• Journal of Computing Science and Engineering
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• v.12 no.1
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• pp.1-11
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• 2018

Human activities recognition is a challenging task due to its complexity of human movements and the variety performed by different subjects for the same action. This paper presents a recognition algorithm by using skeleton information generated from depth maps. Concatenating motion features and temporal constraint feature produces feature vector. Reducing dictionary scale proposes an improved fast classifier based on sparse representation. The developed method is shown to be effective by recognizing different activities on the UTD-MHAD dataset. Comparison results indicate superior performance of our method over some existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2632-2649
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• 2012

Most approaches to human action recognition is limited due to the use of simple action datasets under controlled environments or focus on excessively localized features without sufficiently exploring the spatio-temporal information. This paper proposed a framework for recognizing realistic human actions. Specifically, a new action representation is proposed based on computing a rich set of descriptors from keypoint trajectories. To obtain efficient and compact representations for actions, we develop a feature fusion method to combine spatial-temporal local motion descriptors by the movement of the camera which is detected by the distribution of spatio-temporal interest points in the clips. A new topic model called Markov Semantic Model is proposed for semantic feature selection which relies on the different kinds of dependencies between words produced by "syntactic " and "semantic" constraints. The informative features are selected collaboratively based on the different types of dependencies between words produced by short range and long range constraints. Building on the nonlinear SVMs, we validate this proposed hierarchical framework on several realistic action datasets.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1075-1082
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• 2007

In this paper, we propose the method of multiple human action recognition on video clip. For being invariant to the change of speed or size of actions, Spatial-Temporal Pyramid method is applied. Proposed method can minimize the complexity of the procedures owing to select Motion Gradient Histogram (MGH) based on statistical approach for action representation feature. For multiple action detection, Motion Energy Image (MEI) of binary frame difference accumulations is adapted and then we detect each action of which area is represented by MGH. The action MGH should be compared with pre-learning MGH having pyramid method. As a result, recognition can be done by the analyze between action MGH and pre-learning MGH. Ten video clips are used for evaluating the proposed method. We have various experiments such as mono action, multiple action, speed and site scale-changes, comparison with previous method. As a result, we can see that proposed method is simple and efficient to recognize multiple human action with stale variations.

• Steel and Composite Structures
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• v.19 no.5
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• pp.1145-1165
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• 2015

A behavior model for screw connections is developed to provide a better representation of the nonlinear response of thin steel plate shear walls (TSPSWs) with infill plates attached to the boundary frame members via self-drilling screws. This analytical representation is based on the load-bearing deformation relationship between the infill plate and the screw threads. The model can be easily implemented in strip models of TSPSWs where the tension field action of the infill plates is represented by a series of parallel discrete tension-only strips. Previously reported experimental results from tests of two different TSPSWs are used to provide experimental validation of the modeling approach. The beam-to-column connection behavior was also included in the analyses using a four parameter rotational spring model that was calibrated to a test of an identical frame as used for the TSPSW specimens but without the infill plates. The complete TSPSW models consisting of strips representing the infill plates, zero length elements representing the load-bearing deformation response of the screw connection at each end of the strips and the four parameter spring model at each beam-to-column connection are shown to have good agreement with the experimental results. The resulting models should enable design and analysis of TSPSWs for both new construction and retrofit of existing buildings.

• Korean Journal of Cognitive Science
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• v.27 no.4
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• pp.573-597
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• 2016

Recent fMRI and EEG research for neural representations of action concepts insist that processing of action concepts evoke the simulation of sensory-motor information. Moreover, there are several behavioral studies showing that understanding of action verbs or sentences describing actions interfere or facilitate current action performance. However, it is unclear that online interaction between processing of action concepts and current action is based on the simulation of sensory-motor information, or other neural mechanisms. The present research aims to explore the underlying neural mechanism that how the perception of action language influence the performance of current action using high-spacial temporal resolution EEG and multiple source analysis techniques. For this, participants were asked to perform a cued-motor reaction task in which button-pressing hand action and pedal-stepping foot action were required according to the color of the cue, and we presented auditorily action verbs describing the responding actions (i.e., /press/, /step/, /stop/) just before the color cue and examined the interaction effect from the semantic congruency between the action verbs and the current action. Behavioral results revealed consistently a facilitatory effect when action verbs and responding actions were semantically congruent in both button-pressing and pedal-stepping actions, and an inhibitory effect when semantically incongruent in the button-pressing action condition. In the results of EEG source waveform analysis, the semantic congruency effects between action verbs and the responding actions were observed in the Wernicke's area during the perception of action verbs, in the anterior cingulate gyrus and the supplementary motor area (SMA) at the time when the motor-cue was presented, and in the SMA and primary motor cortex (M1) during action execution stage. Based on the current findings, we argue that perceived action verbs evoke the facilitation/inhibition effect by influencing the expectation and preparation stage of following actions rather than the directly activating the particular motor cortex. Finally we discussed the implication on the neural representation of action concepts and methodological limitations of the current research.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.337-345
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• 2023

In this study, we formulated a method that evaluates Taekwondo Poomsae performance using a series of choreographed training movements. Despite recent achievements in 3D human pose estimation (HPE) performance, the analysis of human actions remains challenging. In particular, Taekwondo Poomsae action analysis is challenging owing to the absence of time synchronization data and necessity to compare postures, rather than directly relying on joint locations owing to differences in human shapes. To address these challenges, we first decomposed human joint representation into joint rotation (posture) and limb length (body shape), then synchronized a comparison between test and reference pose sequences using DTW (dynamic time warping), and finally compared pose angles for each joint. Experimental results demonstrate that our method successfully synchronizes test action sequences with the reference sequence and reflects a considerable gap in performance between practitioners and professionals. Thus, our method can detect incorrect poses and help practitioners improve accuracy, balance, and speed of movement.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.739-748
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• 2014

In this paper, we propose pyramid appearance and global structure action descriptors on both RGB and depth motion history images and a model-free method for human action recognition. In proposed algorithm, we firstly construct motion history image for both RGB and depth channels, at the same time, depth information is employed to filter RGB information, after that, different action descriptors are extracted from depth and RGB MHIs to represent these actions, and then multimodality information collaborative representation and recognition model, in which multi-modality information are put into object function naturally, and information fusion and action recognition also be done together, is proposed to classify human actions. To demonstrate the superiority of the proposed method, we evaluate it on MSR Action3D and DHA datasets, the well-known dataset for human action recognition. Large scale experiment shows our descriptors are robust, stable and efficient, when comparing with the-state-of-the-art algorithms, the performances of our descriptors are better than that of them, further, the performance of combined descriptors is much better than just using sole descriptor. What is more, our proposed model outperforms the state-of-the-art methods on both MSR Action3D and DHA datasets.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.383-388
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• 2022

This thesis explores the principle of a performer's organic action by means of his/her bodily responses on stage. This research has been developed to define the nature of a performer's central task in order to constitute empirical understanding of acting and the purpose of training in addressing the question of what sort of qualitative bodily training is necessary to be in a state of the full body involvement. This study investigates to articulate a performer's fundamental task at the most rudimentary level by utilizing those theatre artists' concepts with practical assumptions. In particular, the key terms, happen and change signifies the quality of a performer's body that has to fit into the given environment in which the performer's body can be subordinated through the moment on stage. Here, we argue that a performer's essential task parallel to make the following moment to happen and change by means of progressing a set of the next moment. In this manner, we also argue that a moment of displaying the performer's conscious effort, forceful and externalizing the visible elements under the use of erroneous language leads his/her body not to function on stage, a state of disengagement from his/her body. Finally, we provide a way to facilitate a performer's organic action by focused on his/her lived experience to create the functional moment which is opposite to the predominance of a representation, maintaining the performer's intellectual sense.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.4
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• pp.368-376
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• 1999

This paper studies the operational characteristics of thyristor controlled shunt compensator in a simple power t transmission system. With Fourier series representation of the thyristor switching action and the system parameters, t the thyristor current equations are derived, which transmit the required real power of the simple power transmission s system. Bisection algorithm is used to solve the thyristor current equations, which informs the thyristor firing an밍e, t the thyristor conduction an밍e, the power flows and the harmonic characteristics. The stability analysis is performed w with the theory of Poincare mapping for the nonlinear discrete periodic dynamic system. EMTP simulations at the v various operating points show the transient characteristics of the thyristor controlled shunt compensator and C correspond to the results calculated with Fourier series representation and the stability analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1587-1592
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• 2003

There are three kinds of singularity in controlling redundant manipulators. Kinematic, algorithmic and representation singularities are those. If manipulators fall into any singularity without proper action to avoid it, the control system must go away from our desire, and we can meet a dangerous situation. Hence, we have to deal the singularities very carefully. In this paper, we describe an on-line solution for avoiding the occurrence of both algorithmic and kinematic singularities in task-priority based kinematic controllers of robotic manipulators. Representation singularity can be easily avoided by using proper representation algorithm, so, in this paper, we only consider kinematic and algorithmic singularities. The proposed approach uses a desired task reconstruction and a successive task projection in order to maintain the measure for singularity over a user defined minimum value. It shows a gain in performance and a better task error especially when working in proximity of singular configurations. It is particularly suitable for autonomous systems where an off-line trajectory control scheme is often not applicable. The advantage and performance of the proposed controller is verified by simulation works. And, the experiment with real manipulator is remaining for the future works.