The Experts below are selected from a list of 16860 Experts worldwide ranked by ideXlab platform
A C Tsai - One of the best experts on this subject based on the ideXlab platform.
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a novel stft ranking feature of multi channel emg for Motion Pattern recognition
Expert Systems With Applications, 2015Co-Authors: A C TsaiAbstract:STFT-ranking feature is efficient for multi-channel EMG signal analysis.STFT-ranking feature can characterize relationships between multi-channel signals.Recognition accuracy over 90% was achieved applying the STFT-ranking feature.The performance of STFT-ranking feature is superior to conventional features.STFT-ranking feature can be applied to other multi-channel signals applications. Electromyography (EMG) is widely applied for neural engineering. For Motion Pattern recognition, many features of multi-channel EMG signals were investigated, but the relationships between muscles were not considered. In this study, a novel STFT-ranking feature based on short-time Fourier transform (STFT) is proposed. The novelty of STFT-ranking features is considering and covering the relationship information between EMG signals and multiple muscles in a Motion Pattern. With an exoskeleton robot arm, two series of Motion Patterns corresponding to the shoulder and elbow in the sagittal plane were investigated. EMG signals from six muscles were acquired in arm Motion Patterns when participants worn the robot arm. Four types of feature combinations, including seven conventional features, were compared with the STFT-ranking feature. The principal component analysis (PCA) and support vector machine (SVM) were used to build the Motion recognition model. With the STFT-ranking feature, the recognition performance (93.9%) is superior to the conventional features (33.3-90.8%). The recognition variation is smaller (SD=4.3%) than the other features tested (SD=5.9-13.8%). These achievements will contribute to the advancement of control method of exoskeleton robots or power orthoses based on multi-channel EMG signals in the future. Based on the principle of STFT-ranking feature, the method also has potential for other multi-channel signal applications, such as electroencephalography (EEG) signal processing, speech recognition, and acoustic analysis.
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A novel STFT-ranking feature of multi-channel EMG for Motion Pattern recognition
Expert Systems with Applications, 2015Co-Authors: A C Tsai, Jer Junn Luh, T. T. LinAbstract:Electromyography (EMG) is widely applied for neural engineering. For Motion Pattern recognition, many features of multi-channel EMG signals were investigated, but the relationships between muscles were not considered. In this study, a novel STFT-ranking feature based on short-time Fourier transform (STFT) is proposed. The novelty of STFT-ranking features is considering and covering the relationship information between EMG signals and multiple muscles in a Motion Pattern. With an exoskeleton robot arm, two series of Motion Patterns corresponding to the shoulder and elbow in the sagittal plane were investigated. EMG signals from six muscles were acquired in arm Motion Patterns when participants worn the robot arm. Four types of feature combinations, including seven conventional features, were compared with the STFT-ranking feature. The principal component analysis (PCA) and support vector machine (SVM) were used to build the Motion recognition model. With the STFT-ranking feature, the recognition performance (93.9%) is superior to the conventional features (33.3-90.8%). The recognition variation is smaller (SD = 4.3%) than the other features tested (SD = 5.9-13.8%). These achievements will contribute to the advancement of control method of exoskeleton robots or power orthoses based on multi-channel EMG signals in the future. Based on the principle of STFT-ranking feature, the method also has potential for other multi-channel signal applications, such as electroencephalography (EEG) signal processing, speech recognition, and acoustic analysis.
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A comparison of upper-limb Motion Pattern recognition using EMG signals during dynamic and isometric muscle contractions
Biomedical Signal Processing and Control, 2014Co-Authors: A C Tsai, Jer Junn Luh, T H Hsieh, T. T. LinAbstract:Multichannel electromyography (EMG) signals are one of the common methods used in human Motion Pattern recognition. In exoskeleton robot control, EMG signals are measured during dynamic or isometric muscle contractions. Various types of contraction can cause EMG signals to vary, affecting recognition performance. A Motion Pattern recognition model using EMG signals from either dynamic or isometric muscle contractions has not yet been fully investigated. In this study, a novel feature extraction method, using the short-time Fourier transform ranking (STFT-ranking) feature, was employed to determine multichannel EMG signals. The performance of the novel feature and conventional features for Motion Pattern recognition using EMG signals, which included time-domain and frequency-domain features, was compared during dynamic and isometric muscle contractions. Experiments were conducted using an exoskeleton robotic arm to aid users in generating EMG signals of designated Motion Patterns. Among the features tested, the STFT-ranking feature yielded an accuracy rate exceeding 90% when the EMG signals used in the training and validation feature data sets were of the same type of muscle contraction. After examining the STFT-ranking feature projected onto the PCA space, the STFT-ranking feature was determined to offer more satisfactory performance than the other features tested for Motion Pattern recognition, because the feature data it collected from various Motion Patterns were more separable. The experimental results also revealed that it is preferable that EMG signals from the same type of muscle contraction, whether dynamic or isometric, are consistently used in both the training and validation (control) phases. Inconsistent EMG signals in the training and validation phases yielded a negative effect on Motion Pattern recognition performance. The methodology developed in this study has potential applications in exoskeleton robot control and rehabilitation.
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A modified multi-channel EMG feature for upper limb Motion Pattern recognition
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012Co-Authors: A C TsaiAbstract:The EMG signal is a well-known and useful biomedical signal. Much information related to muscles and human Motions is included in EMG signals. Many approaches have proposed various methods that tried to recognize human Motion via EMG signals. However, one of the critical problems of Motion Pattern recognition is that the performance of recognition is easily affected by the normalization procedure and may not work well on different days. In this paper, a modified feature of the multi-channel EMG signal is proposed and the normalization procedure is also simplified by using this modified feature. To recognize Motion Pattern, we applied the support vector machine (SVM) to build the Motion Pattern recognition model. In training and validation procedures, we used the 2-DoF exoskeleton robot arm system to do the designed pose, and the multi-channel EMG signals were obtained while the user resisted the robot. Experiment results indicate that the performance of applying the proposed feature (94.9%) is better than that of conventional features. Moreover, the performances of the recognition model, which applies the modified feature to recognize the Motions on different days, are more stable than other conventional features.
Gerard Medioni - One of the best experts on this subject based on the ideXlab platform.
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ECCV (2) - Tracking Using Motion Patterns for Very Crowded Scenes
Computer Vision – ECCV 2012, 2012Co-Authors: Xuemei Zhao, Dian Gong, Gerard MedioniAbstract:This paper proposes Motion Structure Tracker (MST) to solve the problem of tracking in very crowded structured scenes. It combines visual tracking, Motion Pattern learning and multi-target tracking. Tracking in crowded scenes is very challenging due to hundreds of similar objects, cluttered background, small object size, and occlusions. However, structured crowded scenes exhibit clear Motion Pattern(s), which provides rich prior information. In MST, tracking and detection are performed jointly, and Motion Pattern information is integrated in both steps to enforce scene structure constraint. MST is initially used to track a single target, and further extended to solve a simplified version of the multi-target tracking problem. Experiments are performed on real-world challenging sequences, and MST gives promising results. Our method significantly outperforms several state-of-the-art methods both in terms of track ratio and accuracy.
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Tracking using Motion Patterns for very crowded scenes
Computer Vision–ECCV 2012, 2012Co-Authors: Xuemei Zhao, Dian Gong, Gerard MedioniAbstract:This paper proposes Motion Structure Tracker (MST) to solve the problem of tracking in very crowded structured scenes. It com- bines visual tracking, Motion Pattern learning and multi-target tracking. Tracking in crowded scenes is very challenging due to hundreds of similar objects, cluttered background, small object size, and occlusions. How- ever, structured crowded scenes exhibit clear Motion Pattern(s), which provides rich prior information. In MST, tracking and detection are per- formed jointly, and Motion Pattern information is integrated in both steps to enforce scene structure constraint. MST is initially used to track a single target, and further extended to solve a simplified version of the multi-target tracking problem. Experiments are performed on real-world challenging sequences, and MST gives promising results. Our method significantly outperforms several state-of-the-art methods both in terms of track ratio and accuracy. Keywords:
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ICCV - Robust unsupervised Motion Pattern inference from video and applications
2011 International Conference on Computer Vision, 2011Co-Authors: Xuemei Zhao, Gerard MedioniAbstract:We propose an unsupervised learning framework to infer Motion Patterns in videos and in turn use them to improve tracking of moving objects in sequences from static cameras. Based on tracklets, we use a manifold learning method Tensor Voting to infer the local geometric structures in (x, y) space, and embed tracklet points into (x, y, θ) space, where θ represents Motion direction. In this space, points automatically form intrinsic manifold structures, each of which corresponds to a Motion Pattern. To define each group, a novel robustmanifold grouping algorithm is proposed. Tensor Voting is performed to provide multiple geometric cues which formulate multiple similarity kernels between any pair of points, and a spectral clustering technique is used in this multiple kernel setting. The grouping algorithm achieves better performance than state-of-the-art methods in our applications. Extracted Motion Patterns can then be used as a prior to improve the performance of any object tracker. It is especially useful to reduce false alarms and ID switches. Experiments are performed on challenging real-world sequences, and a quantitative analysis of the results shows the framework effectively improves state-of-the-art tracker.
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Robust unsupervised Motion Pattern inference from video and applications
2011 International Conference on Computer Vision, 2011Co-Authors: Xuemei Zhao, Gerard MedioniAbstract:We propose an unsupervised learning framework to infer Motion Patterns in videos and in turn use them to improve tracking of moving objects in sequences from static cameras. Based on tracklets, we use a manifold learning method Tensor Voting to infer the local geometric structures in (x, y) space, and embed tracklet points into (x, y, θ) space, where θ represents Motion direction. In this space, points automatically form intrinsic manifold structures, each of which corresponds to a Motion Pattern. To define each group, a novel robustmanifold grouping algorithm is proposed. Tensor Voting is performed to provide multiple geometric cues which formulate multiple similarity kernels between any pair of points, and a spectral clustering technique is used in this multiple kernel setting. The grouping algorithm achieves better performance than state-of-the-art methods in our applications. Extracted Motion Patterns can then be used as a prior to improve the performance of any object tracker. It is especially useful to reduce false alarms and ID switches. Experiments are performed on challenging real-world sequences, and a quantitative analysis of the results shows the framework effectively improves state-of-the-art tracker.
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Motion Pattern interpretation and detection for tracking moving vehicles in airborne video
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009Co-Authors: Qian Yu, Gerard MedioniAbstract:Detection and tracking of moving vehicles in airborne videos is a challenging problem. Many approaches have been proposed to improve Motion segmentation on frame-by-frame and pixel-by-pixel bases, however, little attention has been paid to analyze the long-term Motion Pattern, which is a distinctive property for moving vehicles in airborne videos. In this paper, we provide a straightforward geometric interpretation of a general Motion Pattern in 4D space (x, y, vx, vy). We propose to use the tensor voting computational framework to detect and segment such Motion Patterns in 4D space. Specifically, in airborne videos, we analyze the essential difference in Motion Patterns caused by parallax and independent moving objects, which leads to a practical method for segmenting Motion Patterns (flows) created by moving vehicles in stabilized airborne videos. The flows are used in turn to facilitate detection and tracking of each individual object in the flow. Conceptually, this approach is similar to “track-before-detect” techniques, which involves temporal information in the process as early as possible. As shown in the experiments, many difficult cases in airborne videos, such as parallax, noisy background modeling and long term occlusions, can be addressed by our approach.
T. T. Lin - One of the best experts on this subject based on the ideXlab platform.
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A novel STFT-ranking feature of multi-channel EMG for Motion Pattern recognition
Expert Systems with Applications, 2015Co-Authors: A C Tsai, Jer Junn Luh, T. T. LinAbstract:Electromyography (EMG) is widely applied for neural engineering. For Motion Pattern recognition, many features of multi-channel EMG signals were investigated, but the relationships between muscles were not considered. In this study, a novel STFT-ranking feature based on short-time Fourier transform (STFT) is proposed. The novelty of STFT-ranking features is considering and covering the relationship information between EMG signals and multiple muscles in a Motion Pattern. With an exoskeleton robot arm, two series of Motion Patterns corresponding to the shoulder and elbow in the sagittal plane were investigated. EMG signals from six muscles were acquired in arm Motion Patterns when participants worn the robot arm. Four types of feature combinations, including seven conventional features, were compared with the STFT-ranking feature. The principal component analysis (PCA) and support vector machine (SVM) were used to build the Motion recognition model. With the STFT-ranking feature, the recognition performance (93.9%) is superior to the conventional features (33.3-90.8%). The recognition variation is smaller (SD = 4.3%) than the other features tested (SD = 5.9-13.8%). These achievements will contribute to the advancement of control method of exoskeleton robots or power orthoses based on multi-channel EMG signals in the future. Based on the principle of STFT-ranking feature, the method also has potential for other multi-channel signal applications, such as electroencephalography (EEG) signal processing, speech recognition, and acoustic analysis.
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A comparison of upper-limb Motion Pattern recognition using EMG signals during dynamic and isometric muscle contractions
Biomedical Signal Processing and Control, 2014Co-Authors: A C Tsai, Jer Junn Luh, T H Hsieh, T. T. LinAbstract:Multichannel electromyography (EMG) signals are one of the common methods used in human Motion Pattern recognition. In exoskeleton robot control, EMG signals are measured during dynamic or isometric muscle contractions. Various types of contraction can cause EMG signals to vary, affecting recognition performance. A Motion Pattern recognition model using EMG signals from either dynamic or isometric muscle contractions has not yet been fully investigated. In this study, a novel feature extraction method, using the short-time Fourier transform ranking (STFT-ranking) feature, was employed to determine multichannel EMG signals. The performance of the novel feature and conventional features for Motion Pattern recognition using EMG signals, which included time-domain and frequency-domain features, was compared during dynamic and isometric muscle contractions. Experiments were conducted using an exoskeleton robotic arm to aid users in generating EMG signals of designated Motion Patterns. Among the features tested, the STFT-ranking feature yielded an accuracy rate exceeding 90% when the EMG signals used in the training and validation feature data sets were of the same type of muscle contraction. After examining the STFT-ranking feature projected onto the PCA space, the STFT-ranking feature was determined to offer more satisfactory performance than the other features tested for Motion Pattern recognition, because the feature data it collected from various Motion Patterns were more separable. The experimental results also revealed that it is preferable that EMG signals from the same type of muscle contraction, whether dynamic or isometric, are consistently used in both the training and validation (control) phases. Inconsistent EMG signals in the training and validation phases yielded a negative effect on Motion Pattern recognition performance. The methodology developed in this study has potential applications in exoskeleton robot control and rehabilitation.
Tomohiro Shibata - One of the best experts on this subject based on the ideXlab platform.
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IROS - Geometric proto-symbol manipulation towards language-based Motion Pattern synthesis and recognition
2008 IEEE RSJ International Conference on Intelligent Robots and Systems, 2008Co-Authors: Tetsunari Inamura, Tomohiro ShibataAbstract:In this paper, we propose an improved mimesis method for interpolation and extrapolation of Motion Patterns in the proto-symbol space towards an ultimate goal that Motion Pattern synthesis and recognition of humanoid robots are achieved by means of natural language. The proto-symbol space is a topological space which abstracts Motion Patterns by utilizing continuous hidden Markov models. An interpolation algorithm for the proto-symbol space was proposed in a previous work, but an extrapolation algorithm was not. Therefore, in this study, we propose and extrapolation method which can further clarify the physical meaning of the dimension of the proto-symbol space that is one of the most essential issues for the realization of translation between Motion Patterns and language using the proto-symbol space. The extrapolation method also enables the robot to recognize and synthesis various kinds of Motion Patterns using a fewer number of proto-symbols. The feasibility of the proposed method is demonstrated through simulation experiments.
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Geometric proto-symbol manipulation towards language-based Motion Pattern synthesis and recognition
2008 IEEE RSJ International Conference on Intelligent Robots and Systems IROS, 2008Co-Authors: Tetsunari Inamura, Tomohiro ShibataAbstract:In this paper, we propose an improved mimesis method for interpolation and extrapolation of Motion Patterns in the proto-symbol space towards an ultimate goal that Motion Pattern synthesis and recognition of humanoid robots are achieved by means of natural language. The proto-symbol space is a topological space which abstracts Motion Patterns by utilizing continuous hidden Markov models. An interpolation algorithm for the proto-symbol space was proposed in a previous work, but an extrapolation algorithm was not. Therefore, in this study, we propose and extrapolation method which can further clarify the physical meaning of the dimension of the proto-symbol space that is one of the most essential issues for the realization of translation between Motion Patterns and language using the proto-symbol space. The extrapolation method also enables the robot to recognize and synthesis various kinds of Motion Patterns using a fewer number of protosymbols. The feasibility of the proposed method is demonstrated through simulation experiments. ©2008 IEEE.
Vassilis J. Tsotras - One of the best experts on this subject based on the ideXlab platform.
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Spatio-Temporal Databases: Complex Motion Pattern Queries
2013Co-Authors: Marcos R. Vieira, Vassilis J. TsotrasAbstract:This brief presents several new query processing techniques, called complex Motion Pattern queries, specifically designed for very large spatio-temporal databases of moving objects. The brief begins with the definition of flexible Pattern queries, which are powerful because of the integration of variables and Motion Patterns. This is followed by a summary of the expressive power of Patterns and flexibility of Pattern queries. The brief then present the Spatio-Temporal Pattern System (STPS) and density-based Pattern queries. STPS databases contain millions of records with information about mobile phone calls and are designed around cellular towers and places of interest. Density-based Pattern queries capture the aggregate behavior of trajectories as groups. Several evaluation algorithms are presented for finding groups of trajectories that move together in space and time, i.e. within a predefined distance to each other. Finally, the brief describes a generic framework, called DivDB, for diversifying query results. Two new evaluation methods, as well as several existing ones, are described and tested in the proposed DivDB framework. The efficiency and effectiveness of all the proposed complex Motion Pattern queries are demonstrated through an extensive experimental evaluation using real and synthetic spatio-temporal databases. This clear evaluation of new query processing techniques makes Spatio-Temporal Database a valuable resource for professionals and researchers studying databases, data mining, and Pattern recognition.
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Complex Motion Pattern queries for trajectories
2011 IEEE 27th International Conference on Data Engineering Workshops, 2011Co-Authors: Marcos R. Vieira, Vassilis J. TsotrasAbstract:With the recent advancements and wide usage of location detection devices, large quantities of data are collected by GPS and cellular technologies in the form of trajectories. While most previous work on trajectory-based queries has concentrated on traditional range, nearest-neighbor and similarity queries, there is still the need to query trajectories using complex, yet more intuitive to users, Motion Patterns. In this paper, we describe several types of Motion Pattern queries for trajectories. In particular, we describe in detail two types of Motion Pattern queries: the flexible Pattern queries, which focus on trajectories that follow a sequence of spatiotemporal events; and the density-based Pattern queries, where the goal is to search trajectories that “stay together” for a long period of time. We then conclude this paper by briefly describing two other novel complex Motion Pattern queries that are currently under development.
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ICDE Workshops - Complex Motion Pattern queries for trajectories
2011 IEEE 27th International Conference on Data Engineering Workshops, 2011Co-Authors: Marcos R. Vieira, Vassilis J. TsotrasAbstract:With the recent advancements and wide usage of location detection devices, large quantities of data are collected by GPS and cellular technologies in the form of trajectories. While most previous work on trajectory-based queries has concentrated on traditional range, nearest-neighbor and similarity queries, there is still the need to query trajectories using complex, yet more intuitive to users, Motion Patterns. In this paper, we describe several types of Motion Pattern queries for trajectories. In particular, we describe in detail two types of Motion Pattern queries: the flexible Pattern queries, which focus on trajectories that follow a sequence of spatiotemporal events; and the density-based Pattern queries, where the goal is to search trajectories that “stay together” for a long period of time. We then conclude this paper by briefly describing two other novel complex Motion Pattern queries that are currently under development.
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A Generic Framework for Continuous Motion Pattern Query Evaluation
2008 IEEE 24th International Conference on Data Engineering, 2008Co-Authors: Petko Bakalov, Vassilis J. TsotrasAbstract:We introduce a novel query type defined over streaming moving object data, namely, the continuous Motion Pattern (CMP) queries. A Motion Pattern is defined as a sequence of distinct spatial predicates, each attached to a temporal constraint. The spatial predicates can be of various types (range, nearest neighbor, etc.) The temporal constraints are relative to the current time instant and are used to specify the order of the spatial predicates on the time axis. A CMP query is continuously reevaluated over streaming spatiotemporal data, producing the moving objects which satisfy the query's Motion Pattern. We first introduce an easily maintainable indexing scheme for spatiotemporal streams that facilitates the evaluation of the spatial predicates over their temporal constraints. Using this scheme we propose a generic framework for efficiently answering a wide range of CMP queries. The effectiveness of our algorithms in reducing the query computation cost and I/O operations is revealed through a thorough experimental evaluation.
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ICDE - A Generic Framework for Continuous Motion Pattern Query Evaluation
2008 IEEE 24th International Conference on Data Engineering, 2008Co-Authors: Petko Bakalov, Vassilis J. TsotrasAbstract:We introduce a novel query type defined over streaming moving object data, namely, the continuous Motion Pattern (CMP) queries. A Motion Pattern is defined as a sequence of distinct spatial predicates, each attached to a temporal constraint. The spatial predicates can be of various types (range, nearest neighbor, etc.) The temporal constraints are relative to the current time instant and are used to specify the order of the spatial predicates on the time axis. A CMP query is continuously reevaluated over streaming spatiotemporal data, producing the moving objects which satisfy the query's Motion Pattern. We first introduce an easily maintainable indexing scheme for spatiotemporal streams that facilitates the evaluation of the spatial predicates over their temporal constraints. Using this scheme we propose a generic framework for efficiently answering a wide range of CMP queries. The effectiveness of our algorithms in reducing the query computation cost and I/O operations is revealed through a thorough experimental evaluation.