Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.
Ruxolitinib is the first janus kinase 1 (JAK1) and JAK2 inhibitor that was approved by the United States Food and Drug Administration (FDA) agency for the treatment of myeloproliferative neoplasms. The drug targets the JAK/STAT signalling pathway, which is critical in regulating the gliogenesis process during nervous system development. In the study, we assessed the effect of non-maternal toxic dosages of ruxolitinib (0-30 mg/kg/day between E7.5-E20.5) on the brain of the developing mouse embryos. While the pregnant mice did not show any apparent adverse effects, the Gfap protein marker for glial cells and S100β mRNA marker for astrocytes were reduced in the postnatal day (P) 1.5 pups' brains. Gfap expression and Gfap+ cells were also suppressed in the differentiating neurospheres culture treated with ruxolitinib. Compared to the control group, adult mice treated with ruxolitinib prenatally showed no changes in motor coordination, locomotor function, and recognition memory. However, increased explorative behaviour within an open field and improved spatial learning and long-term memory retention were observed in the treated group. We demonstrated transplacental effects of ruxolitinib on astrogenesis, suggesting the potential use of ruxolitinib to revert pathological conditions caused by gliogenic-shift in early brain development such as Down and Noonan syndromes.