9.01 Robotic light touch support during locomotion in balance impaired humans (ROLITOS)


In clinical settings, caregivers provide manual support during locomotion to patients with impaired body balance. Thus, interpersonal manual support represents an ecologically valid and effective strategy for controlling a patients' fall risk in dynamic postural activities.

From a therapeutic point of view, however, restricting patient's movement degrees of freedom by grasping his body to support his weight has to be considered inadequate for the purpose of practicing own control of body balance. A more promising strategy is balance support provided in a 'light touch' fashion, for example by lightly resting a hand on the back or shoulder of a patient without taking patient's weight.

We like to ask which are the qualities that make an expert healthcare provider so efficient in the provision of adaptive manual balance support? We believe the answer is associated with the ability to anticipate a patient's dynamics. The scientific aim of the research project is to improve the understanding of interpersonal dynamics of light touch in general and the caregiver-patient interaction during light interpersonal touch stabilisation in particular. Our engineering aim is the translation of the principles of human-to-human interpersonal coordination for light tactile balance support into a robotic solution.

Major outcomes will be measures of the contact receiver's postural stability and interpersonal coordination with the provider but also autonomic measures of the receiver's state anxiety of falling. The latter will express human acceptance of the robotic balance support.

Model of the interpersonal light touch support during maximum forward reach.
Figure 3a shows the participants in the Baseline position at the start of a trial.
Figure 3b shows the participants during the dynamic execution of a trial.


Steinl, S. & Johannsen, L. (2017) Interpersonal interactions for haptic guidance during maximum forward reaching, Gait & Posture, 53, 17-24. doi: 10.1016/j.gaitpost.2016.12.029

K. Hu, C. Ott and D. Lee (2016) Learning and Generalization of Compensative Zero-Moment Point Trajectory for Biped Walking, IEEE Transactions on Robotics, vol. 32, no. 3, pp. 717-725. doi: 0.1109/TRO.2016.2553677

Soloperto, R., Saveriano, M., Lee, D. (2015) A bidirectional invariant representation of motion for gesture recognition and reproduction." In Robotics and Automation (ICRA), IEEE International Conference , pp. 6146-6152). doi: 10.1109/ICRA.2015.7140062

Kai Hu, C. Ott and Dongheui Lee (2015) Online iterative learning control of zero-moment point for biped walking stabilization, IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, pp. 5127-5133. doi: 10.1109/ICRA.2015.7139913

Saveriano, M., Lee, D. (2014) Learning motion and impedance behaviors from human demonstrations, Ubiquitous Robots and Ambient Intelligence (URAI), 11th International Conference, pp. 368-373. doi: 10.1109/URAI.2014.7057371

Kai Hu, Christian Ott, Dongheui Lee (2014) Online human walking imitation in task and joint space based on quadratic programming K Hu, C Ott, D Lee - Robotics and Automation (ICRA), IEEE. doi: 10.1109/ICRA.2014.6907357

Saveriano, M., Lee, D. (2014) Distance based dynamical system modulation for reactive avoidance of moving obstacles, Robotics and Automation (ICRA), IEEE International Conference, pp. 5618-5623), doi: 10.1109/ICRA.2014.6907685

Saveriano, M., Lee, D. (2013) Invariant representation for user independent motion recognition, RO-MAN, IEEE, pp. 650-655. doi: 10.1109/ROMAN.2013.6628422


Project team leader

Ferdinand Tusker
Chair of Human Movement Science

Doctoral researcher

Saskia Steinl
Chair of Human Movement Science

Doctoral researcher

David Kaulmann
Chair of Human Movement Science

Principal investigator

Professor Joachim Hermsdörfer
Chair of Human Movement Science

Principal investigator

Professor Dongheui Lee
Dynamic Human-Robot-Interaction for Automation Systems