Quantitative assessment of paretic limb dexterity and interlimb coordination during bilateral arm rehabilitation training

Characterization of Bimanual Cyclical Tasks from Single-trial EEG-fNIRS Measurements

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 low-dimensional 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 robot-assisted bimanual training.

Workspace Volume of Human Bimanual Precision Manipulation Influenced by the Wrist Configuration and Finger Combination

Bimanual precision manipulation is an essential ability in daily human lives. However, the kinematic ability of bimanual precision manipulation due to its complexity and randomness was rarely discussed. This study firstly presents an objective quantitative evaluation of bimanual precision manipulation based on workspace volume. It focuses on studying the effects of the wrist and finger factors on the bimanual manipulation abilities by measuring the workspaces through which ten participants manipulated an object under the 12 situations (3 wrist configurations × 4 finger combinations). The results show that the wrists participation significantly increases the workspace for bimanual precision manipulation, while different finger combinations also substantially affect workspace volume. Therefore, we found an optimal hand situation (two indexes cooperating with the wrists participation), allowing the workspace to reach a volume of 1600cm³, which is ten times higher than the worst situation. Furthermore, the involvement of the right thumb can significantly increase the contribution ratio of finger movement in bimanual precision manipulation, making the movement more accurate and stable. The study has the potential to contribute to the researches in many domains, ranging from developing surgical devices, training doctors in microsurgical techniques, providing normative data for rehabilitation.

Comparison of Machine Learning approaches for Classifying Upper Extremity Tasks in Individuals Post-Stroke

After a stroke, individuals often exhibit upper extremity (UE) motor dysfunction, influencing the performance of everyday tasks. Characterizing UE movements is useful to track recovery and response to intervention. Yet, due to the complexity of the recovery process, UE movements may be extremely variable and person-specific. While this renders automatic recognition of these gestures challenging, machine learning methods could be used to classify UE movements in atypical populations. In the current study, we utilize data from 20 individuals post-stroke and 20 age-matched controls to identify an optimal set of sensor-extracted features for the classification of unimanual and bimanual gestures during task performance. We found that using fewer than 100 features along with a random forest classifier produced the best performance across both groups, with both user-dependent and user-independent models.

Development of Sensor-Based Measures of Upper Extremity Interlimb Coordination

The development of motor impairment after the onset of an injury such as stroke may result in long-term compensatory behaviors. Because compensation often evolves in ambient settings (outside the purview of monitoring clinicians), there is a need for quantitative tools capable of accurately detecting the subtleties of compensation and related reduction in interlimb coordination. Improvement in interlimb coordination may serve as a marker of recovery from stroke, and rehabilitation progress. The current study investigates measures of upper extremity interlimb coordination in persons post-stroke and healthy controls. It introduces a novel algorithm for objective characterization of interlimb coordination during the performance of real-world tasks.

Roles of Force Cues and Proprioceptive Joint Angles in Active Exploration of Compliant Objects

We employ distinct exploratory procedures to improve our perceptual judgments of an object's properties. For instance, with respect to compliance, we exert pressure against a resisting force. The present work investigates ties between strategies for active control of the finger and resultant cues by which compliances may be discriminated. In particular, we employ elastic spheres that co-vary in compliance and radius, as these generate non-differentiable contact areas and are discriminable only in active touch with proprioceptive inputs. During human-subjects psychophysical experiments, we measure touch force, fingertip displacement, and joint kinematics. Two active touch paradigms are used, with and without a force constraint. First, in behaviorally-controlled situations that make force cues non-useful, the results indicate that participants can employ a force-matching strategy between the compliant objects and rely upon displacement-related cues to differentiate them. We show these cues are directly tied to a proprioception mechanism, specifically, the angle of the MCP joint. However, in the fully active paradigm, participants control displacements instead and discriminate via force-related cues. Similar to prior findings in passive touch, we find that force-related cues, likewise, are used in active touch for the optimal and efficient discrimination of compliant objects.

Short-term Efficacy of Hand-Arm Bimanual Intensive Training on Upper Arm Function in Acute Stroke Patients: A Randomized Controlled Trial

Background

Rehabilitation training during the acute phase of stroke (<48 h) markedly improves impaired upper-limb movement. Hand-arm bimanual intensive training (HABIT) represents an intervention that promotes improvements in upper extremity function in children with cerebral palsy. This study repurposed HABIT in acute stroke patients and assessed recovery of upper extremity function when compared with a conventional rehabilitation program (CRP).

Methods

In a randomized trial, 128 patients with acute stroke were assigned to the HABIT or the CRP groups. The primary endpoint was clinical motor functional assessment that was guided by the Fugl-Meyer motor assessment (FMA) and outcomes of the action research arm test (ARAT). The secondary endpoint was an improved neurophysiological evaluation according to the motor-evoked potential amplitude (AMP), resting motion threshold (RMT), and central motor conduction time (CMCT) scores over the 2-week course of therapy. In both groups, scores were evaluated at baseline, 1 week from commencing therapy, and post-therapy.

Results

After 2 weeks, the HABIT group showed improved scores as compared the CRP group for FMA (51.7 ± 6.44 vs. 43.5 ± 5.6, P < 0.001), ARAT (34.5 ± 6.2 vs. 33.3 ± 6.3, P = 0.022), and AMP (1.1 ± 0.1 vs. 1.0 ± 0.1, P < 0.001). However, CMCT (8.6 ± 1.0 vs. 9.1 ± 0.6, P = 0.054) and RMT (55.3 ± 4.2 vs. 57.5 ± 4.1, P = 0.088) were similar when comparing between groups.

Conclusion

HABIT significantly improved motor functional and neuro-physiological outcomes in patients with acute stroke, which suggested that HABIT might represent an improved therapeutic strategy as compared CRP.