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Objective: Robot-assisted neuro-rehabilitation therapy plays a central role in upper extremity recovery of stroke. Even though, the efficacy of robotic training on upper extremity is not yet well defined and scant attention has been devoted to its potential effect on lower extremity. In this paper, the aim was to compare efficacy on upper and lower extremities between robot-assisted training (RAT) and therapist-mediated enhanced upper extremity therapy (EUET).Methods: A  randomized clinical trial involving 172 stroke survivors was conducted in China. All participants received either RAT or EUET for 3 weeks. The Fugl-Meyer assessment upper extremity subscale (FMA-UE), Fugl-Meyer assessment lower extremity subscale (FMA-LE), and Modified Barthel Index (MBI) were administered at baseline, mid-treatment (one week after treatment start), and posttreatment.  Results: Participants in RAT group showed a significant improvement in hemiplegia extremity, which was non-inferior to EUET group in FMA-UE (p<0.05), while suggesting greater motor recovery of lower extremity in FMA-LE (p<0.05) compared with EUET group. A marked increase in MBI was observed within groups, however, no significant difference was detected between groups.Conclusion: RAT is non-inferior in reducing impairment of upper extremity and appears to be superior in that of lower extremity compared with EUET for stroke survivors.

Quantitative assessment of hand function can assist therapists in providing appropriate rehabilitation strategies, which plays an essential role in post-stroke rehabilitation. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. To quantitatively assess the hand motor function of patients with post-stroke hemiplegia, this study proposes a novel multi-modality fusion assessment framework. This framework includes three components: the kinematic feature extraction based on a graph convolutional network (HAGCN), the surface electromyography (sEMG) signal processing based on a multi-layer long short term memory (LSTM) network, and the quantitative assessment based on the multi-modality fusion. To the best of the authors' knowledge, this is the first study of applying a graph convolution network to assess the hand motor function. We also collect the kinematic data and sEMG data from 70 subjects who completed 28 types of hand movements. Therapists first graded patients using traditional motor assessment scales (Brunnstrom Scale and Fugl-Meyer Assessment Scale) and further refined the patient's motor assessment result by their experience. Then, we trained the HAGCN and LSTM networks and quantitatively assessed each patient based on the proposed assessment framework. Finally, the Spearman correlation coefficient (SC) between the assessment result of this study and the traditional scale are 0.908 and 0.967, demonstrating a significant correlation between the proposed assessment and the traditional scale scores. In addition, the SC value between the score of this study and the refined hand motor function is 0.997, indicating the "ceiling effect" of some traditional scales can be avoided.

To investigate the cerebral mechanisms underlying learning of motor skills, we assessed serial changes of brain activation patterns during a pursuit rotor (PR) task in 12 right-handed healthy subjects using functional near-infrared spectroscopy (fNIRS). The subjects performed the task with their right hand for 15-s, alternated with a 30-s rest period, for 18 repetitions (cycles 1 to 18). Gains in motor skill were evaluated by recording the time for which the stylus remained on the target. Performance improved with repetition of the task. Task-related increases of oxygenated hemoglobin (oxy-Hb) were observed around the predicted location of the sensorimotor cortices on both hemispheres. The increased oxy-Hb levels appeared to reduce with repetition of the task in the channels covering the left sensorimotor area. Furthermore, there was a significant correlation between PR task performance gain and the oxy-Hb signal in the left and right sensorimotor areas. Our results suggest that cortical activation in the sensorimotor cortex reflects changes in a number of factors including sensory feedback processing, correct motor commands, and perceptual or cognitive function during learning of a PR task. Therefore, changes in contralateral sensorimotor cortical activation may serve as a motor sequence learning biomarker for rehabilitation purposes or the prediction of recovery.

[Purpose] The purpose of this study was to compare prefrontal activations during an added-purpose task with those during a single-purpose task using functional near-infrared spectroscopy. [Subjects] Six healthy right-handed adults were included in this study. [Methods] The participants were instructed to complete both added-purpose and single-purpose activities separately with each hand. The near-infrared spectroscopy probes were placed on the scalp overlying the prefrontal cortex, according to the International 10–20 system (Fz). Changes in the oxygenated hemoglobin and deoxygenated hemoglobin concentrations in the prefrontal cortex were measured during performance of the activities. We then compared the number of activation channels with significant increase in oxygenated hemoglobin, during added-purpose activity to single-purpose activity using both hands separately. [Results] A greater number of widespread activations were observed in the prefrontal cortex during the added-purpose task than during the single-purpose task. These results were noted with both right and left hands. [Conclusion] According to our findings, added-purpose activity can bring about more activation in the prefrontal cortex, which may provide occupational therapists with effective guides in therapeutic practice.

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BackgroundThe mirror neuron system (MNS) plays a key role in the neural mechanism underlying motor learning and neural plasticity. Action observation (AO), action execution (AE), and a combination of both, known as action imitation (AI), are the most commonly used rehabilitation strategies based on MNS. It is possible to enhance the cortical activation area and amplitude by combining traditional neuromuscular electrical stimulation (NMES) with other top-down and active rehabilitation strategies based on the MNS theory.ObjectiveThis study aimed to explore the cortical activation patterns induced by NMES synchronized with rehabilitation strategies based on MNS, namely NMES+AO, NMES+AE, and NMES+AI. In addition, the study aimed to assess the feasibility of these three novel rehabilitative treatments in order to provide insights and evidence for the design, implementation, and application of brain-computer interfaces.MethodsA total of 70 healthy adults were recruited from July 2022 to February 2023, and 66 of them were finally included in the analysis. The cortical activation patterns during NMES+AO, NMES+AE, and NMES+AI were detected using the functional Near-Infrared Spectroscopy (fNIRS) technique. The action to be observed, executed, or imitated was right wrist and hand extension, and two square-shaped NMES electrodes were placed on the right extensor digitorum communis. A block design was adopted to evaluate the activation intensity of the left MNS brain regions.ResultsGeneral linear model results showed that compared with the control condition, the number of channels significantly activated (PFDR &lt; 0.05) in the NMES+AO, NMES+AE, and NMES+AI conditions were 3, 9, and 9, respectively. Region of interest (ROI) analysis showed that 2 ROIs were significantly activated (PFDR &lt; 0.05) in the NMES+AO condition, including BA6 and BA44; 5 ROIs were significantly activated in the NMES+AE condition, including BA6, BA40, BA44, BA45, and BA46; and 6 ROIs were significantly activated in the NMES+AI condition, including BA6, BA7, BA40, BA44, BA45, and BA46.ConclusionThe MNS was activated during neuromuscular electrical stimulation combined with an AO, AE, and AI intervention. The synchronous application of NMES and mirror neuron rehabilitation strategies is feasible in clinical rehabilitation. The fNIRS signal patterns observed in this study could be used to develop brain-computer interface and neurofeedback therapy rehabilitation devices.