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Vinay Venkataraman
Arizona State University
17Publications
7H-index
131Citations
Publications 17
Newest
#1Anirudh Som (ASU: Arizona State University)H-Index: 2
#2Kowshik Thopalli (ASU: Arizona State University)H-Index: 1
Last.Pavan Turaga (ASU: Arizona State University)H-Index: 24
view all 6 authors...
Topological methods for data analysis present opportunities for enforcing certain invariances of broad interest in computer vision, including view-point in activity analysis, articulation in shape analysis, and measurement invariance in non-linear dynamical modeling. The increasing success of these methods is attributed to the complementary information that topology provides, as well as availability of tools for computing topological summaries such as persistence diagrams. However, persistence d...
#1Anirudh Som (ASU: Arizona State University)H-Index: 2
#2Narayanan Krishnamurthi (ASU: Arizona State University)H-Index: 4
Last.Pavan Turaga (ASU: Arizona State University)H-Index: 24
view all 5 authors...
We propose a nonparametric framework for analyzing and modeling dynamic postural shifts of human subjects. The postural shifts are represented in the phase space using time-delay embeddings and novel shape-theoretic features are extracted. The proposed multiscale descriptors are used as discriminative features to differentiate dynamical systems. The descriptors are simple and easy to compute, and model the multiscale characteristics of the attractor's multi-dimensional shape measurements. We dem...
#1Vinay Venkataraman (ASU: Arizona State University)H-Index: 7
#2Pavan Turaga (ASU: Arizona State University)H-Index: 24
This paper presents a shape-theoretic framework for dynamical analysis of nonlinear dynamical systems which appear frequently in several video-based inference tasks. Traditional approaches to dynamical modeling have included linear and nonlinear methods with their respective drawbacks. A novel approach we propose is the use of descriptors of the shape of the dynamical attractor as a feature representation of nature of dynamics. The proposed framework has two main advantages over traditional appr...
Sep 1, 2016 in ICIP (International Conference on Image Processing)
#1Vinay Venkataraman (ASU: Arizona State University)H-Index: 7
Last.Pavan Turaga (ASU: Arizona State University)H-Index: 24
view all 3 authors...
In this paper, we propose a novel framework for dynamical analysis of human actions from 3D motion capture data using topological data analysis. We model human actions using the topological features of the attractor of the dynamical system. We reconstruct the phase-space of time series corresponding to actions using time-delay embedding, and compute the persistent homology of the phase-space reconstruction. In order to better represent the topological properties of the phase-space, we incorporat...
Aug 1, 2016 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Anirudh Som (ASU: Arizona State University)H-Index: 2
#2Narayanan Krishnamurthi (ASU: Arizona State University)H-Index: 4
Last.Pavan Turaga (ASU: Arizona State University)H-Index: 24
view all 4 authors...
In this paper, we propose a computational framework using high-dimensional shape descriptors of reconstructed attractors of center-of-pressure (CoP) tracings collected from subjects with Parkinson's disease while performing dynamical posture shifts, to quantitatively assess balance impairment. Using a dataset collected from 60 subjects, we demonstrated that the proposed method outperforms traditional methods, such as dynamical shift indices and use of chaotic invariants, in assessment of balance...
Jun 1, 2016 in CVPR (Computer Vision and Pattern Recognition)
#1Rushil Anirudh (ASU: Arizona State University)H-Index: 6
#2Vinay Venkataraman (ASU: Arizona State University)H-Index: 7
Last.Pavan TuragaH-Index: 24
view all 4 authors...
Topological data analysis is becoming a popular way to study high dimensional feature spaces without any contextual clues or assumptions. This paper concerns itself with one popular topological feature, which is the number of d–dimensional holes in the dataset, also known as the Betti–d number. The persistence of the Betti numbers over various scales is encoded into a persistence diagram (PD), which indicates the birth and death times of these holes as scale varies. A common way to compare PDs i...
#1Vinay Venkataraman (ASU: Arizona State University)H-Index: 7
#2Pavan Turaga (ASU: Arizona State University)H-Index: 24
Last.Steven L. Wolf (Emory University)H-Index: 69
view all 8 authors...
In this paper, we propose a general framework for tuning component-level kinematic features using therapists’ overall impressions of movement quality, in the context of a home-based adaptive mixed reality rehabilitation (HAMRR) system. We propose a linear combination of nonlinear kinematic features to model wrist movement, and propose an approach to learn feature thresholds and weights using high-level labels of overall movement quality provided by a therapist. The kinematic features are chosen ...
Jan 1, 2016 in AAAI (National Conference on Artificial Intelligence)
#1Vinay Venkataraman (ASU: Arizona State University)H-Index: 7
#2Jonathan Lenchner (IBM)H-Index: 9
Last.Pavan TuragaH-Index: 24
view all 7 authors...
#1Michael Krzyzaniak (ASU: Arizona State University)H-Index: 1
#2Rushil Anirudh (ASU: Arizona State University)H-Index: 6
Last.Sha Xin Wei (ASU: Arizona State University)
view all 5 authors...
With the proliferation of wearable sensors, we have access to rich information regarding human movement that gives us insights into our daily activities like never before. In a sensor rich environment, it is desirable to build systems that are aware of human interactions by studying contextual information. In this paper, we attempt to quantify one such contextual cue - the connectedness of physical movement. Inspired by the Semblance of Typology Entrainments, we estimate the connectedness of tra...
#1Michael Baran (ASU: Arizona State University)H-Index: 5
#2Nicole Lehrer (ASU: Arizona State University)H-Index: 8
Last.Thanassis Rikakis (CMU: Carnegie Mellon University)H-Index: 16
view all 9 authors...
Interactive neurorehabilitation (INR) systems provide therapy that can evaluate and deliver feedback on a patient's movement computationally. There are currently many approaches to INR design and implementation, without a clear indication of which methods to utilize best. This article presents key interactive computing, motor learning, and media arts concepts utilized by an interdisciplinary group to develop adaptive, mixed reality INR systems for upper extremity therapy of patients with stroke....
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