A Comprehensive Analysis of a Novel Real-time Adaptive Assist-as-needed Controller for Robot-assisted Rehabilitation for Stroke Patients

摘要

<title>Abstract</title> <bold>Background</bold>: Rehabilitation therapy plays an essential role in assisting stroke patients in regaining function. For this reason, many studies have been conducted to optimize rehabilitation interventions to improve effectiveness and efficiency. In this context, robotic devices for rehabilitation and assistance can be effective. Several studies have demonstrated that using a robot as a therapy tool can significantly reduce motor impairment. However, the slacking behavior, in which the patient lets the robot guide their movements even when they are capable of doing so by themselves, has been identified as a major barrier to reaching the full potential of robot-assist rehabilitation. This paper developed a novel electromyographical-based adaptive assist-as-needed (aAAN) controller aiming to avoid this slacking behavior. <bold>Methods</bold>: Five stroke patients were recruited to test the controller. Motor impairment status was documented with the Fugl-Meyer (FMA) assessment. In this experiment, horizontal arms tasks were conducted with the robot off and on to assess the subject’s performance in both scenarios. Velocity, time, and position were quantified as performance parameters during the training. Arm and shoulder EMG and electroencephalography (EEG) were used to assess the performance of the controller. <bold>Results</bold>: The cross-sectional results showed strong second-order relationships between Fugl-Meyer score and outcome measures, where performance metrics (path length and accuracy) were sensitive to change in participants with lower functional status. In comparison, speed and electrophysiological metrics (EMG and EEG) were more sensitive to change in participants with higher functional status. EEG signal amplitude increased when the robot suggested that the robot was inducing a challenge during the training tasks. <bold>Conclusion</bold>: The preliminary results were very promising; slacking was avoided for all participants during training with the aAAN controller.