APPLICATION OF AUGMENTED REALITY TOOLS TO THE DESIGN PREPARATION OF PRODUCTION

The paper describes the application of augmented reality tools to create an auxiliary interactive tool in the field of design. To mediate it, an application with AR operation based on marker tracking has been designed. The created application works with the mobile devices platform. Two types of markers have been used in the application. To display basic information, buttons with functions are added to the scene, such as part information, rotation and change of position in the X, Y, Z direction. The application runs on a mobile phone, with a built-in camera. The marker is displayed in the drawing's lower left corner. The model is positioned so that it appears above the view of the part in the drawing. The task of the application is to support intelligent tools applicable in the design stage of production preparation, in the creation of drawing documentation.

“Augmenting visual faculties: an exploration of traditional and experimental augmented reality methods in artistic practice”

"The final project resulted in a series of artistic works applying both traditional and experimental AR methods. The various AR artworks created compose a body of work that are intended to be viewed as a series resulting from two streams of exploration: traditional marker tracking methods, and experimental processes with non-marker images and alternative materials"--From page 8.

“Augmenting visual faculties: an exploration of traditional and experimental augmented reality methods in artistic practice”

"The final project resulted in a series of artistic works applying both traditional and experimental AR methods. The various AR artworks created compose a body of work that are intended to be viewed as a series resulting from two streams of exploration: traditional marker tracking methods, and experimental processes with non-marker images and alternative materials"--From page 8.

A strain-based investigation of the accuracy of embedded markers used in tracking cadaveric brain motion

Measurements of intracranial brain displacement in cadaveric specimens have been instrumental to the validation finite element (FE) models of brain injury. These data collections have used radiographic and sonomicrometric techniques, requiring the use of tissue-embedded tracking markers; however, marker accuracy has never been adequately characterized. Marker tracking precision has been previously conflated with measurement accuracy, not accounting for changes in the natural responseof surrounding tissues due to marker presence. Non-negligible inertia, high stiffness, and the aspect ratio of markers all contribute to this interference. This work investigated the dynamic coupling between published marker designs (NDTs, Sonomicrometry Crystals, and Tin) and a new elastomeric marker, and a block of tissue simulant subjected to a drop impact. The measured strains were compared to the baseline response of the simulant containing massless markers. The results found notable evidence of interference in simulant strain amplitudes as well as considerable directional bias in the response of some markers. The elastomeric marker was found to have minimal interference in the deformation field. FutureFE model validation will need to account for the considerable interference and directional biases to the natural response of brain tissue in existing cadaveric datasets to maintain confidence in strain predictions.

Real-Time and Dynamically Consistent Estimation of Muscle Forces Using a Moving Horizon EMG-Marker Tracking Algorithm—Application to Upper Limb Biomechanics

Real-time biofeedback of muscle forces should help clinicians adapt their movement recommendations. Because these forces cannot directly be measured, researchers have developed numerical models and methods informed by electromyography (EMG) and body kinematics to estimate them. Among these methods, static optimization is the most computationally efficient and widely used. However, it suffers from limitation, namely: unrealistic joint torques computation, non-physiological muscle forces estimates and inconsistent for motions inducing co-contraction. Forward approaches, relying on numerical optimal control, address some of these issues, providing dynamically consistent estimates of muscle forces. However, they result in a high computational cost increase, apparently disqualifying them for real-time applications. However, this computational cost can be reduced by combining the implementation of a moving horizon estimation (MHE) and advanced optimization tools. Our objective was to assess the feasibility and accuracy of muscle forces estimation in real-time, using a MHE. To this end, a 4-DoFs arm actuated by 19 Hill-type muscle lines of action was modeled for simulating a set of reference motions, with corresponding EMG signals and markers positions. Excitation- and activation-driven models were tested to assess the effects of model complexity. Four levels of co-contraction, EMG noise and marker noise were simulated, to run the estimator under 64 different conditions, 30 times each. The MHE problem was implemented with three cost functions: EMG-markers tracking (high and low weight on markers) and marker-tracking with least-squared muscle excitations. For the excitation-driven model, a 7-frame MHE was selected as it allowed the estimator to run at 24 Hz (faster than biofeedback standard) while ensuring the lowest RMSE on estimates in noiseless conditions. This corresponds to a 3,500-fold speed improvement in comparison to state-of-the-art equivalent approaches. When adding experimental-like noise to the reference data, estimation error on muscle forces ranged from 1 to 30 N when tracking EMG signals and from 8 to 50 N (highly impacted by the co-contraction level) when muscle excitations were minimized. Statistical analysis was conducted to report significant effects of the problem conditions on the estimates. To conclude, the presented MHE implementation proved to be promising for real-time muscle forces estimation in experimental-like noise conditions, such as in biofeedback applications.

Penerapan Penerapan Teknologi Augmented Reality Pada Pembelajaran Huruf Kaganga Sunda Dengan Metode Marker Tracking

Pandemi Covid-19 ini semua kegiatan manusia telah mulai berubah, smartphone menjadi sebuah alat yang sangat berguna dalam kehidupan sehari-hari manusia terutama dalam kegiatan transaksi ekonomi. Tidak terlepas juga dalam dunia pendidikan, model pembelajaran tatap muka secara langsung tergantikan dengan model pembelajaran secara daring (dalam jaringan) memanfaatkan smartphone dan internet. Kendala yang dihadapi adalah beberapa mata pelajaran yang diberikan oleh guru kurang dipahami oleh siswa karena hanya berupa teks atau gambar. Selain itu, dengan cara belajar membaca teks kurang menarik bagi siswa dan menyebabkan siswa menjadi bosan atau jenuh. Augmented reality adalah salah satu perkembangan teknologi dalam bidang perangkat lunak. Augmented Reality banyak digunakan dalam industri game, pendidikan, maupun kedokteran dikarenakan dapat memodelkan suatu objek dalam bentuk 3 Dimensi yang menyerupai objek sebenarnya. Penelitian ini dimaksudkan untuk mengkombinasikan teknologi augmented reality dalam bidang pendidikan untuk mengurangi kejenuhan siswa dalam pembelajaran. Metodologi penelitian yang digunakan oleh peneliti untuk menyusun laporan ini adalah metode kualitatif dimana pada metode ini penelitian dilakukan secara sistematis, spesifik, terstruktur dan juga terencana dengan baik dari awal hingga mendapatkan sebuah kesimpulan. Metode pengembangan sistem yang digunakan adalah metode Rappid Application Develepoment (RAD). Rapid Application Development (RAD) merupakan metodologi yang lengkap, dengan 4 fase siklus hidup yang sejajar dengan fase SDLC tradisional Pembelajaran huruf kaganga sunda dibuat dalam bentuk animasi 3 dimensi yang mengajarkan cara penulisan aksara sunda, sehingga dapat menarik bagi siswa SD. Aplikasi ini juga menggunakan teknologi augmented reality dalam aplikasi android, sehingga siswa SD dapat belajar sambil bermain.