Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.

The outward appearance of human tongue can reflect changes in blood circulation caused by pathological states, and it has been used as an assisted method for clinical diseases diagnosis for thousands of years in China. The purpose of this study is to observe the changes in the tongue of patients with menstrual-related diseases in hyperspectral imaging and to explore the value of hyperspectral tongue imaging combining with machine learning algorithm (HSI-ML) in the diagnosis of menstrual diseases (MD). Hyperspectral tongue images are collected from 429 patients with five different kinds of MD and 52 participants with normal menstruation. Tongue coating and tongue body spectral characteristics are extracted and used as model input variables to investigate the influence on the modeling results.Normalization (Norm), first derivative (1st D), second derivative (2nd D), savitzky-golay smoothing (S-G), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV) are used as preprocessing method. Four model algorithms, k-nearest neighbor (KNN), random forest (RF), support vector machines (SVM) and artificial neural network(ANN) are used and compared. Experimental results show that patients of each MD group exhibit significantly lower spectral reflectance of tongue coating and tongue body (P < 0.05) than participants of normal menstruation group. And the modeling results indicate that the “2nd D + S-G + ANN” identification model based on tongue body spectral characteristics yields the optimal performance. Specifically, its accuracy, macro-precision, macro-recall, and macro-F1 score are 0.9729, 0.9697, 0.9703, and 0.97, respectively. It indicates that HSI-ML method with hyperspectral tongue images can provide a rapid and noninvasive detection method for MD screening.

With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis results) participate. Reflectance spectra of subjects’ tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity. Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation. To measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with 100 times Hold-out method and 67 times Leave-one-out method are 88.05% and 97.01%, respectively. Experimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.

Background: Type 2 diabetes (T2D) accounts for the majority of diabetes incidences and remains a widespread global chronic disorder. Apart from early lifestyle changes, intervention options for T2D are mainly pharmaceutical.Methods: Repetitive transcranial magnetic stimulation (rTMS) has been approved by the FDA as a therapeutic intervention option for major depressive disorders, with further studies also indicating its role in energy metabolism and appetite. Considering its safe and non‐invasive properties, we evaluated the effects of rTMS on systemic metabolism using T2D rats.Results: We observed that rTMS improved glucose tolerance and insulin sensitivity in T2D rats after a 10‐day exposure. Improved systemic insulin sensitivity was maintained after a 21‐day treatment period, accompanied by modest yet significant weight loss. Circulating serum lipid levels, including those of cholesteryl ester, tryglyceride and ceramides, were also reduced following rTMS application. RNA‐seq analyses further revealed a changed expression profile of hepatic genes that are related to sterol production and fatty acid metabolism. Altered expression of hypothalamic genes that are related to appetite regulation, neural activity and ether lipid metabolism were also implicated.Conclusion: In summary, our data report a positive impact of rTMS on systemic insulin sensitivity and weight management of T2D rats. The underlying mechanisms via which rTMS regulates systemic metabolic parameters partially involve lipid utilization in the periphery as well as central regulation of energy intake and lipid metabolism.