Rehabilitation therapy plays an essential role in assisting people with stroke regain arm function. Upper extremity robot therapy offers a number of advantages over manual therapies, but can suffer from slacking behavior, where the user lets the robot guide their movements even when they are capable of doing so by themselves, representing a major barrier to reaching the full potential of robot-assist rehabilitation. This is a pilot clinical study that investigates the use of an electromyography-based adaptive assist-as-needed controller to avoid slacking behavior during robotic rehabilitation for people with stroke. The study involved a convenience sample of five individuals with chronic stroke who underwent a robot therapy program utilizing horizontal arm tasks. The Fugl-Meyer assessment (FM) was used to document motor impairment status at baseline. Velocity, time, and position were quantified as performance parameters during the training. Arm and shoulder surface electromyography (EMG) and electroencephalography (EEG) were used to assess the controller’s performance. The cross-sectional results showed strong second-order relationships between FM score and outcome measures, where performance metrics (path length and accuracy) were sensitive to change in participants with lower functional status. In comparison, speed, EMG and EEG metrics 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. This study highlights the importance of multi-sensor integration to monitor and improve upper-extremity robotic therapy.

(1) Background: The dynamics of hand tremors involve nonrandom and short-term motor patterns (STMPs). This study aimed to (i) identify STMPs in Parkinson’s disease (PD) and physiological resting tremor and (ii) characterize STMPs by amplitude, persistence, and regularity. (2) Methods: This study included healthy (N = 12, 60.1 ± 5.9 years old) and PD (N = 14, 65 ± 11.54 years old) participants. The signals were collected using a triaxial gyroscope on the dorsal side of the hand during a resting condition. Data were preprocessed and seven features were extracted from each 1 s window with 50% overlap. The STMPs were identified using the clustering technique k-means applied to the data in the two-dimensional space given by t-Distributed Stochastic Neighbor Embedding (t-SNE). The frequency, transition probability, and duration of the STMPs for each group were assessed. All STMP features were averaged across groups. (3) Results: Three STMPs were identified in tremor signals (p < 0.05). STMP 1 was prevalent in the healthy control (HC) subjects, STMP 2 in both groups, and STMP3 in PD. Only the coefficient of variation and complexity differed significantly between groups. (4) Conclusion: These results can help professionals characterize and evaluate tremor severity and treatment efficacy.

Background: 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. Methods: 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. Results: 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. Conclusion: The preliminary results were very promising; slacking was avoided for all participants during training with the aAAN controller.