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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.