Kernelized structural classification for 3D dogs body parts detection

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.

Energy expenditure, body-part discomfort and mental work load among nurses

The purpose of this prospective observational field study was to present a model for measuring energy expenditure among nurses and to determine if there was a difference between the energy expenditure of nurses providing direct care to adult patients on general medical-surgical units in two major metropolitan hospitals and a recommended energy expenditure of 3.0 kcal/minute over 8 hours. One-third of the predicted cycle ergometer VO2max for the study population was used to calculate the recommended energy expenditure.^ Two methods were used to measure energy expenditure among participants during an 8 hour day shift. First, the Energy Expenditure Prediction Program (EEPP) developed by the University of Michigan Center for Ergonomics was used to calculate energy expenditure using activity recordings from observation (OEE; n = 39). The second method used ambulatory electrocardiography and the heart rate-oxygen consumption relationship (HREE; n = 20) to measure energy expenditure. It was concluded that energy expenditure among nurses can be estimated using the EEPP. Using classification systems from previous research, work load among the study population was categorized as "moderate" but was significantly less than (p = 0.021) 3.0 kcal/minute over 8 hours or 1/3 of the predicted VO2max.^ In addition, the relationships between OEE, body-part discomfort (BPCDS) and mental work load (MWI) were evaluated. The relationships between OEE/BPCDS and OEE/MWI were not significant (p = 0.062 and 0.091, respectively). Among the study population, body-part discomfort significantly increased for upper arms, mid-back, lower-back, legs and feet by mid-shift and by the end of the shift, the increase was also significant for neck and thighs.^ The study also provided documentation of a comprehensive list of nursing activities. Among the most important findings were the facts that the study population spent 23% of the workday in a bent posture, walked an average of 3.14 miles, and spent two-thirds of the shift doing activities other than direct patient care, such as paperwork and communicating with other departments. A discussion is provided regarding the ergonomic implications of these findings. ^

Elements of the practice of physics, in two parts. Part I. Containing, the natural history of the human body. Part II. The history and methods of treating fevers, and internal inflammations.

Mode of access: Internet.

Postura da mão e imagética motora: um estudo sobre reconhecimento de partes do corpo

OBJETIVOS: Assim como a imagética motora, o reconhecimento de partes do corpo aciona representações somatosensoriais específicas. Essas representações são ativadas implicitamente para comparar o corpo com o estímulo. No presente estudo, investigou-se a influência da informação proprioceptiva da postura no reconhecimento de partes do corpo (mãos) e propõe-se a utilização dessa tarefa na reabilitação de pacientes neurológicos. MATERIAIS E MÉTODOS: Dez voluntários destros participaram do experimento. A tarefa era reconhecer a lateralidade de figuras da mão apresentada, em várias perspectivas e em vários ângulos de orientação. Para a figura da mão direita, o voluntário pressionava a tecla direita e para a figura da mão esquerda, a tecla esquerda. Os voluntários realizavam duas sessões: uma com as mãos na postura prona e outra com as mãos na postura supina. RESULTADOS: Os tempos de reação manual (TRM) eram maiores para as vistas e orientações, nas quais é difícil realizar o movimento real, mostrando que durante a tarefa, existe um acionamento de representações motoras para comparar o corpo com o estímulo. Além disso, existe uma influência da postura do sujeito em vistas e ângulos específicos. CONCLUSÕES: Estes resultados mostram que representações motoras são ativadas para comparar o corpo com o estímulo e que a postura da mão influencia esta ressonância entre estímulo e parte do corpo.

Turning configural processing upside down: Part and whole body postures

Like faces, body postures are susceptible to an inversion effect in untrained viewers. The inversion effect may be indicative of configural processing, but what kind of configural processing is used for the recognition of body postures must be specified. The information available in the body stimulus was manipulated. The presence and magnitude of inversion effects were compared for body parts, scrambled bodies, and body halves relative to whole bodies and to corresponding conditions for faces and houses. Results suggest that configural body posture recognition relies on the structural hierarchy of body parts, not the parts themselves or a complete template match. Configural recognition of body postures based on information about the structural hierarchy of parts defines an important point on the configural processing continuum, between recognition based on first-order spatial relations and recognition based on holistic undifferentiated template matching.

Combat sports analytics: Boxing punch classification using overhead depth imagery

In competitive combat sporting environments like boxing, the statistics on a boxer's performance, including the amount and type of punches thrown, provide a valuable source of data and feedback which is routinely used for coaching and performance improvement purposes. This paper presents a robust framework for the automatic classification of a boxer's punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through both a multi-class SVM and Random Forest classifiers. A coarse-to-fine hierarchical SVM classifier is presented based on prior knowledge of boxing punches. This framework has been applied to shadow boxing image sequences taken at the Australian Institute of Sport with 8 elite boxers. Results demonstrate the effectiveness of the proposed approach, with the hierarchical SVM classifier yielding a 96% accuracy, signifying its suitability for analysing athletes punches in boxing bouts.

Modulation of pain threshold by virtual body ownership

Background Appropriate sensorimotor correlations can result in the illusion of ownership of exogenous body parts. Nevertheless, whether and how the illusion of owning a new body part affects human perception, and in particular pain detection, is still poorly investigated. Recent findings have shown that seeing one’s own body is analgesic, but it is not known whether this effect is transferable to newly embodied, but exogenous, body parts. In recent years, results from our laboratory have demonstrated that a virtual body can be felt as one’s own, provided realistic multisensory correlations. Methods The current work aimed at investigating the impact of virtual body ownership on pain threshold. An immersive virtual environment allowed a first-person perspective of a virtual body that replaced the own. Passive movement of the index finger congruent with the movement of the virtual index finger was used in the “synchronous” condition to induce ownership of the virtual arm. The pain threshold was tested by thermal stimulation under four conditions: 1) synchronous movements of the real and virtual fingers, 2) asynchronous movements, 3) seeing a virtual object instead of an arm, and 4) not seeing any limb in real world. Results Our results show that, independently of attentional and stimulus adaptation processes, the ownership of a virtual arm per se can significantly increase the thermal pain threshold. Conclusions This finding may be relevant for the development and improvement of digital solutions for rehabilitation and pain treatment.

More than just a problem with faces: Altered body perception in a group of congenital prosopagnosics

It has been estimated that one out of forty people in the general population suffer from congenital prosopagnosia (CP), a neurodevelopmental disorder characterized by difficulty identifying people by their faces. CP involves impairment in recognising faces, although the perception of non-face stimuli may also be impaired. Given that social interaction does not only depend on face processing, but also the processing of bodies, it is of theoretical importance to ascertain whether CP is also characterised by body perception impairments. Here, we tested eleven CPs and eleven matched control participants on the Body Identity Recognition Task (BIRT), a forced-choice match-to-sample task, using stimuli that require processing of body, not clothing, specific features. Results indicated that the group of CPs was as accurate as controls on the BIRT, which is in line with the lack of body perception complaints by CPs. However the CPs were slower than controls, and when accuracy and response times were combined into inverse efficiency scores (IES), the group of CPs were impaired, suggesting that the CPs could be using more effortful cognitive mechanisms to be as accurate as controls. In conclusion, our findings demonstrate CP may not generally be limited to face processing difficulties, but may also extend to body perception

Viewing the body modulates tactile receptive fields

Tactile discrimination performance depends on the receptive field (RF) size of somatosensory cortical (SI) neurons. Psychophysical masking effects can reveal the RF of an idealized "virtual" somatosensory neuron. Previous studies show that top-down factors strongly affect tactile discrimination performance. Here, we show that non-informative vision of the touched body part influences tactile discrimination by modulating tactile RFs. Ten subjects performed spatial discrimination between touch locations on the forearm. Performance was improved when subjects saw their forearm compared to viewing a neutral object in the same location. The extent of visual information was relevant, since restricted view of the forearm did not have this enhancing effect. Vibrotactile maskers were placed symmetrically on either side of the tactile target locations, at two different distances. Overall, masking significantly impaired discrimination performance, but the spatial gradient of masking depended on what subjects viewed. Viewing the body reduced the effect of distant maskers, but enhanced the effect of close maskers, as compared to viewing a neutral object. We propose that viewing the body improves functional touch by sharpening tactile RFs in an early somatosensory map. Top-down modulation of lateral inhibition could underlie these effects.

Optokinetic stimulation induces illusory movement of both out-of-the-body and on-the-body hand-held visual objects

The coding of body part location may depend upon both visual and proprioceptive information, and allows targets to be localized with respect to the body. The present study investigates the interaction between visual and proprioceptive localization systems under conditions of multisensory conflict induced by optokinetic stimulation (OKS). Healthy subjects were asked to estimate the apparent motion speed of a visual target (LED) that could be located either in the extrapersonal space (visual encoding only, V), or at the same distance, but stuck on the subject's right index finger-tip (visual and proprioceptive encoding, V-P). Additionally, the multisensory condition was performed with the index finger kept in position both passively (V-P passive) and actively (V-P active). Results showed that the visual stimulus was always perceived to move, irrespective of its out- or on-the-body location. Moreover, this apparent motion speed varied consistently with the speed of the moving OKS background in all conditions. Surprisingly, no differences were found between V-P active and V-P passive conditions in the speed of apparent motion. The persistence of the visual illusion during the active posture maintenance reveals a novel condition in which vision totally dominates over proprioceptive information, suggesting that the hand-held visual stimulus was perceived as a purely visual, external object despite its contact with the hand.

A survey of human motion analysis using depth imagery

Analysis of human behaviour through visual information has been a highly active research topic in the computer vision community. This was previously achieved via images from a conventional camera, but recently depth sensors have made a new type of data available. This survey starts by explaining the advantages of depth imagery, then describes the new sensors that are available to obtain it. In particular, the Microsoft Kinect has made high-resolution real-time depth cheaply available. The main published research on the use of depth imagery for analysing human activity is reviewed. Much of the existing work focuses on body part detection and pose estimation. A growing research area addresses the recognition of human actions. The publicly available datasets that include depth imagery are listed, as are the software libraries that can acquire it from a sensor. This survey concludes by summarising the current state of work on this topic, and pointing out promising future research directions.

Supervised learning for labelling human body with attached props

 In this research, a novel method for generating training data of human postures with attached objects is proposed. The results has shown a significant increase in body-part classification accuracy for subjects with props from 60% to 94% using the generated image set

A description of the growth of the major body components of 2 broiler chicken strains

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modelling human behaviour and event detection from videos

Automatic analysis of human behaviour in large collections of videos is gaining interest, even more so with the advent of file sharing sites such as YouTube. However, challenges still exist owing to several factors such as inter- and intra-class variations, cluttered backgrounds, occlusion, camera motion, scale, view and illumination changes. This research focuses on modelling human behaviour for action recognition in videos. The developed techniques are validated on large scale benchmark datasets and applied on real-world scenarios such as soccer videos. Three major contributions are made. The first contribution is in the area of proper choice of a feature representation for videos. This involved a study of state-of-the-art techniques for action recognition, feature extraction processing and dimensional reduction techniques so as to yield the best performance with optimal computational requirements. Secondly, temporal modelling of human behaviour is performed. This involved frequency analysis and temporal integration of local information in the video frames to yield a temporal feature vector. Current practices mostly average the frame information over an entire video and neglect the temporal order. Lastly, the proposed framework is applied and further adapted to real-world scenario such as soccer videos. A dataset consisting of video sequences depicting events of players falling is created from actual match data to this end and used to experimentally evaluate the proposed framework.