Key Postures Research Articles

Hearing the Word and Seeing the Light: Voice and Vision in Acts

The theme of sight plays a central role throughout the book of Acts. While some scholars have identified this theme, especially with respect to visions, they typically overlook the intersection of sight and audition. This article argues that Luke pairs seeing with hearing and emphasizes the sense of hearing to an even greater degree. The article first addresses sight and visions in Acts and then focuses on Stephen’s vision in Acts 7. This is the one vision in Acts that does not involve divinely bestowed words, yet it still betrays Luke’s preference for hearing – and speaking – God’s word. The article then explores key themes that emerge from Stephen’s vision regarding the visual and verbal modes of perception in Acts as a whole. Overall, Luke’s emphasis on the spoken ‘word’ reflects his reliance on Jewish scriptural traditions that likewise highlight listening as a key epistemological posture.

Serpenoid polygonal rolling for chain-type modular robots: A study of modeling, pattern switching and application

Rolling is a frequently used high-efficiency motion pattern for articulated loop robots. An important issue is planning the angle of each joint to keep a loop closed on the fly and to roll forward stably. Most of the current approaches achieve a gait table by designing a few key postures or a numerical approximation of a special loop geometry (e.g., an ellipse), which is difficult to implement for non-experts and makes motion planning time-consuming. The change in joint angles for many current loop gaits is not smooth. The serpenoid curve, whose curvature changes sinusoidally, exhibits the advantage of making angle changes smooth. Based on this feature, a generalized Serpenoid Polygon model for loop gait is proposed, which extends Hirose's Serpenoid Oval to Polygon. Furthermore, we derive its scalable planning model for loop robots with different numbers of joints, which is easy to implement. The influence of key parameters on the performance of this model is investigated through numerous dynamics simulations with a general linkage-mechanism. In addition, loop forming and rolling-pattern switching are studied to facilitate the implementation of this model. In the end, the applications of the model are discussed, its effectiveness is validated through experiments using the UBot modular robot. A serpenoid polygonal rolling model is proposed for articulated modular robots.The outer shape can be changed to adapt to varying environments and tasks.Investigating switching among various loop patterns facilitates the model application.The effectiveness is validated by physical experiments.The model is scalable and can be implemented in similar modular robots.

Imrovement of Human Key Posture Recognition

Imrovement of Human Key Posture Recognition

Human movement analysis around a view circle using time-order similarity distributions

This paper presents a new behavior classification system to analyze human movements around a view circle using time-order similarity distributions. To maintain the view in-variance, an action is represented not only from its spatial domain but also its temporal domain. After that, a novel alignment scheme is proposed for aligning each action to a fixed view. With the best view, the task of behavior analysis becomes a string matching problem. One novel idea proposed in this paper is to code a posture using not only its best matched key posture but also other unmatched key postures to form various similarity distributions. Then, recognition of two actions becomes a problem of matching two time-order distributions which can be very effectively solved by comparing their KL distance via a dynamic programming scheme.

Sketch-based human motion retrieval via selected 2D geometric posture descriptor

Sketch-based human motion retrieval is a hot topic in computer animation in recent years. In this paper, we present a novel sketch-based human motion retrieval method via selected 2-dimensional (2D) Geometric Posture Descriptor (2GPD). Specially, we firstly propose a rich 2D pose feature call 2D Geometric Posture Descriptor (2GPD), which is effective in encoding the 2D posture similarity by exploiting the geometric relationships among different human body parts. Since the original 2GPD is of high dimension and redundant, a semi-supervised feature selection algorithm derived from Laplacian Score is then adopted to select the most discriminative feature component of 2GPD as feature representation, and we call it as selected 2GPD. Finally, a posture-by-posture motion retrieval algorithm is used to retrieve a motion sequence by sketching several key postures. Experimental results on CMU human motion database demonstrate the effectiveness of our proposed approach.

Human motion retrieval based on freehand sketch

ABSTRACTIn this paper, we present an integrated framework of human motion retrieval based on freehand sketch. With some simple rules, the user can acquire a desired motion by sketching several key postures. To retrieve efficiently and accurately by sketch, the 3D postures are projected onto several 2D planes. The limb direction feature is proposed to represent the input sketch and the projected‐postures. Furthermore, a novel index structure based on k‐d tree is constructed to index the motions in the database, which speeds up the retrieval process. With our posture‐by‐posture retrieval algorithm, a continuous motion can be got directly or generated by using a pre‐computed graph structure. What's more, our system provides an intuitive user interface. The experimental results demonstrate the effectiveness of our method. © 2014 The Authors. Computer Animation and Virtual Worlds published by John Wiley & Sons, Ltd.

Body Language in the Preconquest and Colonial Nahua World

This article examines several key gestures and postures documented in the early postconquest Nahua world: the eating of earth, squatting and kneeling, prostration, bowing, and finger pointing. Combining distinct genres of sources, ranging from linguistic evidence to iconographic data, I attempt to reconstruct preconquest practices through postconquest filters as well as to illuminate the ways in which local traditions coalesced with European practices and concepts. The study of body language illustrates broader phenomena related to change and continuity in the postcontact era, revealing the survival of preconquest elements, their transformation under European impact, cultural convergence, and adoption of new forms of bodily expression. An inherent part of this endeavor is the study of the postconquest terminology referring to gestures and postures, adding to our knowledge of the mechanisms of coinage of native terms referring to Christian religion.

Motion Key-frames extraction based on amplitude of distance characteristic curve

AbstractThe key frames extraction technique extracts key postures to describe the original motion sequence, which has been widely used in motion compression, motion retrieval, motion edition and so on. In this paper, we propose a method based on the amplitude of curve to find key frames in a motion captured sequence. First we select a group of joint distance features to represent the motion and adopt the Principal Component Analysis (PCA) method to obtain the one dimension principal component as a features curve which will be used. Then we gain the initial key-frames by extracting the local optimum points in the curve. At last, we get the final key frames by inserting frames based on the amplitude of the curve and merging key frames too close. A number of experimental examples demonstrate that our method is practicable and efficient not only in the visual performance but also in the aspect of the compression ratio and error rate.

Similar key posture transformation based on hierarchical Option for humanoid robot

Concerning the problem in which the fixed locomotion track captured from human movement can not be used in transformation between key postures for humanoid robot,a method of similar key posture transformation based on hierarchical Option for humanoid robot was proposed.The multi-level dendrogram of key postures was constructed and the difference of key postures was illustrated in respects of similar joint difference,moment total similar difference,period total similar difference.The hierarchical reinforcement Option learning was introduced,in which the sets of key postures and Option actions were constructed.SMDP-Q method tended to be the optimal Option function by the accumulative rewards of key posture difference and the transformations were realized.The experiments show the validity of the method.

Silhouette-Edge-Based Descriptor for Human Action Representation and Recognition

Extraction and representation of postures and/or gestures from human activities in videos have been a focus of research in this area of action recognition. With various applications cropping up from different fields, this paper seeks to improve the performance of these action recognition machines by proposing a shape-based silhouette-edge descriptor for the human body. Information entropy, a method to measure the randomness of a sequence of symbols, is used to aid the selection of vital key postures from video frames. Morphological operations are applied to extract and stack edges to uniquely represent different actions shape-wise. To classify an action from a new input video, a Hausdorff distance measure is applied between the gallery representations and the query images formed from the proposed procedure. The method is tested on known public databases for its validation. An effective method of human action annotation and description has been effectively achieved.

Human Action Recognition Algorithm Based on Key Posture

A novel human action recognition algorithm based on key posture is proposed in this paper. In the method, the mesh features of each image in human action sequences are firstly calculated; then the key postures of the human mesh features are generated through k-medoids clustering algorithm; and the motion sequences are thus represented as vectors of key postures. The component of the vector is the occurrence number of the corresponding posture included in the action. For human action recognition, the observed action is firstly changed into key posture vector; then the correlevant coefficients to the training samples are calculated and the action which best matches the observed sequence is chosen as the final category. The experiments on Weizmann dataset demonstrate that our method is effective for human action recognition. The average recognition accuracy can exceed 90%.

Human movement using a constraint-based software environment: gaining understanding during initial design phases

Digital human modelling is becoming increasingly important in the early stages of the design process for several reasons including the need to investigate human-product interactions. This paper presents an original approach to predicting human movement, and in particular, the key postures, required to perform a particular task. The approach is based on the use of a constraint-based model of a human manikin, and, as such, it can be used even when the designs of the environment and the task itself have not been fully defined. Constraints are applied to specify the task requirements and also the limits of potential joint movement. This enables the model to be used as a means for understanding what postures and movements are required (in the evolving digital environment) rather than requiring them to be specifically given. The approach is demonstrated with a case study based on the domestic task of ironing clothes.

Study of similar motion imitation of stepping upstairs for humanoid robot

Aimed at the situation of fitting variable environments in similarity imitation of stepping upstairs for humanoid robot, the process of designing similarity imitation was analysed as well as the rhythm control of key posture in similar locomotion. The simplified locomotion model and retargeting, connection of motion phases, and ZMP stability constraints were shown in similarity locomotion. The hierarchical reinforcement learning MaxQ was used to adjust the movement tracks of similarity to fit the variable stairs. The experiments showed the validity of the method.

Hash-based early recognition of gesture patterns

In these days, "early recognition" of gesture patterns has been studied by many researchers. Early recognition is a method to make a decision of gesture recognition at the beginning part of it. In traditional method, the key postures for a gesture are utilized for recognition and early recognition is performed frame-by-frame. However, this method has a problem that computational time in recognition processing increases in proportion to size of posture database. If the processing time becomes longer, some input frames will be ignored from the processing. It results in lower recognition accuracy. In this paper, we introduce a hash-based approach to search the posture database. It realizes real-time processing, and keep high performance of recognition.

Indigenizing Obama

This article examines the production and circulation of an ‘Indigenous’ Obama. It dissects a key image circulated widely in the wake of the ObamaCare debate and proposes that the modes of adoption and adaptation at work in the image invoke and negatively reflect upon Obama’s adoption and adaptation of a Native American policy agenda, understood by the Tea Party as an attack on presumed white privileges and entitlements. The image’s re-citation of key postures and props from a photograph of the Apache warrior, Goyaalé/Geronimo, invokes and denies the ongoing insurgency of Native American sovereignties sublimating the threat posed by substituting the more consoling image of a feminized Pacific body for an insurgent Indian one.

Key Posture Extraction from Object Manipulations Experiments

In this work we present a novel concept for key posture by looking for grasping similarities among several grasping experiments. To highlight the nature of key posture idea, the object used in experiment had different sizes, although share the same type. Grouping the extracted data by joint, we search for time interval with high data concentration. If this time interval is shared by many joints in the same experiment we can extract key posture from that interval. The key posture can help a robotic hand system to grasp, control and manipulate the object through a specific task.

The Psychological Benefits of Yoga Practice for Older Adults: Evidence and Guidelines

Yoga is an effective complementary approach to health maintenance and promotion for older adults and has been demonstrated to support many dimensions of psychological wellbeing, from everyday stress to anxiety, depression, and coping with health challenges. Yoga has the potential to be even more effective when consciously and systematically integrated into an individual's overall self-care and medical care program, through deliberate and open dialogue among patients, healthcare professionals, and yoga professionals. The purpose of this article is to (1) briefly review the psychological benefits of yoga practice for older adults; (2) outline practice guidelines for older adult yoga, including key postures; and (3) provide some basic practical guidelines for both healthcare professionals referring patients to yoga and yoga teachers interested in working with older adults.

From Canonical Poses to 3D Motion Capture Using a Single Camera

We combine detection and tracking techniques to achieve robust 3D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera. We rely on detecting key postures, which can be done reliably, using a motion model to infer 3D poses between consecutive detections, and finally refining them over the whole sequence using a generative model. We demonstrate our approach in the cases of golf motions filmed using a static camera and walking motions acquired using a potentially moving one. We will show that our approach, although monocular, is both metrically accurate because it integrates information over many frames and robust because it can recover from a few misdetections.

Segmentation of Human Body Parts Using Deformable Triangulation

This paper presents a novel segmentation algorithm to segment a body posture into different body parts using the technique of deformable triangulation. To analyze each posture more accurately, they are segmented into triangular meshes, where a spanning tree can be found from the meshes using a depth-first search scheme. Then, we can decompose the tree into different subsegments, where each subsegment can be considered as a limb. Then, two hybrid methods (i.e., the skeleton-based and model-driven methods) are proposed for segmenting the posture into different body parts according to its occlusion conditions. To analyze occlusion conditions, a novel clustering scheme is proposed to cluster the training samples into a set of key postures. Then, a model space can be used to classify and segment each posture. If the input posture belongs to the nonocclusion category, the skeleton-based method is used to divide it into different body parts that can be refined using a set of Gaussian mixture models (GMMs). For the occlusion case, we propose a model-driven technique to select a good reference model for guiding the process of body part segmentation. However, if two postures' contours are similar, there will be some ambiguity that can lead to failure during the model selection process. Thus, this paper proposes a tree structure that uses a tracking technique so that the best model can be selected not only from the current frame but also from its previous frame. Then, a suitable GMM-based segmentation scheme can be used to finely segment a body posture into the different body parts. The experimental results show that the proposed method for body part segmentation is robust, accurate, and powerful.

Postural activity monitoring for increasing safety in bomb disposal missions

In enclosed suits, such as those worn by explosive ordnance disposal (EOD) experts, evaporative cooling through perspiration is less effective and, particularly in hot environments, uncompensable heat stress (UHS) may occur. Although some suits have cooling systems, their effectiveness during missions is dependent on the operative's posture. In order to properly assess thermal state, temperature-based assessment systems need to take posture into account. This paper builds on previous work for instrumenting EOD suits with regard to temperature monitoring and proposes to also monitor operative posture with MEMS accelerometers. Posture is a key factor in predicting how body temperature will change and is therefore important in providing local or remote warning of the onset of UHS. In this work, the C4.5 decision tree algorithm is used to produce an on-line classifier that can differentiate between nine key postures from current acceleration readings. Additional features that summarize how acceleration is changing over time are used to improve average classification accuracy to around 97.2%. Without such temporal feature extraction, dynamic postures are difficult to classify accurately. Experimental results show that training over a variety of subjects, and in particular, mixing gender, improves results on unseen subjects. The main advantages of the on-line posture classification system described here are that it is accurate, does not require integration of acceleration over time, and is computationally lightweight, allowing it to be easily supported on wearable microprocessors.