Robot-assisted bimanual training is promising to improve motor function and cortical reorganization for hemiparetic stroke patients. Closing the rehabilitation training loop with neurofeedback can help refine training protocols in time for better engagements and outcomes. However, due to the low signal-to-noise ratio (SNR) and non-stationary properties of neural signals, reliable characterization of bimanual training-induced neural activities from single-trial measurement is challenging. In this study, ten human participants were recruited conducting robot-assisted bimanual cyclical tasks (in-phase, 90 • out-of-phase, and anti-phase) when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. A unified EEG-fNIRS bimodal signal processing framework was proposed to characterize neural activities induced by three types of bimanual cyclical tasks. In this framework, novel artifact removal methods were used to improve the SNR and the task-related component analysis (TRCA) was introduced to increase the reproducibility of EEG-fNIRS bimodal features. The optimized features were transformed into lowdimensional indicators to reliably characterize bimanual training-induced neural activation. The SVM classification results of three bimanual cyclical tasks revealed a good discrimination ability of EEG-fNIRS bimodal indicators (90.1%), which was higher than that using EEG (74.8%) or fNIRS (82.2%) alone, supporting the proposed method as a feasible technique to characterize neural activities during robotassisted bimanual training.

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Background: Non-age-related gait kinematics and kinetics are essential indicators for gait with normal function. They are instrumental in clinical evaluation and assistant device design. However, only a few studies focus on non-age-related gait analysis. This study aims to identify the non-age-related gait kinematics and kinetics by comparing their normalized time-varying waveforms in the healthy elderly and young groups. Methods: Gait analysis for each gait cycle at self-paced is conducted from marker trajectories and ground reaction force. Pattern distance and percentage of significant difference between the young and elderly are calculated to represent the two sets of waveforms. The k-means clustering and elbow method are used to select and validate non-age-related kinematics and kinetics. The average waveforms with standard deviation are plotted for the comparison of the results. Results: There is no significant difference in weight and height between two aging groups. The elbow point is where the k value equals two. The cluster centers of the two groups are 0.1417 and 0.3691 while total Euclidean distances are 0.001794 and 0.02750, respectively. The two critical values closest to the cutoff are 0.1593 and 0.3037. The average waveforms of the non-age-related group are highly overlapped with a minor standard deviation between healthy young and elderly groups but show lager variations between healthy and abnormal groups. Conclusions: Ankle moment, knee angle, hip flexion angle, and hip adduction moment are identified as the non-age-related gait kinematic or kinetic features with distinguishing cutoff. These features are validated to reflect abnormal walking function, which is essential in the evaluation of mobility and functional ability of the elderly, and data fusion of the assistant device.

Background The change of gait kinematics and kinetics along aging were reported to indicate age-related gait patterns. However, few studies focus on non-age-related gait analysis. This study aims to explore the non-age-related gait kinematics and kinetics by comparing gait analysis outcomes among the healthy elderly and young subjects. Methods Gait analysis at self-paced was conducted on 12 healthy young subjects and 8 healthy elderly subjects. Kinematic and kinetic features of ankle, knee and hip joints were analyzed and compared in two groups. The degree of variation between the young and elderly in each kinematic or kinetic feature was calculated from pattern distance and percentage of significant difference. The k-means clustering and Elbow Method were applied to select and validate non-age-related features. The average waveforms with standard deviation were plotted for the comparison of the results. Results A total of five kinematic and five kinetic features were analyzed on ankle, knee and hip joints in healthy young and elderly groups. The degrees of variation in ankle moment, knee angle, hip flexion angle, and hip adduction moment were 0.1074, 0.1593, 0.1407, and 0.1593, respectively. The turning point was where the k value equals two. The clustering centers were 0.1417 and 0.3691, and the two critical values closest to the cutoff were 0.1593 and 0.3037. The average waveforms of the kinematic or kinetic features mentioned above were highly overlapped with a minor standard deviation between the healthy young and elderly but showed larger variations between the healthy and abnormal. Conclusions The cluster with a minor degree of variation in kinematic and kinetic features between the young and elderly were identified as non-age-related, including ankle moment, knee angle, hip flexion angle, and hip adduction moment. Non-age-related gait kinematics and kinetics are essential indicators for gait with normal function, which is essential in the evaluation of mobility and functional ability of the elderly, and data fusion of the assistant device.