Real-Time Gait Phase Detection on Wearable Devices for Real-World Free-Living Gait

Detecting gait phases with wearables unobtrusively and reliably in real-time is important for clinical gait rehabilitation and early diagnosis of neurological diseases. Due to hardware limitations of microcontrollers in wearable devices (e.g., memory and computation power), reliable real-time gait phase detection on the microcontrollers remains a challenge, especially for long-term real-world free-living gait. In this work, a novel algorithm based on a reduced support vector machine (RSVM) and a finite state machine (FSM) is developed to address this. The RSVM is developed by exploiting the cascaded K-means clustering to reduce the model size and computation time of a standard SVM by 88% and a factor of 36, with only minor degradation in gait phase prediction accuracy of around 4%. For each gait phase prediction from the RSVM, the FSM is designed to validate the prediction and correct misclassifications. The developed algorithm is implemented on a microcontroller of a wearable device and its real-time (on the fly) classification performance is evaluated by twenty healthy subjects walking along a predefined real-world route with uncontrolled free-living gait. It shows a promising real-time performance with an accuracy of 91.51%, a sensitivity of 91.70%, and a specificity of 95.77%. The algorithm also demonstrates its robustness with varying walking conditions

FEM/FD Immersed Boundary FSI Simulations

Immersed boundary simulations have been under development for physiological flows, allowing for elegant handling of fluid-structure interaction modelling with large deformations due to retained domain-specific meshing. We couple a structural system in Lagrangian representation that is formulated in a weak form with a Navier-Stokes system discretized through a finite differences scheme. We build upon a proven highly scalable imcompressible flow solver that we extend to handle FSI. We aim at applying our method to investigating the hemodynamics of Aortic Valves. The code is going to be extended to conform to the new hybrid-node supercomputers

Towards CFD at Exascale: Hybrid Multicore/Manycore Massively Parallel High-Order Navier-Stokes Solver

A GPU-accelerated high-order massively-parallel 3D Navier-Stokes solver has been developed for heart valve simulation. It is optimized for a Cray XC50 supercomputer by distributing the workload to different MPI processes and by offloading double-precision kernels to GPUs. The GPU kernels are written in CUDA C and are called by the FORTRAN legacy code. For a high-order finite-difference gradient kernel speedups of 5x (Tesla K20x) and 20x (Tesla P100) were achieved. In combination with 16 MPI threads on a single node of the Cray XC50, a peak speedup of 33x was achieved using CUDA MPS. Similar performance was also achieved for other differential operators, demonstrating the potential of GPU technology for bringing biomedical CFD to exascale computing

GPU-accelerated Immersed Boundary Method for the efficient simulation of biomedical fluid-structure interactions

Immersed boundary methods have become the most usable and useful tools for simulation of biomedical fluid-structure interaction, e.g., in the aortic valve of human heart. In such problems, complex geometry and motion of the soft tissue impose significant computational cost for bodyfitted- mesh methods. Resorting to a fixed Eulerian grid for the flow simulation along with the immersed boundary method to model the interaction with the soft tissue eliminates the expensive mesh generation and updating costs. Nevertheless, the computational cost for the geometry operations including adaptive search algorithms are still significant. Herein, we implemented the immersed boundary kernels with CUDA to be transferred and executed on thousands of parallel threads on the general purpose GPU. Host-device memory optimisation along with optimal usage of GPU multiprocessors results in a boosted performance in fluid-structure interaction simulation