Sylvia J. Spengler CNS Division Of Computer and Network Systems
CSE Direct For Computer & Info Scie & Enginr
August 29, 2012
November 30, 2016 (Estimated)
Awarded Amount to Date:
Yun Fu email@example.com (Principal Investigator)
360 HUNTINGTON AVE
INFORMATION TECHNOLOGY RESEARC,
CYBER-PHYSICAL SYSTEMS (CPS)
Program Reference Code(s):
1640, 7752, 7918, 7924, 9178, 9251
Program Element Code(s):
The objective of this research is to develop a comprehensive theoretical and experimental cyber-physical framework to enable intelligent human-environment interaction capabilities by a synergistic combination of computer vision and robotics. Specifically, the approach is applied to examine individualized remote rehabilitation with an intelligent, articulated, and adjustable lower limb orthotic brace to manage Knee Osteoarthritis, where a visual-sensing/dynamical-systems perspective is adopted to: (1) track and record patient/device interactions with internet-enabled commercial-off-the-shelf computer-vision-devices; (2) abstract the interactions into parametric and composable low-dimensional manifold representations; (3) link to quantitative biomechanical assessment of the individual patients; (4) facilitate development of individualized user models and exercise regimen; and (5) aid the progressive parametric refinement of exercises and adjustment of bracing devices. This research and its results will enable us to understand underlying human neuro-musculo-skeletal and locomotion principles by merging notions of quantitative data acquisition, and lower-order modeling coupled with individualized feedback. Beyond efficient representation, the quantitative visual models offer the potential to capture fundamental underlying physical, physiological, and behavioral mechanisms grounded on biomechanical assessments, and thereby afford insights into the generative hypotheses of human actions.
Knee osteoarthritis is an important public health issue, because of high costs associated with treatments. The ability to leverage a quantitative paradigm, both in terms of diagnosis and prescription, to improve mobility and reduce pain in patients would be a significant benefit. Moreover, the home-based rehabilitation setting offers not only immense flexibility, but also access to a significantly greater portion of the patient population. The project is also integrated with extensive educational and outreach activities to serve a variety of communities.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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Yu Kong, Yunde Jia, and Yun Fu. "Interactive Phrases: Semantic Descriptions for Human In-teraction Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), v.36, 2014, p. 1775.
Kang Li and Yun Fu. "Prediction of Human Activity by Discovering Temporal Sequence Patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), v.36, 2014, p. 1644.
Ming Shao, Dmitry Kit, and Yun Fu. "Generalized Transfer Subspace Learning through Low-Rank Constraint," International Journal of Computer Vision (IJCV), v.109, 2014, p. 74.
Liangyue Li, Sheng Li and Yun Fu. "Learning Low-Rank and Discriminative Dictionary for Image Classification," Image and Vision Computing (IVC), v.32, 2014, p. 814.
Jun, S., Zhou, X., Ramsey, D., and Krovi, V.. "Smart Knee Brace Design with Parallel Coupled Compliant Plate (PCCP) Mechanism and Pennate Elastic Band (PEB) Spring," ASME Journal of Mechanisms and Robotics, 2014.
Ya Su, Sheng Li, Shengjin Wang, and Yun Fu. "Submanifold Decomposition," IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), 2014.
Yuan Yao and Yun Fu. "Contour Model based Hand-Gesture Recognition Using Kinect Sensor," IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), 2014.
Jun, S.K., Zhou, X., Ramsey, D.K., and Krovi, V.. "Smart Knee Brace Design with Parallel Coupled Compliant Plate (PCCP) Mechanism and Pennate Elastic Band (PEB) Spring," ASME Journal of Mechanisms and Robotics, v.7, 2015, p. 041024-04.
Zhou, X., Jun, S.K. and Krovi, V.. "A Cable-based Active Variable Stiffness Module with Decoupled Tension," ASME Journal of Mechanisms and Robotics, v.7, 2015.
Zhou, X., Jun, S.K. and Krovi, V.. "Tension Distribution Shaping via Reconfigurable Attachment in Planar Mobile Cable Robots," Robotica, v.32, 2014.
 Zhengming Ding, Ming Shao, and Yun Fu. "Missing Modality Transfer Learning via Latent Low-Rank Constraint," IEEE Transactions on Image Processing, v.24, 2015, p. 4322.
Sheng Li and Yun Fu. "Learning Balanced and Unbalanced Graphs via Low-Rank Coding," IEEE Transactions on Knowledge and Data Engineering, v.27, 2015, p. 1274.