Objective To explore the effects of upper limb robot-assisted therapy on motor function and activities of daily living in con-valescent stroke patients. Methods From June to September, 2016, 12 chronic stroke patients at their first-ever stroke were enrolled and ran-domized into experimental group (n=6) and control group (n=6). Both groups received routine rehabilitation. Additional robot-assisted thera-py was provided to the experimental group, and additional repetitive movement training was provided to the control group, 20 minutes a day, five days a week for four weeks. Fugl-Meyer Assessment-Upper Extremities (FMA-UE), modified Ashworth Scale (MAS) and Func-tional Independent Measure (FIM) were used to assess the motor function of the upper limbs and hands, the muscular tension of shoulder and elbow, and activities of daily living (ADL) before and after treatment. Results After treatment, the scores of FMA-UE and FIM were bet-ter in both groups (Z>2.032, P<0.05), and no significant difference was found between two groups (t<0.723, P>0.05), however, the scores were a little bit higher in the experimental group than in the control group. After treatment, for the experimental group, the MAS scores of shoulder abduction/adduction and elbow flexion/extension improved (Z>2.121, P<0.05);for the control group, the MAS scores of shoulder abduction/adduction improved (Z>2.000, P<0.05), but the MAS scores of elbow flexion/extension were not significantly different (Z<1.890,P>0.05). There was no significant difference in the MAS scores of shoulder abduction/adduction and elbow flexion/extension between two group (Z<1.734, P>0.05). The moving trail recorded by the computer, gradually became a regular pattern from the mass, saying the motor control ability became better. Conclusion Upper limb robot-assisted therapy can promote the recovery of the motor function of upper limbs and ADL in convalescent stroke patient, similar to the repetitive movement training.

Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.
Algorithms ; Databases, Factual ; Electrocardiography ; Humans ; Inferior Wall Myocardial Infarction ; Support Vector Machine