22,653 research outputs found
Gait recognition with shifted energy image and structural feature extraction
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper, we present a novel and efficient gait recognition system. The proposed system uses two novel gait representations, i.e., the shifted energy image and the gait structural profile, which have increased robustness to some classes of structural variations. Furthermore, we introduce a novel method for the simulation of walking conditions and the generation of artificial subjects that are used for the application of linear discriminant analysis. In the decision stage, the two representations are fused. Thorough experimental evaluation, conducted using one traditional and two new databases, demonstrates the advantages of the proposed system in comparison with current state-of-the-art systems
Unsupervised Discovery of Parts, Structure, and Dynamics
Humans easily recognize object parts and their hierarchical structure by
watching how they move; they can then predict how each part moves in the
future. In this paper, we propose a novel formulation that simultaneously
learns a hierarchical, disentangled object representation and a dynamics model
for object parts from unlabeled videos. Our Parts, Structure, and Dynamics
(PSD) model learns to, first, recognize the object parts via a layered image
representation; second, predict hierarchy via a structural descriptor that
composes low-level concepts into a hierarchical structure; and third, model the
system dynamics by predicting the future. Experiments on multiple real and
synthetic datasets demonstrate that our PSD model works well on all three
tasks: segmenting object parts, building their hierarchical structure, and
capturing their motion distributions.Comment: ICLR 2019. The first two authors contributed equally to this wor
Body image distortions following spinal cord injury
Background: Following spinal cord injury (SCI) or anaesthesia, people may continue to experience feelings of the size, shape, and posture of their body, suggesting that the conscious body image is not fully determined by immediate sensory signals. How this body image is affected by changes in sensory inputs from, and motor outputs to the body remains unclear.
Methods: We tested paraplegic and tetraplegic SCI patients on a task that yields quantitative measures of body image. Participants were presented with an anchoring stimulus on a computer screen and told to imagine that the displayed body part was part of a standing mirror image of themselves. They then identified the position on the screen, relative to the anchor, where each of several parts of their body would be located. Veridical body dimensions were identified based on measurements and photographs of participants.
Results: Compared to age-matched controls, paraplegic and tetraplegic patients alike perceived their torso and limbs as elongated relative to their body width. No effects of lesion level were found.
Conclusions: The common distortions in body image across patient groups, despite differing SCI levels, imply that a body image may be maintained despite chronic sensory and motor loss. Systematic alterations in body image follow SCI, though our results suggest these may reflect prolonged changes in body posture and wheelchair use, rather than loss of specific sensorimotor pathways. These findings provide new insight into how the body image is maintained, and may prove useful in treatments that intervene to manipulate the body image
Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors
Robot awareness of human actions is an essential research problem in robotics
with many important real-world applications, including human-robot
collaboration and teaming. Over the past few years, depth sensors have become a
standard device widely used by intelligent robots for 3D perception, which can
also offer human skeletal data in 3D space. Several methods based on skeletal
data were designed to enable robot awareness of human actions with satisfactory
accuracy. However, previous methods treated all body parts and features equally
important, without the capability to identify discriminative body parts and
features. In this paper, we propose a novel simultaneous Feature And Body-part
Learning (FABL) approach that simultaneously identifies discriminative body
parts and features, and efficiently integrates all available information
together to enable real-time robot awareness of human behaviors. We formulate
FABL as a regression-like optimization problem with structured
sparsity-inducing norms to model interrelationships of body parts and features.
We also develop an optimization algorithm to solve the formulated problem,
which possesses a theoretical guarantee to find the optimal solution. To
evaluate FABL, three experiments were performed using public benchmark
datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter
robot in practical assistive living applications. Experimental results show
that our FABL approach obtains a high recognition accuracy with a processing
speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method
to enable real-time robot awareness of human behaviors in practical robotics
applications.Comment: 8 pages, 6 figures, accepted by ICRA'1
Cascaded 3D Full-body Pose Regression from Single Depth Image at 100 FPS
There are increasing real-time live applications in virtual reality, where it
plays an important role in capturing and retargetting 3D human pose. But it is
still challenging to estimate accurate 3D pose from consumer imaging devices
such as depth camera. This paper presents a novel cascaded 3D full-body pose
regression method to estimate accurate pose from a single depth image at 100
fps. The key idea is to train cascaded regressors based on Gradient Boosting
algorithm from pre-recorded human motion capture database. By incorporating
hierarchical kinematics model of human pose into the learning procedure, we can
directly estimate accurate 3D joint angles instead of joint positions. The
biggest advantage of this model is that the bone length can be preserved during
the whole 3D pose estimation procedure, which leads to more effective features
and higher pose estimation accuracy. Our method can be used as an
initialization procedure when combining with tracking methods. We demonstrate
the power of our method on a wide range of synthesized human motion data from
CMU mocap database, Human3.6M dataset and real human movements data captured in
real time. In our comparison against previous 3D pose estimation methods and
commercial system such as Kinect 2017, we achieve the state-of-the-art
accuracy
Basic gestures as spatiotemporal reference frames for repetitive dance/music patterns in samba and charleston
THE GOAL OF THE PRESENT STUDY IS TO GAIN BETTER insight into how dancers establish, through dancing, a spatiotemporal reference frame in synchrony with musical cues. With the aim of achieving this, repetitive dance patterns of samba and Charleston were recorded using a three-dimensional motion capture system. Geometric patterns then were extracted from each joint of the dancer's body. The method uses a body-centered reference frame and decomposes the movement into non-orthogonal periodicities that match periods of the musical meter. Musical cues (such as meter and loudness) as well as action-based cues (such as velocity) can be projected onto the patterns, thus providing spatiotemporal reference frames, or 'basic gestures,' for action-perception couplings. Conceptually speaking, the spatiotemporal reference frames control minimum effort points in action-perception couplings. They reside as memory patterns in the mental and/or motor domains, ready to be dynamically transformed in dance movements. The present study raises a number of hypotheses related to spatial cognition that may serve as guiding principles for future dance/music studies
Numerical simulation of electrocardiograms for full cardiac cycles in healthy and pathological conditions
This work is dedicated to the simulation of full cycles of the electrical
activity of the heart and the corresponding body surface potential. The model
is based on a realistic torso and heart anatomy, including ventricles and
atria. One of the specificities of our approach is to model the atria as a
surface, which is the kind of data typically provided by medical imaging for
thin volumes. The bidomain equations are considered in their usual formulation
in the ventricles, and in a surface formulation on the atria. Two ionic models
are used: the Courtemanche-Ramirez-Nattel model on the atria, and the "Minimal
model for human Ventricular action potentials" (MV) by Bueno-Orovio, Cherry and
Fenton in the ventricles. The heart is weakly coupled to the torso by a Robin
boundary condition based on a resistor- capacitor transmission condition.
Various ECGs are simulated in healthy and pathological conditions (left and
right bundle branch blocks, Bachmann's bundle block, Wolff-Parkinson-White
syndrome). To assess the numerical ECGs, we use several qualitative and
quantitative criteria found in the medical literature. Our simulator can also
be used to generate the signals measured by a vest of electrodes. This
capability is illustrated at the end of the article
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