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 Ya Su, Yun Fu, Xinbo Gao and Qi Tian. "Discriminant Learning through Multiple Principal Angles for Visual Recognition," IEEE Transactions on Image Processing, v.21, 2012, p. 1381.

Recent Submissions

  • Kinect_ETM_HZ 

    Krovi, Venkat N. (2014)
  • Vicon_data_ETM_HZ 

    Aliakbar Alamdari; Seung Kook Jun; Venkat Krovi (2014-10-31)
  • IMU_data_ETM_HZ 

    Aliakbar Alamdari; Seung Kook Jun; Venkat Krovi (2014-10-31)
  • README 

    Aliakbar Alamdari; Seung Kook Jun; Venkat Krovi (2014-10-31)