The Experts below are selected from a list of 3099 Experts worldwide ranked by ideXlab platform
Shuoyu Wang - One of the best experts on this subject based on the ideXlab platform.
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Knowledge Acquisition Method Based on Singular Value Decomposition for Human Motion Analysis
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu WangAbstract:The Knowledge remembered by the human body and reflected by the dexterity of body motion is called Embodied Knowledge. In this paper, we propose a new method using singular value decomposition for extracting Embodied Knowledge from the time-series data of the motion. We compose a matrix from the time-series data and use the left singular vectors of the matrix as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. Two experiments were conducted to validate the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with indexes of similarity and estimation that use left singular vectors. The proposed method obtained a higher correct categorization ratio than principal component analysis (PCA) and correlation efficiency (CE). The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability. The first singular values derived from the walking acceleration were suggested to be a reliable criterion to evaluate walking disability. Finally we discuss the characteristic and significance of the Embodied Knowledge extraction using the singular value decomposition proposed in this paper.
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Embodied Knowledge extraction from human motion using singular value decomposition
2012 IEEE International Conference on Fuzzy Systems, 2012Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu WangAbstract:Embodied Knowledge is the Knowledge remembered by the human body and reflected by the dexterity in the motion of the body. In this paper, we propose a new method using singular value decomposition for extracting Embodied Knowledge from the time-series data of the motion which is measured with various sensors such as an accelerometer, a motion capture system and a force sensor. We compose a matrix from the the time-series data and use the left singular vectors of the matrix as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. Two experiments were conducted to testify the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with the indexes of similarity and estimation using left singular vectors. The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability using a 3D hyperplane constructed by the singular values. Finally we discuss the characteristic and significance of the Embodied Knowledge extraction using singular value decomposition proposed in this paper.
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FUZZ-IEEE - Embodied Knowledge extraction from human motion using singular value decomposition
2012 IEEE International Conference on Fuzzy Systems, 2012Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu WangAbstract:Embodied Knowledge is the Knowledge remembered by the human body and reflected by the dexterity in the motion of the body. In this paper, we propose a new method using singular value decomposition for extracting Embodied Knowledge from the time-series data of the motion which is measured with various sensors such as an accelerometer, a motion capture system and a force sensor. We compose a matrix from the the time-series data and use the left singular vectors of the matrix as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. Two experiments were conducted to testify the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with the indexes of similarity and estimation using left singular vectors. The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability using a 3D hyperplane constructed by the singular values. Finally we discuss the characteristic and significance of the Embodied Knowledge extraction using singular value decomposition proposed in this paper.
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Acquisition of Embodied Knowledge on Gesture Motion by Singular Value Decomposition
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2011Co-Authors: Isao Hayashi, Yinlai Jiang, Shuoyu WangAbstract:Communication is classified in terms of verbal and nonverbal information. We discuss an acquisition method of Knowledge from nonverbal information. In particular, a gesture is an efficient form of nonverbal communication as well as in verbal ways, and we formulate here a method that measures similarity and estimation between gestures. A gesture includes human Embodied Knowledge, and therefore the visible bodily actions can communicate particular messages. However, we have infinite patterns for gesture, determined by personality. Recently, the singular spectrum analysis method is utilized as an attractive method. In this paper, we propose a new method for acquiring Embodied Knowledge from time-series data on gestures using singular value decomposition. The motion behavior is categorized into several clusters with similarity and estimation between interval time-series data. We discuss the usefulness of the proposed method using an example of gesture motion.
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Embodied Knowledge of Gesture Motion Acquired by Singular Spectrum Analysis
Vulnerability Uncertainty and Risk, 2011Co-Authors: Isao Hayashi, Yong Jiang, Shuoyu WangAbstract:Whenever a disaster occurs, it's of utmost importance that the rescue system recognizes accurately human behavior and evacuation command in the fire and its black smoke. However, we have infinite pattern for movement instructions by our personality. On the other hand, the singular spectrum analysis method has proposed as analytical method for time-series data. In this paper, we propose a method for acquiring Embodied Knowledge of human behavior from time-series gesture data using singular spectrum analysis. A behavior is distinguished in terms of gesture characteristic with similarity criteria by interval time-series data. We discuss the usefulness of the proposed method using an example of gesture motion.
Carla Costa - One of the best experts on this subject based on the ideXlab platform.
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drivers of spin off performance in industry clusters Embodied Knowledge or embedded firms
Research Policy, 2018Co-Authors: Guido Buenstorf, Carla CostaAbstract:Numerous studies attest to the distinctive performance of intra-industry spin-offs located in agglomerated regions. Besides entrepreneurs’ pre-entry experience, both superior hires and regional embeddedness have been suggested as factors contributing to this pattern. We employ linked employer-employee data to assess their relevance in the empirical context of the Portuguese plastic injection molds industry. We find that the longevity of entrants is associated with the number and quality of early employees hired from within the industry, consistent with the importance of Embodied Knowledge flows. Our findings do not suggest that entrants’ centrality in the regional industry network enhances their longevity.
Guido Buenstorf - One of the best experts on this subject based on the ideXlab platform.
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drivers of spin off performance in industry clusters Embodied Knowledge or embedded firms
Research Policy, 2018Co-Authors: Guido Buenstorf, Carla CostaAbstract:Numerous studies attest to the distinctive performance of intra-industry spin-offs located in agglomerated regions. Besides entrepreneurs’ pre-entry experience, both superior hires and regional embeddedness have been suggested as factors contributing to this pattern. We employ linked employer-employee data to assess their relevance in the empirical context of the Portuguese plastic injection molds industry. We find that the longevity of entrants is associated with the number and quality of early employees hired from within the industry, consistent with the importance of Embodied Knowledge flows. Our findings do not suggest that entrants’ centrality in the regional industry network enhances their longevity.
Shuichi Fukuda - One of the best experts on this subject based on the ideXlab platform.
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Acquiring Embodied Knowledge Through Practice: A Wisdom Engineering Approach
Volume 11: Systems Design and Complexity, 2016Co-Authors: Shuichi FukudaAbstract:Although remarkable progress has been made in the field of explicit Knowledge, research about tacit Knowledge is still very few. This paper takes up Embodied Knowledge such as bicycle riding, as one kind of tacit Knowledge. As Embodied Knowledge cannot be articulated and verbalized, it has to be transferred to another person through practice. But how we can acquire Embodied Knowledge more effectively through practice is still the question at issue. Indeed, there are works to help a learner to acquire Embodied Knowledge by showing the videos or through OJT. But since features or control points are not explicit, it is very difficult for a learner to acquire a good sense for judgments and for decisions to cope with the changing situations. Although there are many approaches to multivariate analysis, there are very few approaches which provide a holistic perspective. In this sense, pattern-based approach is better than other approaches. This paper points out that pattern-based Recognition Taguchi (RT) approach in Mahalanobis Taguchi System (MTS) is expected to be a very promising and versatile tool to help a learner acquire Embodied Knowledge because it allows us to take the differences of body behavior from person to person in addition to providing the holistic perspective.
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Human Action Modeling and Application to a Control System Using the Mahalanobis-Taguchi System
Volume 1A: 36th Computers and Information in Engineering Conference, 2016Co-Authors: Yusuke Asaka, Shuichi Fukuda, Keiichi Watanuki, Keiichi Muramatsu, Kazunori KaedeAbstract:This paper discusses human action modeling and its application to a control system that uses the Mahalanobis – Taguchi System (MTS). In this study, we define Embodied Knowledge as being included in tacit Knowledge. We also define a set of skills based on experiences and intuitive sense as seen in creating an art, sport, craft, or other skilled task. Embodied Knowledge is difficult to express explicitly. As our goals, we analyze Embodied Knowledge acquisition for human action modeling and apply to a control system by using MTS. An analysis of Embodied Knowledge using devices and pattern-recognition techniques to recognize un-explicit Knowledge are being developed owing to recent improvements in technology. Embodied Knowledge acquisition element of recognition can be represented as a pattern recognition technique. In this paper, we confirm that MTS is an adaptive method for recognizing pattern in human action modeling. We set up a control model including a controller using the MTS, which is modeled after an internal model of the cerebellum. We apply the controller based on the Recognition Taguchi (RT) method to invert the control of the pendulum. The result indicate that the controller is capable of detecting disturbance.
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somatic Embodied Knowledge representation a challenge
ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2012Co-Authors: Shuichi FukudaAbstract:Recent brain studies revealed brain and body cannot be separated. Further it revealed blood and muscles play an important role in our information processing. Bike riding is known as a typical example of tacit Knowledge. Although there are efforts on how we can change such tacit or somatic/Embodied Knowledge of ours as this example into explicit one, we have been not so successful. From our past two series of experiments about detection of emotion from face and about calligraphy, we learned acceleration plays a crucial role. This paper attempts to represent somatic/Knowledge representation as patterns of position and acceleration. This is still a preliminary study but it may lead us to another way of representing our tacit Knowledge and thus we may develop another way of transferring tacit Knowledge such as skills, bike riding, etc in the form of patterns of position and acceleration. Mechanical engineering is a tangible engineering. Therefore the author would like to emphasize the importance of exploring how we can represent our somatic/Embodied Knowledge. This is a very much preliminary step toward that goal.Copyright © 2012 by ASME
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Somatic/Embodied Knowledge Representation: A Challenge
Volume 2: 32nd Computers and Information in Engineering Conference Parts A and B, 2012Co-Authors: Shuichi FukudaAbstract:Recent brain studies revealed brain and body cannot be separated. Further it revealed blood and muscles play an important role in our information processing. Bike riding is known as a typical example of tacit Knowledge. Although there are efforts on how we can change such tacit or somatic/Embodied Knowledge of ours as this example into explicit one, we have been not so successful. From our past two series of experiments about detection of emotion from face and about calligraphy, we learned acceleration plays a crucial role. This paper attempts to represent somatic/Knowledge representation as patterns of position and acceleration. This is still a preliminary study but it may lead us to another way of representing our tacit Knowledge and thus we may develop another way of transferring tacit Knowledge such as skills, bike riding, etc in the form of patterns of position and acceleration. Mechanical engineering is a tangible engineering. Therefore the author would like to emphasize the importance of exploring how we can represent our somatic/Embodied Knowledge. This is a very much preliminary step toward that goal.Copyright © 2012 by ASME
Isao Hayashi - One of the best experts on this subject based on the ideXlab platform.
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Knowledge Acquisition Method Based on Singular Value Decomposition for Human Motion Analysis
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu WangAbstract:The Knowledge remembered by the human body and reflected by the dexterity of body motion is called Embodied Knowledge. In this paper, we propose a new method using singular value decomposition for extracting Embodied Knowledge from the time-series data of the motion. We compose a matrix from the time-series data and use the left singular vectors of the matrix as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. Two experiments were conducted to validate the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with indexes of similarity and estimation that use left singular vectors. The proposed method obtained a higher correct categorization ratio than principal component analysis (PCA) and correlation efficiency (CE). The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability. The first singular values derived from the walking acceleration were suggested to be a reliable criterion to evaluate walking disability. Finally we discuss the characteristic and significance of the Embodied Knowledge extraction using the singular value decomposition proposed in this paper.
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Embodied Knowledge extraction from human motion using singular value decomposition
2012 IEEE International Conference on Fuzzy Systems, 2012Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu WangAbstract:Embodied Knowledge is the Knowledge remembered by the human body and reflected by the dexterity in the motion of the body. In this paper, we propose a new method using singular value decomposition for extracting Embodied Knowledge from the time-series data of the motion which is measured with various sensors such as an accelerometer, a motion capture system and a force sensor. We compose a matrix from the the time-series data and use the left singular vectors of the matrix as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. Two experiments were conducted to testify the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with the indexes of similarity and estimation using left singular vectors. The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability using a 3D hyperplane constructed by the singular values. Finally we discuss the characteristic and significance of the Embodied Knowledge extraction using singular value decomposition proposed in this paper.
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FUZZ-IEEE - Embodied Knowledge extraction from human motion using singular value decomposition
2012 IEEE International Conference on Fuzzy Systems, 2012Co-Authors: Yinlai Jiang, Isao Hayashi, Shuoyu WangAbstract:Embodied Knowledge is the Knowledge remembered by the human body and reflected by the dexterity in the motion of the body. In this paper, we propose a new method using singular value decomposition for extracting Embodied Knowledge from the time-series data of the motion which is measured with various sensors such as an accelerometer, a motion capture system and a force sensor. We compose a matrix from the the time-series data and use the left singular vectors of the matrix as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. Two experiments were conducted to testify the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with the indexes of similarity and estimation using left singular vectors. The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability using a 3D hyperplane constructed by the singular values. Finally we discuss the characteristic and significance of the Embodied Knowledge extraction using singular value decomposition proposed in this paper.
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Acquisition of Embodied Knowledge on Gesture Motion by Singular Value Decomposition
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2011Co-Authors: Isao Hayashi, Yinlai Jiang, Shuoyu WangAbstract:Communication is classified in terms of verbal and nonverbal information. We discuss an acquisition method of Knowledge from nonverbal information. In particular, a gesture is an efficient form of nonverbal communication as well as in verbal ways, and we formulate here a method that measures similarity and estimation between gestures. A gesture includes human Embodied Knowledge, and therefore the visible bodily actions can communicate particular messages. However, we have infinite patterns for gesture, determined by personality. Recently, the singular spectrum analysis method is utilized as an attractive method. In this paper, we propose a new method for acquiring Embodied Knowledge from time-series data on gestures using singular value decomposition. The motion behavior is categorized into several clusters with similarity and estimation between interval time-series data. We discuss the usefulness of the proposed method using an example of gesture motion.
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Special Issue on Cross-Disciplinary Approaches to Embodied Knowledge of Human Skill
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2011Co-Authors: Isao Hayashi, Shinichi FuruyaAbstract:Expertise in sports, music, dance, and craftsmanship is increasingly attracting researchers from many different backgrounds who seek to deepen their understanding of outstanding human skills - a field known as skill science. The goal of skill science is to elucidate neural, cognitive, and computational mechanisms and processes underlying superior sensorimotor functions. To this aim, cross-disciplinary approaches needed include artificial intelligence, computational intelligence, soft computing, robotics, biomechanics, cognitive science, and neuroscience. This special issue includes a variety of paper focusing on new computational approaces and cutting-edge empirical techniques shedding light on Embodied Knowledge. Analytical techniques include factorial analysis, such as Principal Component Analysis (PCA) and Singular Vector Decomposition (SVD), correlation networks, machine learning such as cluster analysis, Bayesian statistics, and nonlinear dynamical modeling. Experimental paradigms and techniques include Virtual Reality (VR) environment, comparison between skilled and unskilled individuals and between individuals with and without neurological disorders, and biomechanical and physiological measurement using motion capture, ElectroMyoGraphy (EMG), functional Magnetic Resonance Imaging (fMRI), Transcranial Magnetic Stimulation (TMS), and Auditory Brainstem Response (ABR). These approaches and techniques have successfully addressed key features of sensorimotor mechanisms behind skilled human behavior. Unique approaches in terms of abduction reasoning and observation learning of robots have quantitatively and qualitatively unraveled cognitive processes in novel skill acquisition. Findings from these studies provide intriguing insights into developing comprehensive models of Embodied Knowledge and into practical applications Quantitative evaluation and precise modeling of human skills are, for example, indispensable for developing hardware and software that mimic human functions and for designing robots and Brain-Machine Interfaces (BMI) that enables dexterous human-like behavior. It is of academic and clinical importance to determine mechanisms for acquiring complex sensorimotor skills. These diverse approaches toward a unique goal are expected to build bridges among researchers with vastly different backgrounds, serving as an impetus for boosting this cross-disciplinary research area. We believe this special issue will serve as a landmark for further developing skill science research.