RESUMEN
Sleep is essential to boost the rehabilitation outcome as it facilitates motor learning, enhances cognitive performance, and improves mood and well-being. Rocking beds that provide vestibular stimulation may be a promising and non-invasive alternative to conventional pharmaceutical treatments for individuals with sleep problems, offering regenerative sleep without unwanted side effects. Previous research has shown that the effectiveness of the interventions is related to the chosen rocking acceleration. Moreover, the movement of the bed must be comfortable and smooth to avoid disturbing the user's sleep. Previously, the motor control parameters were tuned manually, which was time-consuming, subjective, and did not guarantee minimum deviation from the desired acceleration profile. In this work, we present an efficient and effective method using Gaussian processes to automatically tune the PI control parameters of a rocking bed moving along the longitudinal axis. We first simulated the kinematics of a rocking bed and optimized the control parameters for a chosen objective function that included the desired and the actual accelerations in the movement direction. We then compared the number of iterations needed to reach this objective for a model based on Gaussian processes and for a model based on a naive random exploration of the parameter space. Finally, we implemented the Gaussian process on the rocking bed to automatically tune the control parameters and subjectively compared them to the control parameters that were previously obtained after manual tuning. Our simulation showed that we can reach the control objective after a constant number of iterations using Gaussian processes, independent of the search space size. For the random search, the number of iterations increased quadratically with the size of the search space. The Gaussian process was found to be well transferable to the rocking bed. After less than one hour, control parameters were discovered that outperformed the previous parameters in terms of smoothness. However, despite the smoother motion, the noise emission from the motor, which was not part of the optimization, increased considerably. Our presented technique based on Gaussian processes significantly reduced the time and effort required to optimize the bed's control parameters compared to manual tuning. In future work, the control objective has to be refined to include noise emission as an optimization metric as low noise is an important aspect in sleep-related applications.