Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/\ncircuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter\nsignals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to\nextend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data\nacquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification\nalgorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning,\nas well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate\nresults are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired\nobjectives and their real-time implementation as compared to other algorithms....
With the continuous development of information technology and digital medicine, computer-assisted virtual medicine has become\nthe development trend of a new generation of clinical surgery, which aims to improve the accuracy of surgery, reduce the risk of\nsurgery, and achieve precise and minimally invasive treatment. The interface design in the computer-aided virtual medical system\nis a medium for transmitting and exchanging information between humans and machines. This article uses virtual reality\ntechnology and augmented reality technology to develop a virtual medical system interface, which aims to solve the interaction\nproblem between users and virtual medical systems and satisfy users. The multidemand psychology is an effective way of\ninteraction. It provides users with a multichannel and comprehensive communication method, which truly meets the design goals\nthat meet the userâ??s psychological needs. It also expands applications for virtual reality technology and augmented reality technology....
Design teamperformance evaluation can occur in different ways, all of themrequiring considerations on interactions among team\nmembers; in turn, these considerations should count on asmany pieces of information as possible about individuals. Theliterature\nalready explains how personal characteristics and/or external factors influence designersâ?? performance; nevertheless, a way to\nevaluate performance considering several personal characteristics and external factors together ismissing. This research tries to fill\nthe gap by developing the Designerâ??s Performance Estimator (DPE), a ready-to-use tool for researchers and practitioners who\nneed to make information about team members as richer as possible....
A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based\naction recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information\nin single frame and motion information of joints between adjacent frames from the human body skeleton structure and\nthen combine the classification results. However, that does not take into consideration of the complicated temporal and spatial\nrelationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we\nenhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high\ndiscrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of\nsuppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion\nposture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to\nmake use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motionbased\nspatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the\nlow-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments\nhave been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition\nmethods. The designed human-robot interaction system based on action recognition has competitive performance compared with\nthe speech interaction system....
This study explored how telepresence could be affected by stimuli from reality that distracts people while they are watching\ntelevision. The sample comprised of 36 undergraduate and graduate students from a university in South Korea (age range: 18â??38\nyears, M1 22.61, and SD 1 4.12). A between-subjects experimental design was employed with two types of viewing equipment (a\ntelevision screen vs. a television screen with side screens that act as stimuli from reality) and two bezel widths (2 cm vs. 10 cm) to\nexamine how each condition influenced the viewersâ?? perceived telepresence. The results revealed that participantsâ?? perception of\ntelepresence was not affected by the type of viewing equipment. However, the level of telepresence was affected by the bezel width:\nthe thinner the bezel, the more telepresence felt by the viewers. These findings provide important insights that can guide the future\ndesigns of screen bezels for televisions and other devices in order to more effectively create immersive virtual worlds. Future\nstudies are needed to examine the relationship between central vision and telepresence....
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