Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review

Uncovering the Neural Mechanisms of Inter-Hemispheric Balance Restoration in Chronic Stroke Through EMG-Driven Robot Hand Training: Insights From Dynamic Causal Modeling

EMG-driven robot hand training can facilitate motor recovery in chronic stroke patients by restoring the interhemispheric balance between motor networks. However, the underlying mechanisms of reorganization between interhemispheric regions remain unclear. This study investigated the effective connectivity (EC) between the ventral premotor cortex (PMv), supplementary motor area (SMA), and primary motor cortex (M1) using Dynamic Causal Modeling (DCM) during motor tasks with the paretic hand. Nineteen chronic stroke subjects underwent 20 sessions of EMG-driven robot hand training, and their Action Reach Arm Test (ARAT) showed significant improvement ( β =3.56, [Formula: see text]). The improvement was correlated with the reduction of inhibitory coupling from the contralesional M1 to the ipsilesional M1 (r=0.58, p=0.014). An increase in the laterality index was only observed in homotopic M1, but not in the premotor area. Additionally, we identified an increase in resting-state functional connectivity (FC) between bilateral M1 ( β =0.11, p=0.01). Inter-M1 FC demonstrated marginal positive relationships with ARAT scores (r=0.402, p=0.110), but its changes did not correlate with ARAT improvements. These findings suggest that the improvement of hand functions brought about by EMG-driven robot hand training was driven explicitly by task-specific reorganization of motor networks. Particularly, the restoration of interhemispheric balance was induced by a reduction in interhemispheric inhibition from the contralesional M1 during motor tasks of the paretic hand. This finding sheds light on the mechanistic understanding of interhemispheric balance and functional recovery induced by EMG-driven robot training.

Active Range of Motion Measurement System Using an Optical Sensor to Evaluate Hand Functions

Impairment of hand function greatly affects the independence of a human being. Proper assessment of hand function before and after any treatment for functional restoration is important to decide better treatment strategies. Despite traditional techniques of hand function evaluation, individual joint based assessment is vital to better track the details of the hand function. Current clinical assessments with goniometers are labour intensive, cumbersome and highly depend on the skill level of the practitioner. This study introduces an active range of motion (AROM) measurement system to measure individual range of motion of finger joints using an optical sensor. The proposed method is highly efficient, and the results demonstrated that the measurements are instant, repeatable and can successfully be employed in a clinical setup for patient evaluations.Clinical Relevance-Closely working with clinician to develop rehabilitation systems, we have identified that the assessment of patient hand functions is time consuming, and accuracy can be depended on the skill level of the practitioner in measuring joint range of motions (ROM). System introduced in this study can measure the joint AROMs instantly and independent of the practitioner's skill level and hence can provide a reliable, repeatable assessment of patient's hand function.

Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System

Stroke is one of the most prevalent health issues that people face today, causing long-term complications such as paresis, hemiparesis, and aphasia. These conditions significantly impact a patient's physical abilities and cause financial and social hardships. In order to address these challenges, this paper presents a groundbreaking solution-a wearable rehabilitation glove. This motorized glove is designed to provide comfortable and effective rehabilitation for patients with paresis. Its unique soft materials and compact size make it easy to use in clinical settings and at home. The glove can train each finger individually and all fingers together, using assistive force generated by advanced linear integrated actuators controlled by sEMG signals. The glove is also durable and long-lasting, with 4-5 h of battery life. The wearable motorized glove is worn on the affected hand to provide assistive force during rehabilitation training. The key to this glove's effectiveness is its ability to perform the classified hand gestures acquired from the non-affected hand by integrating four sEMG sensors and a deep learning algorithm (the 1D-CNN algorithm and the InceptionTime algorithm). The InceptionTime algorithm classified ten hand gestures' sEMG signals with an accuracy of 91.60% and 90.09% in the training and verification sets, respectively. The overall accuracy was 90.89%. It showed potential as a tool for developing effective hand gesture recognition systems. The classified hand gestures can be used as a control command for the motorized wearable glove placed on the affected hand, allowing it to mimic the movements of the non-affected hand. This innovative technology performs rehabilitation exercises based on the theory of mirror therapy and task-oriented therapy. Overall, this wearable rehabilitation glove represents a significant step forward in stroke rehabilitation, offering a practical and effective solution to help patients recover from stroke's physical, financial, and social impact.

Quo Vadis, Amadeo Hand Robot? A Randomized Study with a Hand Recovery Predictive Model in Subacute Stroke

BACKGROUND

Early identification of hand-prognosis-factors at patient's admission could help to select optimal synergistic rehabilitation programs based on conventional (COHT) or robot-assisted (RAT) therapies.

METHODS

In this bi-phase cross-over prospective study, 58 stroke patients were enrolled in two randomized groups. Both groups received same treatments A + B (A = 36 COHT sessions for 10 weeks; B = 36 RAT sessions for 10 weeks; 45 min/session; 3 to 5 times per week). Outcome repeated measures by blinded assessors included FMUL, BBT, NHPT, Amadeo Robot (AHR) and AMPS. Statistical comparisons by Pearson's rank correlations and one-way analyses of variance (ANOVA) with Bonferroni posthoc tests, with size effects and statistic power, were reported. Multiple backward linear regression models were used to predict the variability of sensorimotor and functional outcomes.

RESULTS

Isolated COHT or RAT treatments improved hand function at 3 months. While "higher hand paresis at admission" affected to sensorimotor and functional outcomes, "laterality of injury" did not seem to affect the recovery of the hand. Kinetic-kinematic parameters of robot allowed creating a predictive model of hand recovery at 3 and 6 months from 1st session.

CONCLUSIONS

Hand impairment is an important factor in define sensorimotor and functional outcomes, but not lesion laterality, to predict hand recovery.