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Cardiac electrophysiology simulations are numerically challenging because of the propagation of a steep electrochemical wave front and thus require discretizations with small mesh sizes to obtain accurate results. In this work, we present an approach based on the hybridizable discontinuous Galerkin method (HDG), which allows an efficient implementation of high-order discretizations into a computational framework. In particular, using the advantage of the discontinuous function space, we present an efficient p-adaptive strategy for accurately tracking the wave front. The HDG allows to reduce the overall degrees of freedom in the final linear system to those only on the element interfaces. Additionally, we propose a rule for a suitable integration accuracy for the ionic current term depending on the polynomial order and the cell model to handle high-order polynomials. Our results show that for the same number of degrees of freedom, coarse high-order elements provide more accurate results than fine low-order elements. Introducing p-adaptivity further reduces computational costs while maintaining accuracy by restricting the use of high-order elements to resolve the wave front. For a patient-specific simulation of a cardiac cycle, p-adaptivity reduces the average number of degrees of freedom by 95% compared to the nonadaptive model. In addition to reducing computational costs, using coarse meshes with our p-adaptive high-order HDG method also simplifies practical aspects of mesh generation and postprocessing.

Mixed reality rehabilitation systems and games are demonstrating potential as innovative adjunctive therapies for health professionals in their treatment of various hand and upper limb motor impairments. Unilateral motor deficits of the arm, for example, are commonly experienced post stroke. Our TheraMem system provides an augmented reality game environment that contributes to this increasingly rich area of research. We present a prototype system which "fools the brain" by visually amplifying users' hand movements -small actual hand movements lead to perceived larger movements. We validate the usability of our system in an empirical study with forty-five non-clinical participants. In addition, we present early qualitative evidence for the utility of our approach and system for stroke recovery and motor rehabilitation. Future uses of the system are considered by way of conclusion.

Knowledge of appropriate local fiber architecture is necessary to simulate patient‐specific electromechanics in the human heart. However, it is not yet possible to reliably measure in vivo fiber directions especially in human atria. Thus, we present a method that defines the fiber architecture in arbitrarily shaped atria using image registration and reorientation methods based on atlas atria with fibers predefined from detailed histological observations. Thereby, it is possible to generate detailed fiber families in every new patient‐specific geometry in an automated, time‐efficient process. We demonstrate the good performance of the image registration and fiber definition on 10 differently shaped human atria. Additionally, we show that characteristics of the electrophysiological activation pattern that appear in the atlas atria also appear in the patients' atria. We arrive to analogous conclusions for coupled electro‐mechano‐hemodynamical computations.

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