The Comfort and Measurement Precision-Based Multi-Objective Optimization Method for Gesture Interaction.

As an advanced interaction mode, gestures have been widely used for human-computer interaction (HCI). This paper proposes a multi-objective optimization method based on the objective function JCP to solve the inconsistency between the gesture comfort JCS and measurement precision JPH in the gesture interaction. The proposed comfort model CS takes seventeen muscles and six degrees of freedom into consideration based on the data from muscles and joints, and is capable of simulating the energy expenditure of the gesture motion. The CS can provide an intuitive indicator to predict which act has the higher risk of fatigue or injury for joints and muscles. The measurement precision model ∆PH is calculated from the measurement error (∆XH,∆YH,∆ZH) caused by calibration, that provides a means to evaluate the efficiency of the gesture interaction. The modeling and simulation are implemented to analyze the effectiveness of the multi-objective optimization method proposed in this paper. According to the result of the comparison between the objective function JCS, based on the comfort model CS, and the objective function JPH, based on the measurement precision models ∆PH, the consistency and the difference can be found due to the variation of the radius rB_RHO and the center coordinates PB_RHOxB_RHO,yB_RHO,zB_RHO. The proposed objective function JCP compromises the inconsistency between the objective function JCS and JPH. Therefore, the multi-objective optimization method proposed in this paper is applied to the gesture design to improve the ergonomics and operation efficiency of the gesture, and the effectiveness is verified through usability testing.

Exploring the mechanism of astragalus membranaceus in the treatment of multiple system atrophy based on network pharmacology and molecular docking.

Multiple system atrophy (MSA) is a fatal neurodegenerative disease, it causes functional degradation of multiple organs and systems throughout the body. Astragalus membranaceus (AM), a well-known traditional Chinese medicine, has been used to improve muscle wasting-related disorders for a long history. In this study, we used network pharmacology and molecular docking to predict the mechanism underlying AM for the treatment of MSA. We screened the active compounds of AM and its related targets, as well as the target proteins of MSA. We made a Venn diagram to obtain the intersecting targets and then constructed a protein-protein interaction network to find the core targets and build an active ingredient-target network map. After subjecting the intersecting targets to gene ontology and Kyoto encyclopedia of genes and genomes analysis, the binding ability of core compounds and core target proteins were validated by molecular docking. A total of 20 eligible compounds and 274 intersecting targets were obtained. The core components of treatment are quercetin, kaempferol, and isorhamnetin, and the core targets are TP53, RELA, and TNF. The main biological processes are related to cellular responses and regulation. Molecular functions are mainly associated with apoptosis, inflammation, and tumorigenesis. Molecular docking results show good and standard binding abilities. This study illustrates that AM treats MSA through multiple targets and pathways, and provides a reference for subsequent research.

The effect of acupuncture at the Yuji point on resting-state brain function in anxiety.

BACKGROUND: The COVID-19 epidemic has placed a lot of mental burdens on school students, causing anxiety. Clinically, it has been found that the Yuji point (LU10) can relieve anxiety by regulating Qi. METHODS: Thirty-six volunteers with anxiety disorders were divided into 3 groups, all of whom underwent 2 MRI examinations. The Yuji and nonacupoint groups received acupuncture between functional magnetic resonance imagings. We used the amplitude of low-frequency fluctuation to analyze regional brain activity, and seed-based functional connectivity (FC) to analyze changes in brain networks. RESULTS: After acupuncture, the LU10 was able to activate the frontal lobe, medial frontal gyrus, anterior cingulate gyrus, temporal lobe, hippocampus, etc in the left brain compared to the control group. The frontal lobe, medial frontal gyrus, cingulate gyrus, and anterior cingulate gyrus in the left brain were activated compared to those in the nonacupoint group. Compared with the control group, LU10 showed increased FC in the right parietal lobe, right precuneus, left temporal lobe, left superior temporal gyrus, and with cingulate gyrus. FC was enhanced among the hippocampus with the left temporal lobe and the superior temporal gyrus and reduced in the right lingual gyrus and right occipital lobe. CONCLUSION: Acupuncture at LU10s can regulate anxiety by upregulating or downregulating the relevant brain regions and networks. LU10s can be used to treat not only lung disorders but also related mental disorders.

Effects of Acupuncture at Neiguan in Neural Activity of Related Brain Regions: A Resting-State fMRI Study in Anxiety.

Background: Acupuncture of PC6 points has the effects of calming, tranquilizing, regulating qi, and relieving pain and has been clinically found to alleviate anxiety disorders. To explore the mechanism of improvement at the Neiguan point acupuncture in anxiety patients, we used fMRI to observe the changes in brain function in patients with immediate anxiety before and after acupuncture at the Neiguan point. Subjects and Methods: The experiment followed the principle of randomized, single-blind design. Twenty-four anxiety volunteers (14 males and 10 females, 20-35 years old) were divided randomly into two groups: a group of acupuncture at Neiguan and a group of acupuncture at non-acupoint. Functional magnetic resonance imaging (fMRI) was applied to measure brain activity pre- and post-acupuncture. The amplitude of low-frequency fluctuations (ALFF) and seed-based functional connectivity (FC) was used to analyze the activity and network of brain regions. Statistical analysis was done using SPSS 21.0 and REST 1.8 software. Results: ALFF results revealed that post-acupuncture at Neiguan increased the activity of the left parahippocampal gyrus, fusiform gyrus, and right superior temporal gyrus and decreased the activity of the right middle frontal gyrus, right precuneus, and cuneus. Post-acupuncture at non-acupoint led to a significant ALFF increase in the thalamus and middle frontal gyrus. The ALFF in the left middle frontal gyrus was decreased. Functional connectivity in several anterior default mode network (DMN) regions and vermis cerebelli at left parahippocampal/fusiform gyri was increased, and connectivity in bilateral superior temporal gyri was decreased. FC with posterior DMN regions decreased at the right middle frontal gyrus, right precuneus, and cuneus. Conclusion: Our study elucidates that acupuncture at Neiguan modulates anxiety by activating or deactivating these brain anxiety-related regions and provides potential explanations for the application of PC6 acupuncture in mental diseases.

Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network.

BACKGROUND Depression is a common disease worldwide, with about 280 million people having depression. The unique facial features of depression provide a basis for automatic recognition of depression with deep convolutional neural networks. MATERIAL AND METHODS In this study, we developed a depression recognition method based on facial images and a deep convolutional neural network. Based on 2-dimensional images, this method quantified the binary classification problem and distinguished patients with depression from healthy participants. Network training consisted of 2 steps: (1) 1020 pictures of depressed patients and 1100 pictures of healthy participants were used and divided into a training set, test set, and validation set at the ratio of 7: 2: 1; and (2) fully connected convolutional neural network (FCN), visual geometry group 11 (VGG11), visual geometry group 19 (VGG19), deep residual network 50 (ResNet50), and Inception version 3 convolutional neural network models were trained. RESULTS The FCN model achieved an accuracy of 98.23% and a precision of 98.11%. The Vgg11 model achieved an accuracy of 94.40% and a precision of 96.15%. The Vgg19 model achieved an accuracy of 97.35% and a precision of 98.13%. The ResNet50 model achieved an accuracy of 94.99% and a precision of 98.03%. The Inception version 3 model achieved an accuracy of 97.10% and a precision of 96.20%. CONCLUSIONS The results show that deep convolution neural networks can support the rapid, accurate, and automatic identification of depression.