Contact InformationDanica Kulic


Biography Summary

Dana Kulić is an Associate Professor in the Electrical and Computer Engineering department and the Director of the Adaptive Systems Laboratory.

Professor Kulić’s research interests include human motion analysis and human-machine interaction, with applications to imitation learning, humanoid gait, learning control, affective movement and rehabilitation.

The aim of the imitation learning research is to develop algorithms for life-long, incremental learning of human motion patterns for humanoid and other robots. Professor Kulić and her team are developing incremental algorithms for automatically segmenting, clustering and organizing motion pattern primitives, which are observed from human demonstration. These algorithms can be applied to learn human motion for activity recognition during human-robot interaction, progress monitoring during rehabilitation and sports training, or skill transfer for automation.

Research Interests

  • Machine learning
  • Imitation learning
  • Incremental learning
  • Human motion analysis
  • Human-robot interaction
  • Perception of human emotion
  • Human motion tracking
  • Mechatronics
  • Mechatronics & Controls
  • Humanoid robots
  • Robotics
  • Advanced Manufacturing
  • Operational Artificial Intelligence


  • 2005, Doctorate, Mechanical Engineering, University of British Columbia
  • 1998, Bachelor's, Electromechanical Engineering, University of British Columbia
  • 1998, Master's, Electromechanical Engineering, University of British Columbia


  • BME 356 - Control Systems
    • Taught in 2018
  • ECE 486 - Robot Dynamics & Control
    • Taught in 2017
  • ECE 782 - Humanoid Robotics
    • Taught in 2018
* Only courses taught in the past 5 years are displayed.

Selected/Recent Publications

  • Lin, Jonathan Feng-Shun and Karg, Michelle and Kulić, Dana, Movement Primitive Segmentation for Human Motion Modeling: A Framework for Analysis, IEEE Transactions on Human-Machine Systems, 46(3), 2016, 325 - 339
  • Bonnet, Vincent and Joukov, Vladimir and Kulić, Dana and Fraisse, Philippe and Ramdani, Nacim and Venture, Gentiane, Monitoring of Hip and Knee Joint Angles Using a Single Inertial Measurement Unit During Lower Limb Rehabilitation, IEEE Sensors Journal, 16(6), 2016, 1557 - 1564
  • Lam, Agnes WK and Varona-Marin, Danniel and Li, Yeti and Fergenbaum, Mitchell and Kulić, Dana, Automated rehabilitation system: movement measurement and feedback for patients and physiotherapists in the rehabilitation clinic, Human--Computer Interaction, 31(3-4), 2016, 294 - 334
  • Freeman, Cecille and Kulić, Dana and Basir, Otman, An evaluation of classifier-specific filter measure performance for feature selection, Pattern Recognition, 48(5), 2015, 1812 - 1826
  • Gill, Rajan J and Kulić, Dana and Nielsen, Christopher, Spline path following for redundant mechanical systems, IEEE Transactions on Robotics, 31(6), 2015, 1378 - 1392
  • Karg, Michelle and Seiberl, Wolfgang and Kreuzpointner, Florian and Haas, Johannes-Peter and Kulić, Dana, Clinical Gait Analysis: Comparing Explicit State Duration HMMs Using a Reference-Based Index, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(2), 2015, 319 - 331
  • Houmanfar, Roshanak and Karg, Michelle and Kulić, Dana, Movement analysis of rehabilitation exercises: Distance metrics for measuring patient progress, , 2014
  • Samadani, Ali-Akbar and Gorbet, Rob and Kulić, Dana, Affective movement recognition based on generative and discriminative stochastic dynamic models, IEEE Transactions on Human-Machine Systems, 44(4), 2014, 454 - 467
  • Karg, Michelle and Venture, Gentiane and Hoey, Jesse and Kulić, Dana, Human movement analysis as a measure for fatigue: a hidden Markov-based approach, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(3), 2014, 470 - 481
  • Lin, Jonathan Feng-Shun and Kulić, Dana, Online segmentation of human motion for automated rehabilitation exercise analysis, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(1), 2014, 168 - 180

Graduate Studies