in need of rehabilitation after surgical procedures or injury, the elderly, individuals who might be at high risk of emotional stress, and those who are looking for a healthier lifestyle. Application paradigms for assisted living include activities of daily living (ADLs) monitoring, indoor localization, emergency and fall detection, and rehabilitation. All of these applications require monitoring of movements and physical activities for individuals. Wearable inertial measurement unit (IMU)-based sensors can offer low-cost and ubiquitous monitoring solutions for physical activities. Signal processing techniques with a focus on enhancing accuracy, lowering computational complexity, reducing power consumption, and improving the unobtrusiveness of the Wearable Computers are of interest in this article, which constitutes the first attempt made at reviewing the literature of Wearable IMU-based signal processing techniques for assisted living applications. Various signal processing techniques with the aforementioned performance metrics in mind are reviewed here.
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a case study on minimum energy operation for dynamic time warping signal processing in Wearable Computers
International Conference on Pervasive Computing, 2015Co-Authors: Javad Birjandtalab, Qingxue Zhang, Roozbeh JafariAbstract:Miniaturization and form factor reduction in Wearable Computers leads to enhanced wearability. Power optimization typically translates to form factor reduction, hence of paramount importance. This paper demonstrates power consumption analysis obtained for various operating modes in circuits suitable for Wearable Computers which are typically equipped with sensors that provide time series data (e.g., acceleration, ECG). Dynamic time warping (DTW) is considered a suitable signal processing technique for Wearable Computers, particularly due to its lower computational complexity requirement and the robustness to speed variations (acceleration and de-acceleration) in time series data. Wearable Computers usually have very low computational performance requirements, which is explored in this work to minimize the system level energy consumption. We provide a comparison among three modes of operations, namely minimum energy operating point (MEOP), minimum voltage operation point (MVOP) and nominal voltage operating point (NVOP) all leveraging sleep transistors when circuits are inactive. The results show that the MVOP, in conjunction with sleep transistors, provides the least energy budget and leads to a reduction in energy consumption compared to the MEO, which is known as a suitable operating mode for ultra-low power circuits.
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an ultra low power hardware accelerator architecture for Wearable Computers using dynamic time warping
Design Automation and Test in Europe, 2013Co-Authors: Reza Lotfian, Roozbeh JafariAbstract:Movement monitoring using Wearable Computers has been widely used in healthcare and wellness applications. To reduce the form factor of Wearable nodes which is dominated by battery size, ultra-low power signal processing is crucial. In this paper, we propose an architecture that can be viewed as a hardware accelerator and employs dynamic time warping (DTW) in a hierarchical fashion. The proposed architecture removes events that are not of interest from the signal processing chain as early as possible, deactivating all remaining modules. We consider tunable parameters such as sampling frequency and bit resolution of the incoming sensor readings for DTW to balance the power consumption and classification precision trade-off. We formulate a methodology for determining the optimal set of tunable parameters and provide a solution using Active-set algorithm. We synthesized the architecture using 45nm CMOS and illustrated that a three-tiered module achieves 98% accuracy with a power budget of 1.23μW, while a single level DTW consumes 6.3μW with the same accuracy. We furthermore propose a fast approximation methodology that runs 3200 times faster while introducing less than 3% error over the original optimization for determining the total power consumption.
Mark Billinghurst - One of the best experts on this subject based on the ideXlab platform.
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social panoramas using Wearable Computers to share experiences
International Conference on Computer Graphics and Interactive Techniques, 2014Co-Authors: Mark Billinghurst, Alaeddin Nassani, Carolin ReichherzerAbstract:Camera equipped mobile devices provide a quick way of capturing and sharing social experiences and spaces. Wearable Computers that combine head mounted displays and cameras provide new opportunities for collaboration. We are interested in how Wearable computer users could rapidly capture and share their surroundings using panorama imagery and live video.
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poster social panoramas using Wearable Computers
International Symposium on Mixed and Augmented Reality, 2014Co-Authors: Carolin Reichherzer, Alaeddin Nassani, Mark BillinghurstAbstract:In this paper we describe the concept of Social Panoramas that combine panorama images, Mixed Reality, and Wearable Computers to support remote collaboration. We have developed a prototype that allows panorama images to be explored in real time between a Google Glass user and a remote tablet user. This uses a variety of cues for supporting awareness, and enabling pointing and drawing. We conducted a study to explore if these cues can increase Social Presence. The results suggest that increased interaction does not increase Social Presence, but tools with a higher perceived usability show an improved sense of presence.
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Spatial information displays on a Wearable computer
IEEE Computer Graphics and Applications, 1998Co-Authors: Mark Billinghurst, Jerry Bowskill, N. Dyer, J. MorphettAbstract:Increasing portability of computing power marks a broad emerging trend in advanced human-computer interaction. Wearable Computers, the next generation of portable machines, worn on the body, provide constant access to computing and communications resources. However, wide adoption of Wearable Computers first requires solving unique interface challenges. One of the most important is how to present and interact with information in a Wearable environment. We designed and evaluated three methods for presenting information in a Wearable environment. Subjects found spatial displays easier and more effective. Spatial audio and visual cues further enhanced performance.
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Wearable Computers for three dimensional cscw
International Symposium on Wearable Computers, 1997Co-Authors: Mark Billinghurst, Suzanne Weghorst, Thomas A FurnessAbstract:Using established principles from the field of Computer Supported Collaborative Work (CSCW), we describe how Wearable Computers are ideal platforms for three dimensional CSCW. To illustrate this, we present two pilot studies which imply that Wearables may be able to support three dimensional collaboration and that users will perform better with these interfaces than immersive collaborative environments.
D Reilly - One of the best experts on this subject based on the ideXlab platform.
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cmu Wearable Computers for real time speech translation
International Symposium on Wearable Computers, 1999Co-Authors: Asim Smailagic, Daniel P. Siewiorek, R Martin, D ReillyAbstract:Carnegie Mellon's Wearable Computers Laboratory has built four generations of real time speech translation Wearable Computers, culminating in the Speech Translator Smart Module. Smart Modules are a family of interoperable modules supporting real time speech recognition, language translation, and speech synthesis. We examine the effect of various design factors on performance with emphasis on modularity and scalability. A system level approach to power/performance optimization is described that improved the metric of (performance/(weight*volume*power)) by over a factor of 300 through the four generations.
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ISWC - CMU Wearable Computers for real-time speech translation
1999Co-Authors: Asim Smailagic, Daniel P. Siewiorek, R Martin, D ReillyAbstract:Carnegie Mellon's Wearable Computers Laboratory has built four generations of real time speech translation Wearable Computers, culminating in the Speech Translator Smart Module. Smart Modules are a family of interoperable modules supporting real time speech recognition, language translation, and speech synthesis. We examine the effect of various design factors on performance with emphasis on modularity and scalability. A system level approach to power/performance optimization is described that improved the metric of (performance/(weight*volume*power)) by over a factor of 300 through the four generations.
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A system-level approach to power/performance optimization in Wearable Computers
Proceedings IEEE Computer Society Workshop on VLSI 2000. System Design for a System-on-Chip Era, 1Co-Authors: Asim Smailagic, D Reilly, Daniel P. SiewiorekAbstract:The paper describes a system-level design approach to the power and performance of Carnegie Mellon's Wearable Computers dedicated to speech processing-the Speech Translator Smart Modules. While processor speed and type affect power consumption and performance, memory size and type of secondary storage have a significant influence as well. A system-level approach to power/performance optimization is described that improved the metric of (performance/(weight*volume*power)) by over a factor of 300 through the four generations of Wearable Computers for speech processing.
