Augmented haptic feedback system to promote motor learning of a bicycle task: A literature review
Creators: name: Draukšas, Simonas
Publisher: name: figshare
Year: 2022
Subjects: subject: 90602 Control Systems, Robotics and Automation
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: 90305 Rehabilitation Engineering
subject: FOS: Medical engineering

description: The literature review was conducted as part of Master of Science in Mechanical Engineering studies at TU Delft. The author of this literature review thinks that robot-mediated training is a promising technology, which could accelerate motor learning for novices, provide experts with a safe and controlled training ground, and provide training without requiring as much human intervention. However, current literature cannot yet prove that robot-mediation is more effective than conventional practice, with some literature even showing lower efficacies. In order to reduce the chance of a newly developed training system to be detrimental to learning, this literature review tries to answer the following research question: <em>What needs to be considered when designing a haptic-based system to promote motor learning of a dynamic task, such as riding a bicycle?</em> At first, the author identifies and describes different methods of haptic feedback that can be found in literature. Literature shows that the effectiveness of haptic feedback largely depends on the person’s skill in the task trained, the complexity of the task, and, possibly, task’s rhythmicity. It also suggests that haptic guidance improves learning of temporal aspects while haptic disturbance improves spatial aspects of the task. Additionally, augmented haptic feedback needs to be intuitive and easily understood by the user. The review then looks into some mechanisms employed by humans to find out how humans build internal task models. For simple tasks, error-based and observational learning methods dominate, which can be exploited by using haptic disturbance and guidance, respectively. In complex tasks, especially those dealing with dynamic systems, exploratory learning can improve performance above error-based or observational learning, therefore movement variability becomes important. Then, the author reviews the controllers used in haptic feedback systems. Most of the systems described in literature use simple controllers, such as potential fields or Proportional-Derivative (PD) controllers. However, a couple of recent papers propose the use of more advanced controllers based on Model Predictive Control (MPC). While simpler controllers are easier to implement and require less computational power, they also restrict user’s freedom to explore and could be detrimental to learning of complex dynamic tasks. Lastly, the author forms recommendations for a haptic system that would be used to teach bicycle riding task. The author comes up with three approaches. The first approach deals with expert riders and uses haptic disturbance to provide a safe manner of training bicycle riding under unpredictable conditions, such as windy weather. Second and third approaches deal with novice riders. The first of the two improves over training wheels by using MPC optimization to minimize guidance forces and only act when an unsafe situation is predicted. This should allow for the rider to experience unchanged bicycle dynamics. The second of the two mimics the adapted bicycle approach, but without changing the geometry of the bicycle and instead using a controller to change the dynamics of the bicycle and make it more stable. This approach would be able to match the task difficulty to user’s skill level, but could have a negative effect on user’s sense of agency.