Location Of Body Parts Research Articles

Anesthetic Management During Atrial Septostomy in a Conjoined Thoraco-Omphalopagus Twin With Tricuspid Atresia and d-Transposition of the Great Arteries Before Separation: A Case Report.

Conjoined twins are uncommon with reported incidences of 1 in 30,000-200,000 births. They represent a heterogeneous population in regard to location of joint body parts and presence/extent of internal organ fusion. Positioning, airway management, possible presence of cross-circulation, and the fact that 2 patients require anesthesia for each procedure present significant challenges to the anesthesiologist. We report the anesthetic care of a conjoined twin set in which one of the patients presented with tricuspid atresia, d-transposition of the great arteries, and both atrial and ventricular septal defect. A balloon atrial septostomy was performed to allow survival after a separation procedure.

Egocentric Mapping of Body Surface Constraints.

The relative location of human body parts often materializes the semantics of on-going actions, intentions and even emotions expressed, or performed, by a human being. However, traditional methods of performance animation fail to correctly and automatically map the semantics of performer postures involving self-body contacts onto characters with different sizes and proportions. Our method proposes an egocentric normalization of the body-part relative distances to preserve the consistency of self contacts for a large variety of human-like target characters. Egocentric coordinates are character independent and encode the whole posture space, i.e., it ensures the continuity of the motion with and without self-contacts. We can transfer classes of complex postures involving multiple interacting limb segments by preserving their spatial order without depending on temporal coherence. The mapping process exploits a low-cost constraint relaxation technique relying on analytic inverse kinematics; thus, we can achieve online performance animation. We demonstrate our approach on a variety of characters and compare it with the state of the art in online retargeting with a user study. Overall, our method performs better than the state of the art, especially when the proportions of the animated character deviate from those of the performer.

Human body segmentation based on shape constraint

Human body segmentation is essential for many practical applications, e.g., video surveillance analysis in intelligent urban. However, existing methods mainly suffer from various human poses. In this paper, we try to address this issue by introducing human shape constraint. First, human pose estimation is performed, and locations of human body parts are determined. Contrast to the previous work, we just use the human body parts with high precision. Then we combines the star convexity and the human body parts’ locations as shape constraint. The final segmentation results are acquired through the optimization step. Comprehensive and comparative experimental results demonstrate that the proposed method achieves promising performance and outperforms many state-of-the-art methods over publicly available challenging datasets.

Children’s use of sexual body part terms in witness interviews about sexual abuse

For successful prosecution, investigative interviews with child sexual abuse victims need to establish the nature of the alleged offence by determining the body parts that were involved. To date, however, there has been a paucity of research on the extent to which children clearly identify sexual body parts in interviews and how they respond to interviewers’ attempts to clarify ambiguous terms. The present study sought to explore children’s use of sexual body part terms in field interviews, and their responses to questions aimed at clarifying ambiguous terms. Analyses were conducted on 161 transcripts of field interviews with children aged 4–17 years old, coding for the content of interviewers’ questions and of children’s responses. Results revealed that many children, even some in their late teens, struggled to provide clear terms for their sexual body parts, either initially, or when asked to provide an alternate term. Questions about body part location or function tended to elicit content appropriate responses in children, irrespective of age. The study discusses the capacity (or willingness) of children to provide clear body part terms in witness interviews and the utility of particular questions. The findings imply a need for caution when following up on the meaning of terms or asking particular clarifying questions, and highlight the importance of childhood education around body parts.

Human Pose Estimation by Exploiting Spatial and Temporal Constraints in Body-Part Configurations

We present an algorithm for estimating a sequence of articulated upper-body human pose in unconstrained videos. Most previous work often fails to locate forearms in those video scenes suffering from illumination varieties, background clutter, camera shake, or occlusion. In order to deal with such intractable cases, we propose a novel algorithm for addressing the problem of certain body parts localization. The proposed approach can be roughly divided into two steps: first, a spatial model is designed to capture the high-order relationship between adjacent parts and meanwhile to generate a set of configurations in each frame under the temporal context constraint; second, a competitive method is presented to select the best body parts among diverse pose configurations. In this paper, the proposed algorithm focuses on the unconstrained video scenes and improves the detection precision of certain body parts with high degree of freedom. Moreover, the proposed algorithm can be well applied to a very challenging dataset named Movies. Experimental results show that the proposed algorithm can dramatically improve performance compared with those related algorithms on two benchmark datasets (MPII and SHPED datasets) and on our Movies dataset.

Human Pose Estimation from Monocular Images: A Comprehensive Survey.

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.

Autonomous vision-guided approach for the analysis and grading of vertical suspension tests during Hammersmith Infant Neurological Examination (HINE).

Computer vision assisted diagnostic systems are gaining popularity in different healthcare applications. This paper presents a video analysis and pattern recognition framework for the automatic grading of vertical suspension tests on infants during the Hammersmith Infant Neurological Examination (HINE). The proposed vision-guided pipeline applies a color-based skin region segmentation procedure followed by the localization of body parts before feature extraction and classification. After constrained localization of lower body parts, a stick-diagram representation is used for extracting novel features that correspond to the motion dynamic characteristics of the infant's leg movements during HINE. This set of pose features generated from such a representation includes knee angles and distances between knees and hills. Finally, a time-series representation of the feature vector is used to train a Hidden Markov Model (HMM) for classifying the grades of the HINE tests into three predefined categories. Experiments are carried out by testing the proposed framework on a large number of vertical suspension test videos recorded at a Neuro-development clinic. The automatic grading results obtained from the proposed method matches the scores of experts at an accuracy of 74%.

Retargeting Human-Object Interaction to Virtual Avatars.

In augmented reality (AR) applications, a virtual avatar serves as a useful medium to represent a human in a different place. This paper deals with the problem of retargeting a human motion to an avatar. In particular, we present a novel method that retargets a human motion with respect to an object to that of an avatar with respect to a different object of a similar shape. To achieve this, we developed a spatial map that defines the correspondences between any points in the 3D spaces around the respective objects. The key advantage of the spatial map is that it identifies the desired locations of the avatar's body parts for any input motion of a human. Once the spatial map is created offline, the motion retargeting can be performed in real-time. The retargeted motion preserves important features of the original motion such as the human pose and the spatial relation with the object. We report the results of a number of experiments that demonstrate the effectiveness of the proposed method.

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.

A Customizable Multimodality Imaging Compound That Relates External Landmarks to Internal Structures.

Numerous research and clinical interventions, such as targeting drug deliveries or surgeries and finding blood clots, abscesses, or lesions, require accurate localization of various body parts. Individual differences in anatomy make it hard to use typical stereotactic procedures that rely on external landmarks and standardized atlases. For instance, it is not unusual to incorrectly place a craniotomy in brain surgery. This project was thus performed to find a new and easy method to correctly establish the relationship between external landmarks and medical scans of internal organs, such as specific regions of the brain. This paper introduces an MRI, CT, and radiographically visible compound that can be applied to any surface and therefore provide an external reference point to an internal (eye-invisible) structure. Tested on nonhuman primates and isolated brain scans, this compound showed up with the same color in different scan types, making practical work possible. Conventional, and mostly of specific utility, products such as contrast agents were differentially colored or completely failed to show up and were not flexible. This compound can be customized to have different viscosities, colors, odors, and other characteristics. It can also be mixed with hardening materials such as acrylic for industrial or engineering uses, for example. Laparoscopy wands, electroencephalogram leads, and other equipment could also be embedded with or surrounded by the compound for ease in 3-dimensional visualizations. A pending U.S. patent endorses this invention.

Implicit body representations and tactile spatial remapping

To perceive the location of a tactile stimulus in external space (external tactile localisation), information about the location of the stimulus on the skin surface (tactile localisation on the skin) must be combined with proprioceptive information about the spatial location of body parts (position sense) — a process often referred to as ‘tactile spatial remapping’. Recent research has revealed that both of these component processes rely on highly distorted implicit body representations. For example, on the dorsal hand surface position sense relies on a squat, wide hand representation. In contrast, tactile localisation on the same skin surface shows large biases towards the knuckles. These distortions can be seen as behavioural ‘signatures’ of these respective perceptual processes. Here, we investigated the role of implicit body representation in tactile spatial remapping by investigating whether the distortions of each of the two component processes (tactile localisation and position sense) also appear when participants localise the external spatial location of touch. Our study reveals strong distortions characteristic of position sense (i.e., overestimation of distances across vs along the hand) in tactile spatial remapping. In contrast, distortions characteristic of tactile localisation on the skin (i.e., biases towards the knuckles) were not apparent in tactile spatial remapping. These results demonstrate that a common implicit hand representation underlies position sense and external tactile localisation. Furthermore, the present findings imply that tactile spatial remapping does not require mapping the same signals in a frame of reference centred on a specific body part.

Intuitive anatomy: Distortions of conceptual knowledge of hand structure

Knowledge of the spatial layout of bodies is mediated by a representation called the body structural description, damage to which results in the condition of autotopagnosia in which patients are impaired in judgments about the location and configuration of body parts. While a large literature has investigated disruption of the body structural description, little research has examined its accuracy in healthy individuals. I show that people have systematically distorted knowledge of the configuration of hands. Participants judged the location of their knuckles (i.e., the metacarpophalangeal joint) by pointing with a baton on their palm. Participants showed clear distal biases, judging their knuckles as farther forward in the hand than they actually are for all fingers except the thumb. This effect appeared both when participants localized the knuckles of their own hand and another person’s hand. These results suggest that intuitive beliefs about body form are systematically distorted.

HUMAN ACTION POSELETS ESTIMATION VIA COLOR G-SURF IN STILL IMAGES

Abstract. Human activity is a persistent subject of interest in the last decade. On the one hand, video sequences provide a huge volume of motion information in order to recognize the human active actions. On the other hand, the spatial information about static human poses is valuable for human action recognition. Poselets were introduced as latent variables representing a configuration for mutual locations of body parts and allowing different views of description. In current research, some modifications of Speeded-Up Robust Features (SURF) invariant to affine geometrical transforms and illumination changes were tested. First, a grid of rectangles is imposed on object of interest in a still image. Second, sparse descriptor based on Gauge-SURF (G-SURF) invariant to color/lighting changes is constructed for each rectangle separately. A common Spatial POselet Descriptor (SPOD) aggregates the SPODs of rectangles with following random forest classification in order to receive fast classification results. The proposed approach was tested on samples from PASCAL Visual Object Classes (VOC) Dataset and Challenge 2010 providing accuracy 61-68% for all possible 3D poses locations and 82-86% for front poses locations regarding to nine action categories.

Dissociating effect of upper limb non-use and overuse on space and body representations

Accurate and updated representations of the space where the body acts, i.e. the peripersonal space (PPS), and the location and dimension of body parts (body representation, BR) are essential to perform actions. Because both PPS and BR are involved in motor execution and display the same plastic proprieties after the use of a tool to reach far objects, it has been suggested that they overlap in a unique representation of the body in a space devoted to action. Here we determined whether manipulating actions in space, without modifying body metrics, i.e. through immobilization, induces a dissociation of the plastic properties of PPS and BR. In 39 healthy subjects we evaluated PPS and BR for the non-used right limb and the overused left limb before and after 10h of right arm immobilization. We observed that non-use reduces PPS representation around the immobilized arm, without affecting the metric representation (i.e. perceived length) of that limb. In contrast, overuse modulates the metric representation of the free arm, leaving PPS unchanged around that limb. These results suggest that the plasticity in PPS and BR depends on different mechanisms; while PPS representation is shaped as a function of the dimension of the acting space, metric characteristics of BR are forged on a complex interplay between visual and sensorimotor information related to the body. This behavioral dissociation between PPS and BR defines a new scenario for the role of action in shaping space and body representations.

Bodypart localization for pressure ulcer prevention.

Pressure ulcers, commonly called bedsores, are defined as injuries to the skin and underlying tissue resulting from prolonged pressure usually on bony areas of the body, such as heels and hips. In order to alleviate pressure accumulation at high-risk regions, the current practice in clinics requires caregivers to reposition their patients every two hours. This relies on the engagement of nursing services and places large burdens on the caregivers. In this study, a bedsore monitoring method using Pictorial Structure models is introduced to localize pressure distributions of the body. A pilot study including 12 subjects reveals that the proposed method enables reliable localization of bodyparts with 89.8% accuracy in common lying postures.

BreathSens: A Continuous On-Bed Respiratory Monitoring System With Torso Localization Using an Unobtrusive Pressure Sensing Array.

The ability to continuously monitor respiration rates of patients in homecare or in clinics is an important goal. Past research showed that monitoring patient breathing can lower the associated mortality rates for long-term bedridden patients. Nowadays, in-bed sensors consisting of pressure sensitive arrays are unobtrusive and are suitable for deployment in a wide range of settings. Such systems aim to extract respiratory signals from time-series pressure sequences. However, variance of movements, such as unpredictable extremities activities, affect the quality of the extracted respiratory signals. BreathSens, a high-density pressure sensing system made of e-Textile, profiles the underbody pressure distribution and localizes torso area based on the high-resolution pressure images. With a robust bodyparts localization algorithm, respiratory signals extracted from the localized torso area are insensitive to arbitrary extremities movements. In a study of 12 subjects, BreathSens demonstrated its respiratory monitoring capability with variations of sleep postures, locations, and commonly tilted clinical bed conditions.

The ChaLearn gesture dataset (CGD 2011)

This paper describes the data used in the ChaLearn gesture challenges that took place in 2011/2012, whose results were discussed at the CVPR 2012 and ICPR 2012 conferences. The task can be described as: user-dependent, small vocabulary, fixed camera, one-shot-learning. The data include 54,000 hand and arm gestures recorded with an RGB-D $$\hbox {Kinect}^\mathrm{TM}$$ Kinect TM camera. The data are organized into batches of 100 gestures pertaining to a small gesture vocabulary of 8---12 gestures, recorded by the same user. Short continuous sequences of 1---5 randomly selected gestures are recorded. We provide man-made annotations (temporal segmentation into individual gestures, alignment of RGB and depth images, and body part location) and a library of function to preprocess and automatically annotate data. We also provide a subset of batches in which the user's horizontal position is randomly shifted or scaled. We report on the results of the challenge and distribute sample code to facilitate developing new solutions. The data, datacollection software and the gesture vocabularies are downloadable from http://gesture.chalearn.org . We set up a forum for researchers working on these data http://groups.google.com/group/gesturechallenge .

Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

Abstract Background For construction management, data collection is a critical process for gathering and measuring information for the evaluation and control of ongoing project performances. Taking into account that construction involves a significant amount of manual work, worker monitoring can play a key role in analyzing operations and improving productivity and safety. However, time-consuming tasks involved in field observation have brought up the issue of implementing worker observation in daily management practice. Methods In an effort to address the issue, this paper investigates the performances of a cost-effective and portable RGB-D sensor, based on recent research efforts extended from our previous study. The performance of an RGB-D sensor is evaluated in terms of (1) the 3D positions of the body parts tracked by the sensor, (2) the 3D rotation angles at joints, and (3) the impact of the RGB-D sensor’s accuracy on motion analysis. For the assessment, experimental studies were undertaken to collect motion capture datasets using an RGB-D sensor and a marker-based motion capture system, VICON, and to analyze errors as compared with the VICON used as the ground truth. As a test case, 25 trials of ascending and descending during ladder climbing were recorded simultaneously with both systems, and the resulting motion capture datasets (i.e., 3D skeleton models) were temporally and spatially synchronized for their comparison. Results Through the comparative assessment, we found a discrepancy of 10.7 cm in the tracked locations of body parts, and a difference of 16.2 degrees in rotation angles. However, motion detection results show that the inaccuracy of an RGB-D sensor does not have a considerable effect on action recognition in the experiment. Conclusions This paper thus provides insight into the accuracy of an RGB-D sensor on motion capture in various measures and directions of further research for the improvement of accuracy.

Motor imagery in spinal cord injury patients: Moving makes the difference

Both real action control and execution and motor imagery abilities require knowledge of the spatial location of body parts, in other words efference copy information and feedbacks from the sensory system (Frith etal., 2000, Philos. Trans. R. Soc. Lond. B. Biol. Sci., 355, 1771). Spinal cord injuries induce severe motor disability, due to a damage of the descending motor pathways (Cramer etal., 2007, Exp. Brain. Res., 177, 233). Patients' motor imagery competences are variably reported as either normal or defective (Decety & Boisson, 1990, Eur. Arch. Psychiatry Clin. Neurosci., 240, 39; Lacourse etal., 1999, Behav. Brain Sci., 104, 73). We explored biomechanical constraint effects in Spinal Cord Injury (SCI) patients, as they are considered the most reliable indexes of motor imagery abilities (Parsons, 1987b, Cogn. Psychol., 19, 178). Sixteen spinal cord injuries patients and 16 neurologically unimpaired subjects have been administered with (1) the Hand Laterality Task (HLT), in which subjects were asked to judge the laterality of a rotated hand; and (2) the Mirror Letter Discrimination Task (MLD), in which subjects were asked to judge if a rotated character was in its correct upright position or mirror-reversed form. Our patients did not present the effect of stimulus orientation, neither did they show any effect related to biomechanical constraints. Based on these data, the hypothesis is that SCI patients' performance may be ascribed to the use of a different strategy to solve the tasks, based on memory rather than on mental rotation.

U.S. Coast Guard Academy Injury and Risk Factor Study

Purpose : Injury rates and injury risk factors were examined for the first time among cadets undergoing Summer Warfare Annual Basic (SWAB) training at the U.S. Coast Guard Academy (USCGA). Methods : Participant s were 778 men and 286 women from four years of SWAB training. Before SWAB training, the subjects were administered a Physical Fitness Assessment (PFA) (2-minute maximal effortsit-ups and 1.5 mile run) andfunctionalmovement screening (FMS) test.Heightand weight were measured and, gender, age and ethnicity were obtained from administrative records. Training -related injuries were compiled from student medical records. The diagnosis, body part location, disposition, and limited duty days were recorded foreach clinic visit. Results:During the 8 weeks of SWAB training, 23.8% of the women and 18.4% of the men were injured at least once. Compared to the men, the odds of an overall injury