Anatomy education is a challenging but vital element in forming future medical professionals. In this work, a personalized and interactive augmented reality system is developed to facilitate education. This system behaves as a "magic mirror" which allows personalized in-situ visualization of anatomy on the user's body. Real-time volume visualization of a CT dataset creates the illusion that the user can look inside their body. The system comprises a RGB-D sensor as a real-time tracking device to detect the user moving in front of a display. In addition, the magic mirror system shows text information, medical images, and 3D models of organs that the user can interact with. Through the participation of 7 clinicians and 72 students, two user studies were designed to respectively assess the precision and acceptability of the magic mirror system for education. The results of the first study demonstrated that the average precision of the augmented reality overlay on the user body was 0.96 cm, while the results of the second study indicate 86.1% approval for the educational value of the magic mirror, and 91.7% approval for the augmented reality capability of displaying organs in three dimensions. The usefulness of this unique type of personalized augmented reality technology has been demonstrated in this paper.

The proposed system proved to be efficient by improving the user engagement and exercise performance outcomes. The results also suggest that the use of biomechanical standards to recognize movements is valuable in guiding users during rehabilitation exercises.

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Herein, two kinds of 3D hexagonal honeycomb equivalent models are proposed, enlightened by the octahedron model constructed by the wine‐rack mechanism. They differ in their unit‐cell arrangement forms, which are array and homogeneous arrangements, respectively. Referring to the analysis of the octahedron model, the expressions of compressibility properties of the two new models are given and the conditions for obtaining negative compressibility are analyzed. Comparing these two models with the octahedron model, it is found that although the three models are quite different in externality, they have similar mechanical properties (Young's modulus, Poisson's ratio, and compressibility), and all have image symmetry in both horizontal directions. Further analysis shows that these three models are so unified that they can be expressed by a more general model, from which another model with negative compressibility can be deduced. Finally, a new method to improve negative compressibility property is concluded from comparing the three models, that is, increasing the number or length of rods without deformation in vertical direction can effectively improve negative compressibility property in this direction and weaken negative compressibility property in the other two directions.

We present a medical Augmented Reality (AR) edutainment system for bone anatomy learning. This learning environment, called AR bone puzzle, is a metaphor for bone anatomy learning with AR visualization and intuitive interaction. AR bone puzzle uses its user's body as a puzzle frame and computer generated virtual bones as puzzle pieces. Users learn bone anatomy by assembling the virtual bone pieces on their body. Key features of this system are 3D AR visualization and intuitive gesture based user interaction.

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Three 3D models with negative compressibility have been presented in our previous paper [X. Q. Zhou et al., Phys. Status Solidi B 2016, 253, 1977]. However, the 2D mechanism (i.e., wine‐rack mechanism) which we used to design 3D models is so symmetrical that the axial properties of it are equivalent, so we have only focused on geometry features. In this work, an extended study is conducted in order to find a more efficient method to design 3D structures with evident negative compressibility. Through theoretical modeling, the compressibility properties of three 3D models made from hinging hexagonal truss mechanism are analyzed and the conditions for negative compressibility to be exhibited are discussed. The study shows that in addition to the geometry features, negative compressibility effects can also be significantly affected by the arrangement of the framework and layout orientations of 2D mechanism.

BackgroundPrediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications.ResultsIn this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture.ConclusionsSpeedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications.