ABSTRACT
BACKGROUND:In clinical application,the therapeutic effect of transcranial magnetic stimulation depends on the ability to accurately target the areas of the brain that need to be stimulated.In recent years,with the development of neuronavigation systems,mobile augmented reality technology,and the new methods of processing magnetic resonance imaging(MRI)data,the accuracy of stimulus target localization and the optimization of target selection are expected to improve further. OBJECTIVE:To review the principle of MRI-based image navigation and its application in transcranial magnetic stimulation and summarize the roles of different modal MRI data analyses in guiding the selection of target areas for transcranial magnetic stimulation. METHODS:An online computer search for relevant literature was performed in PubMed,CNKI database and WanFang database,with the keywords"transcranial magnetic stimulation,coil positioning,neuronavigation,augmented reality,magnetic resonance,theory."Finally,63 documents were included for review. RESULTS AND CONCLUSION:Among the traditional methods of positioning transcranial magnetic stimulation coils,the"5 cm rule"and the international electroencephalogram 10-20 positioning method are the most commonly used.These methods have the advantages of simplicity and economy,but they rely too much on the operator's experience and there were technical differences between operators.The neuronavigation system,which is based on stereotactic technology,is the guiding method for positioning transcranial magnetic stimulation coils with the highest visual degree and accuracy.It achieves visual positioning through MRI data acquisition,3D brain reconstruction,head model registration and stereogeometric positioning.It has high application value in clinical treatment and scientific research,but it cannot be promoted in medical institutions due to its high cost.For various medical institutions,mobile augmented reality is a cost-effective and efficient alternative to the neuronavigation system,which achieves visual positioning of brain tissue under the scalp through MRI data acquisition,2D/3D image construction,virtual image and real brain image superposition.It has the advantages of directly visualization and low cost,and is expected to be popularized and applied in primary medical units.Although the superiority of clinical efficacy of visual coil positioning over the electroencephalogram 10-20 localization strategy has not yet been fully demonstrated,with the progress of brain MRI data analysis,visual positioning is expected to further optimize the target selection strategy of transcranial magnetic stimulation therapy and to improve the response rate and individuation degree of transcranial magnetic stimulation treatment.This is a promising and challenging research direction in the future.
ABSTRACT
Objective:To explore the application of mobile augmented reality (mAR) technology in the teaching of neuroanatomy, and to observe its effect on students' academic performance and cognitive load.Methods:By collecting and designing various neuroanatomy multimedia teaching resources (graphics, animations and videos), using augmented reality (AR) marker-based image recognition technology, the multimedia resources were placed at the tags in the traditional book pages to make the books interactive. And various multimedia resources were combined with traditional printed books through mobile devices. Forty students were randomized into the experimental group or the control group. The experimental group was taught with mAR multimedia materials, and the control group adopted traditional teaching methods. After a 6-hour course was completed, all students had a unified test, and the academic performance test and the PAAS(platform-as-a-service) cognitive load scale were used for data collection and analysis. The variance analyses (MANOVA and ANOVA) were used for significance testing.Results:One-way MANOVA test was used to determine the learning effect of mAR on academic performance and cognitive load. The results showed that there was a significant difference between the experimental group and the control group ( P<0.05). The univariate ANOVA test found that the experimental group students who learned neuroanatomy through mAR had better test scores than the control group students. In addition, compared with the control group students, the cognitive load of students in experimental group was significantly reduced, with statistical significance (all P<0.05). Conclusion:Through the teaching practice, we found that using mAR to learn neuroanatomy helps students improve their academic performance while reducing their cognitive load.
ABSTRACT
Introduction: Individuals with mobility impairments associated with lower limb disabilities often face enormous challenges to participate in routine activities and to move around various environments. For many, the use of wheelchairs is paramount to provide mobility and social inclusion. Nevertheless, they still face a number of challenges to properly function in our society. Among the many difficulties, one in particular stands out: navigating in complex internal environments (indoors). The main objective of this work is to propose an architecture based on Mobile Augmented Reality to support the development of indoor navigation systems dedicated to wheelchair users, that is also capable of recording CAD drawings of the buildings and dealing with accessibility issues for that population. Methods Overall, five main functional requirements are proposed: the ability to allow for indoor navigation by means of Mobile Augmented Reality techniques; the capacity to register and configure building CAD drawings and the position of fiducial markers, points of interest and obstacles to be avoided by the wheelchair user; the capacity to find the best route for wheelchair indoor navigation, taking stairs and other obstacles into account; allow for the visualization of virtual directional arrows in the smartphone displays; and incorporate touch or voice commands to interact with the application. The architecture is proposed as a combination of four layers: User interface; Control; Service; and Infrastructure. A proof-of-concept application was developed and tests were performed with disable volunteers operating manual and electric wheelchairs. Results The application was implemented in Java for the Android operational system. A local database was used to store the test building CAD drawings and the position of fiducial markers and points of interest. The Android Augmented Reality library was used to implement Augmented Reality and the Blender open source library handled the basis for implementing directional navigation arrows. OpenGL ES provided support for various graphics and mathematical transformations for embedded systems, such as smartphones. Experiments were performed in an academic building with various labs, classrooms and male and female bathrooms. Two disable volunteers using wheelchairs showed no difficulties to interact with the application, either by entering touch or voice commands, and to navigate within the testing environment with the help of the navigational arrows implemented by the augmented reality modules. Conclusion The novel features implemented in the proposed architecture, with special emphasis on the use of Mobile Augmented Reality and the ability to identify the best routes free of potential hazards for wheelchair users, were capable of providing significant benefits for wheelchair indoor navigation when compared to current techniques described in the literature.