HapticWe have been curious to understand why people tune the hand when they explore objects. Tuning involves variation of antagonistic muscle co-contraction, posture, and shape of the limbs. Intuitively, we can predict that these conditioning behaviors changes the way external forces are projected across different sensors in the body to extract different features in the stimulus. Here are some of our publications in this area:

  1. Tan, Yongxuan, Sibylle Rérolle, Thilina Dulantha Lalitharatne, Nejra Van Zalk, Rachael E. Jack, and Thrishantha Nanayakkara. "Simulating dynamic facial expressions of pain from visuo-haptic interactions with a robotic patient." Scientific reports, Nature Publishing Group 12, no. 1 (2022): 1-11. [PDF]
  2. Thilina D. Lalitharatne, Leone Costi, Ryman Hashem, Ilana Nisky, Rachael E. Jack, Thrishantha Nanayakkara, Fumiya Iida, “Face Mediated Human-Robot Interaction for Remote Medical Examination”, Scientific Reports, Nature Publishing Group, 2022
  3. Pilar Zhang Qiu, Yongxuan Tan, Oliver Thompson, Bennet Cobley, Thrishantha Nanayakkara, "Soft Tissue Characterisation Using a Novel Robotic Medical Percussion Device with Acoustic Analysis and Neural Networks", accepted for publication in IEEE Robotics and Automation Letters (RAL), 2022 [PDF]
  4. Gerolamo Carboni, Thrishantha Nanayakkara, Atsushi Takagi, Etienne Burdet, "Adapting the visuo-haptic perception through muscle coactivation", Scientific Reports, Nature Publication Group, 2021. [PDF]
  5. He, L., Herzig, N., de Lusignan, S., Scimeca, L., Maiolino, P., Iida, F., & Nanayakkara, T.  "An Abdominal Phantom with Tunable Stiffness Nodules and Force Sensing Capability for Palpation Training", IEEE Transactions on Robotics, 2020. [PDF]
  6. Luca Scimeca, Josie Hughes, Perla Maiolino, Liang He, Thrishantha Nanayakkara, Fumiya Iida, "Action Augmentation of Tactile Perception for Soft-Body Palpation", Soft Robotics, 2021 [PDF]
  7. Thilina Lalitharatne, Jacob Tan, Liang He, Florence Ching Ying Leong, Nejra Van Zalk, Simon De Lusignan, Fumiya Iida, Thrishantha Nanayakkara, “MorphFace: A Hybrid Morphable Face for a Robopatient”, IEEE Robotics and Automation Letters (RAL), 2020. [PDF][Media]
  8. Lalitharatne, Thilina Dulantha, Yongxuan Tan, Florence Leong, Liang He, Nejra Van Zalk, Simon de Lusignan, Fumiya Iida, and Thrishantha Nanayakkara. "Facial Expression Rendering in Medical Training Simulators: Current Status and Future Directions." IEEE Access (2020). [PDF]
  9. Nicolas Herzig, Liang he, Perla Maiolino, Sara-Adela Abad, Thrishantha Nanayakkara, “Conditioned haptic perception for 3D localization of nodules in soft tissue palpation with a variable stiffness probe”, PONE-D-20-03370R2, 2020. [PDF][Media][Data repository]
  10. Jelizaveta Konstantinova, Giuseppe Cotugno, Prokar Dasgupta, Kaspar Althoefer, Thrishantha Nanayakkara, “Palpation Force Modulation Strategies to Identify Hard Regions in Soft Tissue Organs”, PLoS ONE, PONE-D-16-31711R2 PDF
  11. J. Konstantinova, M. Li, M. Gautam, P. Dasgupta, K. Althoefer and T. Nanayakkara. “Behavioral Characteristics of Manual Palpation to Localize Hard Nodules in Soft Tissues”,  IEEE Transactions on Biomedical Engineering, pp. 1651 – 1659, vol. 16, no. 6, DOI: 10.1109/TBME.2013.22968772014. PDF
  12. J. Konstantinova, A. Jiang, K. Althoefer P. Dasgupta, and T. Nanayakkara. “Implementation of Tactile Sensing for Palpation in Robot-Assisted Minimally Invasive Surgery”, IEEE Sensors Journal, pp.2490 – 2501, vol. 14, no. 8, DOI: 10.1109/JSEN.2014.2325794, 2014. PDF
  13. Nantachai Sornkarn, Matthew Howard,Thrishantha Nanayakkara, “Internal Impedance Control Helps Information Gain in Embodied Perception”, in IEEE International Conference on Robotics and Automation (ICRA), pp.6685 – 6690, DOI: 10.1109/ICRA.2014.6907846, 2014. PDF
  14. Nantachai Sornkarn and Thrishantha Nanayakkara, “Can a soft robotic probe use stiffness control like a human finger to improve efficacy of haptic perception?”, in press, IEEE Transactions on Haptics, 2016. PDF
  15. Sornkarn N, Dasgupta P, Nanayakkara T (2016) Morphological Computation of Haptic Perception of a Controllable Stiffness Probe. PLoS ONE 11(6): e0156982. doi:10.1371/journal.pone.0156982 PDF
  16. Nantachai Sornkarn and Thrishantha Nanayakkara “The Efficacy of Interaction Behavior and Internal Stiffness Control for Embodied Information Gain in Haptic Perception”, IEEE International Conference on Robotics and Automation (ICRA), pp. 2657 – 2662, DOI: 10.1109/ICRA.2016.7487425, 2016. PDF
  17. Ranasinghe, A., Dasgupta, P., Nagar, A., & Nanayakkara, T. (2018). Human Behavioral Metrics of a Predictive Model Emerging During Robot-Assisted Following Without Visual Feedback. IEEE Robotics and Automation Letters3(3), pp. 2624-2631. PDF
  18. Ranasinghe A, Dasgupta P, Althoefer K, Nanayakkara T (2015) Identification of Haptic Based Guiding Using Hard Reins. PLoS ONE 10(7): e0132020. doi:10.1371/journal.pone.0132020. PDF
  19. Anuradha Ranasinghe, Jacques Pendars, Prokar Dasguptha, Kaspar Althoefer, Thrishantha Nanayakkara, “Salient Features of Haptic Based Guidance of People with Limited Vision Using Hard Reins”, IEEE Transactions on SMC – Cybernetics, pp. 568 – 579, DOI: 10.1109/TCYB.2015.2409772, 2015. PDF
  20. Song, Xiaojing, Hongbin Liu, Kaspar Althoefer, Thrishantha Nanayakkara, and Lakmal D. Seneviratne. “Efficient Break-Away Friction Ratio and Slip Prediction Based on Haptic Surface Exploration.” IEEE Transactions on Robotics, vol. 30, no. 1, 203 – 219, DOI: 10.1109/TRO.2013.22796302014. PDF





In this video, Elyse Marshall demonstrates how her haptic mouse works with a virtual patient to train physical examination. Florence Leong on her left developed an FEM based surrogate model to project tissue stress information on the virtual patient.

Haptic mouse for physical examination training