Rowing Simulator Modulates Water Density to Foster Motor Learning

Ekin Basalp; Laura Marchal-Crespo; Georg Rauter; Robert Riener; Peter Wolf

doi:10.3389/frobt.2019.00074

Rowing Simulator Modulates Water Density to Foster Motor Learning

Front Robot AI. 2019 Aug 21:6:74. doi: 10.3389/frobt.2019.00074. eCollection 2019.

Authors

Ekin Basalp¹, Laura Marchal-Crespo^{1

2}, Georg Rauter^{1

3}, Robert Riener^{1

4}, Peter Wolf¹

Affiliations

¹ Sensory-Motor Systems Lab, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland.
² Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
³ BIROMED-Lab, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
⁴ Reharobotics Group, Spinal Cord Injury Center, Balgrist University Hospital, Medical Faculty, University of Zurich, Zurich, Switzerland.

Abstract

Although robot-assisted training is present in various fields such as sports engineering and rehabilitation, provision of training strategies that optimally support individual motor learning remains as a challenge. Literature has shown that guidance strategies are useful for beginners, while skilled trainees should benefit from challenging conditions. The Challenge Point Theory also supports this in a way that learning is dependent on the available information, which serves as a challenge to the learner. So, learning can be fostered when the optimal amount of information is given according to the trainee's skill. Even though the framework explains the importance of difficulty modulation, there are no practical guidelines for complex dynamic tasks on how to match the difficulty to the trainee's skill progress. Therefore, the goal of this study was to determine the impact on learning of a complex motor task by a modulated task difficulty scheme during the training sessions, without distorting the nature of task. In this 3-day protocol study, we compared two groups of naïve participants for learning a sweep rowing task in a highly sophisticated rowing simulator. During trainings, groups received concurrent visual feedback displaying the requested oar movement. Control group performed the task under constant difficulty in the training sessions. Experimental group's task difficulty was modulated by changing the virtual water density that generated different heaviness of the simulated water-oar interaction, which yielded practice variability. Learning was assessed in terms of spatial and velocity magnitude errors and the variability for these metrics. Results of final day tests revealed that both groups reduced their error and variability for the chosen metrics. Notably, in addition to the provision of a very well established visual feedback and knowledge of results, experimental group's variable training protocol with modulated difficulty showed a potential to be advantageous for the spatial consistency and velocity accuracy. The outcomes of training and test runs indicate that we could successfully alter the performance of the trainees by changing the density value of the virtual water. Therefore, a follow-up study is necessary to investigate how to match different density values to the skill and performance improvement of the participants.

Keywords: augmented feedback; contextual interference; functional task difficulty; motor learning; practice variability; robot-assisted training; sports engineering.