[Purpose] The effects of various rhythmic auditory stimulation tempos on stroke gait pattern changes when training patients with a smartphone-based rhythmic auditory stimulation application were investigated. [Subjects and Methods] Fifteen patients with chronic stroke were included. Cadence during comfortable walking was measured (baseline). After the baseline findings were recorded, rhythmic auditory stimulation with five different tempos (i.e., -10%, -5%, 0%, +5%, and +10% change from baseline) was randomly applied. Finally, comfortable walking without rhythmic auditory stimulation was initiated to evaluate gait pattern changes. [Results] As the tempo increased, the spatiotemporal gait parameters of the stroke patients changed significantly. Gait speed, cadence, and gait cycle duration showed the greatest improvement in the +10% rhythmic auditory stimulation condition compared to baseline. After gait training with rhythmic auditory stimulation, gait speed, cadence, stride length, gait cycle duration, and step length of the affected and unaffected sides improved significantly compared to baseline. [Conclusion] Significant changes in the gait pattern of stroke patients were noted for various tempos after training with rhythmic auditory stimulation. These findings could be used to customize rehabilitative gait training for patients who experience stroke with hemiplegia.

[Purpose] This study aimed to assess whether robotic rehabilitation can improve upper limb function, activities of daily living performance, and kinematic performance of chronic stroke survivors. [Subjects and Methods] Participants were 21 chronic stroke survivors (19 men; 60.8 years; Mini-Mental State Examination score: 28; onset duration: 10.2 years). Training exercises were performed with a Whole Arm Manipulator and a 120-inch projective display to provide visual and auditory feedback. Once the training began, red and grey balls appeared on the projective display, and participants performed reaching movements, in the assist-as-needed mode, toward 6 directional targets in a 3-dimensional space. All participants received training for 40 minutes per day, thrice per week, for 6 weeks. Main outcome measures were upper limb function (Fugl-Meyer Assessment, Action Research Arm Test, and Box and Blocks Test scores), activities of daily living performance (Modified Barthel Index), and kinematic performance (movement velocity) in 6 directions. [Results] After 6 weeks, significant improvement was observed in upper limb function, activities of daily living performance, and kinematic performance. [Conclusion] This study demonstrated the positive effects of robotic rehabilitation on upper limb function, activities of daily living performance, and kinematic performance in chronic stroke survivors.

Trunk rehabilitation exercises such as those for remediating core stability can help improve the seated balance of patients with weakness or loss of proprioception caused by diseases such as stroke, and aid the recovery of other functions such as gait. However, there has not yet been any reported method for automatically determining the parameters that define exercise difficulty on a trunk rehabilitation robot (TRR) based on data such as the patient’s demographic information, balancing ability, and training sequence, etc. We have proposed a machine learning (ML)-based difficulty adjustment method to determine an appropriate virtual damping gain ( D_virtual ) of the controller for the TRR’s unstable training mode. Training data for the proposed system is obtained from 37 healthy young adults, and the trained ML model thus obtained is tested through experiments with a separate population of 25 healthy young adults. The leave-one-out cross validation results (37 subjects) from the training group for validation of the designed ML model showed 80.90% average accuracy (R2 score) for using the given information to predict the desired difficulty levels, which are represented by the level of balance performance quantified as Mean Velocity Displacement (MVD) of the center of pressure. Statistical analysis (Repeated measures analysis of variance) of subject performance also showed that ground truth difficulty levels from the training data and predicted difficulty levels did not differ significantly under any of the three exercise modes used in this study (Hard, Medium, and Easy), and the standard deviations were reduced by 16.39, 41.39, and 25.68%, respectively. Moreover, the Planar Deviation (PD) of the center of pressure, which was not the target parameter here, also showed results similar to the MVD, which indicates that the predicted D_virtual affected the difficulty level of balance performance. Therefore, the proposed ML model-based difficulty adjustment method has potential for use with people who have varied balancing abilities.