18,484 research outputs found
What Will I Do Next? The Intention from Motion Experiment
In computer vision, video-based approaches have been widely explored for the
early classification and the prediction of actions or activities. However, it
remains unclear whether this modality (as compared to 3D kinematics) can still
be reliable for the prediction of human intentions, defined as the overarching
goal embedded in an action sequence. Since the same action can be performed
with different intentions, this problem is more challenging but yet affordable
as proved by quantitative cognitive studies which exploit the 3D kinematics
acquired through motion capture systems. In this paper, we bridge cognitive and
computer vision studies, by demonstrating the effectiveness of video-based
approaches for the prediction of human intentions. Precisely, we propose
Intention from Motion, a new paradigm where, without using any contextual
information, we consider instantaneous grasping motor acts involving a bottle
in order to forecast why the bottle itself has been reached (to pass it or to
place in a box, or to pour or to drink the liquid inside). We process only the
grasping onsets casting intention prediction as a classification framework.
Leveraging on our multimodal acquisition (3D motion capture data and 2D optical
videos), we compare the most commonly used 3D descriptors from cognitive
studies with state-of-the-art video-based techniques. Since the two analyses
achieve an equivalent performance, we demonstrate that computer vision tools
are effective in capturing the kinematics and facing the cognitive problem of
human intention prediction.Comment: 2017 IEEE Conference on Computer Vision and Pattern Recognition
Workshop
Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data
In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrapand k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead tocorrect classification rates with less than 10% of the original features
A quantitative taxonomy of human hand grasps
Background: A proper modeling of human grasping and of hand movements is fundamental for robotics,
prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific
literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively
justified.
Methods: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on
biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40
healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for
several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic)
taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific
taxonomies provide similar results despite describing different parameters of hand movements, one being muscular
and the other kinematic.
Results: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical
structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the
qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements
can be divided into five movement categories defined based on the overall grasp shape, finger positioning and
muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic
hand grasping synergies.
Conclusions: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what
has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
We present the first real-time method to capture the full global 3D skeletal
pose of a human in a stable, temporally consistent manner using a single RGB
camera. Our method combines a new convolutional neural network (CNN) based pose
regressor with kinematic skeleton fitting. Our novel fully-convolutional pose
formulation regresses 2D and 3D joint positions jointly in real time and does
not require tightly cropped input frames. A real-time kinematic skeleton
fitting method uses the CNN output to yield temporally stable 3D global pose
reconstructions on the basis of a coherent kinematic skeleton. This makes our
approach the first monocular RGB method usable in real-time applications such
as 3D character control---thus far, the only monocular methods for such
applications employed specialized RGB-D cameras. Our method's accuracy is
quantitatively on par with the best offline 3D monocular RGB pose estimation
methods. Our results are qualitatively comparable to, and sometimes better
than, results from monocular RGB-D approaches, such as the Kinect. However, we
show that our approach is more broadly applicable than RGB-D solutions, i.e. it
works for outdoor scenes, community videos, and low quality commodity RGB
cameras.Comment: Accepted to SIGGRAPH 201
- …