Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Cotton, R James"'
Autor:
Unger, Tim, Moslehian, Arash Sal, Peiffer, J. D., Ullrich, Johann, Gassert, Roger, Lambercy, Olivier, Cotton, R. James, Easthope, Chris Awai
Marker-based Optical Motion Capture (OMC) paired with biomechanical modeling is currently considered the most precise and accurate method for measuring human movement kinematics. However, combining differentiable biomechanical modeling with Markerles
Externí odkaz:
http://arxiv.org/abs/2411.14992
Autor:
Cotton, R. James, Seamon, Bryant A., Segal, Richard L., Davis, Randal D., Sahu, Amrita, McLeod, Michelle M., Celnik, Pablo, Ramey, Sharon L.
Precision rehabilitation offers the promise of an evidence-based approach for optimizing individual rehabilitation to improve long-term functional outcomes. Emerging techniques, including those driven by artificial intelligence, are rapidly expanding
Externí odkaz:
http://arxiv.org/abs/2411.03919
Video and wearable sensor data provide complementary information about human movement. Video provides a holistic understanding of the entire body in the world while wearable sensors provide high-resolution measurements of specific body segments. A ro
Externí odkaz:
http://arxiv.org/abs/2405.17368
Single camera 3D pose estimation is an ill-defined problem due to inherent ambiguities from depth, occlusion or keypoint noise. Multi-hypothesis pose estimation accounts for this uncertainty by providing multiple 3D poses consistent with the 2D measu
Externí odkaz:
http://arxiv.org/abs/2403.06164
Autor:
Cotton, R. James
Recent developments have created differentiable physics simulators designed for machine learning pipelines that can be accelerated on a GPU. While these can simulate biomechanical models, these opportunities have not been exploited for biomechanics r
Externí odkaz:
http://arxiv.org/abs/2402.17192
Gait analysis from videos obtained from a smartphone would open up many clinical opportunities for detecting and quantifying gait impairments. However, existing approaches for estimating gait parameters from videos can produce physically implausible
Externí odkaz:
http://arxiv.org/abs/2402.12676
Recent work on object-centric world models aim to factorize representations in terms of objects in a completely unsupervised or self-supervised manner. Such world models are hypothesized to be a key component to address the generalization problem. Wh
Externí odkaz:
http://arxiv.org/abs/2401.00057
Autor:
Cotton, R. James, Peyton, Colleen
Easy access to precise 3D tracking of movement could benefit many aspects of rehabilitation. A challenge to achieving this goal is that while there are many datasets and pretrained algorithms for able-bodied adults, algorithms trained on these datase
Externí odkaz:
http://arxiv.org/abs/2310.19441
Autor:
Cotton, R. James, Peiffer, J. D., Shah, Kunal, DeLillo, Allison, Cimorelli, Anthony, Anarwala, Shawana, Abdou, Kayan, Karakostas, Tasos
Publikováno v:
Ambient Inteligence for Healthcare workshop at MICCAI 2023
Markerless motion capture (MMC) is revolutionizing gait analysis in clinical settings by making it more accessible, raising the question of how to extract the most clinically meaningful information from gait data. In multiple fields ranging from imag
Externí odkaz:
http://arxiv.org/abs/2307.16321
Autor:
Cotton, R. James, DeLillo, Allison, Cimorelli, Anthony, Shah, Kunal, Peiffer, J. D., Anarwala, Shawana, Abdou, Kayan, Karakostas, Tasos
Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis. This could greatly benefit rehabilitation by enabling more accurate tracking of outcomes and providing mo
Externí odkaz:
http://arxiv.org/abs/2303.10654