Validation of a machine learning approach and control strategy for a rehabilitation robot to train the upper extremity in stroke patients.

Stroke is a disease that affects millions of people worldwide and which can result in long-lasting motor impairments. The resulting disabilities affect the performance of stroke patients when executing Activities of Daily Life (ADL). Post-treatment of the disease includes rehabilitation exercises, which are goal-oriented repetitive tasks aimed at restoring motor function of the affected body part. The field of rehabilitation robotics presents novel technology for the delivery of these exercises so as to reduce the workload of the clinician and in order to increase the amount of tasks per session. With that in mind, the eNHANCE device was designed to be used in upper extremity rehabilitation and assistance through reaching tasks. The goal of this thesis is to add functionality to the robotic arm of the eNHANCE device, so that assistance-as-needed is given during training of the upper extremity. In order to do so, two main concepts were addressed: the behavior of the robotic arm and the adjustment of the support level. The behavior of the robotic arm, on the one hand, concerns the assistance given by the robot, so that it resembles healthy performance in reaching tasks. To do so, a machine learning approach was evaluated to obtain a predicted healthy reaching time which would dictate the behavior of the robot. An experiment investigating different machine learning models and the use of different training dataset – Experiment I- was carried out so as to determine the validity of the machine learning approach in terms of prediction accuracy. The adjustment of the support level, on the other hand, is related to the motivational functionality of the device. In such a way, assistance-as-needed will increase user engagement and favor motor training. In order to address the adjustment of the support level, a support level controller was postulated. Later, an experimental set-up –Experiment II- observed the behavior of the controller for three different simulated scenarios: when the participants acted normally, fatigued or was lazy. In addition, user perception of the change in support level was documented. The conclusions from Experiment I led to the decision of choosing a Random Forest as a good candidate model. Furthermore, the features and tasks for the training dataset were specified, with a Base-to-Target task being favored. The final conclusion was that the Machine Learning approach is valid for limits of accuracy of less than 0.25 seconds. The conclusions from Experiment II prove that the proposed support level controller can adjust the support level depending on user contribution in the setting of the eNHANCE device. Furthermore, mean user perception was 50.8% accurate in determining support level change. The end result of the work presented in this thesis is a control strategy that combines the results from the robot behavior and the adjustment of support level. From the combined action of the machine learning model and the support level controller, assistance-as-needed is thus delivered in an upper extremity rehabilitation setting. To conclude, future lines of work addressing the limitations of this study were proposed. These included the full implementation of the control strategy, its integration with a motivational platform and the evaluation of the control strategy in an experiment involving stroke patients.