Cardiovascular diseases (CVDs) stand as the primary reason of fatalities globally, especially in low- and middle-income countries. In recent years, with the leverage of computer audition technologies, the diagnosis of CVDs through heart sounds become a popular topic. Current models and techniques are trained, validated, and tested on the same dataset, which need to be retrained when encountering new data. To make the best use of sparse data, we propose a Prototypical Network framework with heuristic weight for heart sound recognition. After extracting two different features (Mel Spectrogram and Mel Frequency Cepstral Coefficients) and encoding the features, we calculate the distance between two categories (normal and abnormal), then, a heuristic weight is assigned to the distance that makes the blurred boundaries more distinct. By considering the subject independence, the Unweighted Average Recall (UAR) on the PhysioNet/CinC Challenge 2016 is 68.2 % and 67.7 % on two features, respectively. The capability of our model to work on different datasets is proved by a UAR of 66.4 %, which exceeds the baseline UAR of 58.6 % under a single model.

This paper presents preliminary status of our project for developing a multi-scale simulation of musculo-skeletal system for providing a useful tool in the field of musculo-skeletal physiology and related healthcare issues. Here, a mechanically and physiologically detailed model of skeletal muscles, as a key building block of the system, is considered, in which hierarchical structure of skeletal muscles is modeled by multiple one-dimensional muscle fiber models embedded in a three-dimensional finite element continuum-mechanics model. For modeling the excitation-contraction coupling, we combined models at multiple scales including electrophysiology for action potential generation in muscle fibers, Ca2+dynamics of sarcoplasmic reticulum describing calcium release and uptake, and cross-bridge dynamics that can reproduce stochastic cross-bridge kinetics with a state transition model of the myosin molecules via Monte Carlo simulation. We then validated each of these models for the excitation-contraction coupling by comparing dynamics of the models with physiological data available in literatures on the muscle function.

We have an interest in the handling posture at the logistic workplace. Although a lot of studies on compressive and shear forces on lumbar spine have been reported, there was little studies on the difference of asymmetric lifting between logistic workers and beginners with the comparison of measured results. The main purpose of this study was to analyze the difference between ten healthy workers (Group 1) and five beginners (Group 2) during the asymmetric lifting through measurement of center of pressure (COP) locations. We performed experiments to move the 18 kg load to the left side under the condition of closed eyes. Through analysis results of COP locations, we found that Group 1 had the difference from Group 2. Most of COPs of Group 1 was located in the right side during the trunk angle to twist to the left side. We reported all of experimental results in this study.

We have an interest in the relationship between the human postural balance and back injuries. Although back injuries are the number-one problem facing the workforce in the industry of carrying loads, there appears to be no firm idea on how this will deal with in practice. It is known that the brain plays an important role in maintenance of standing posture. In this paper, we focus on evaluating how the human postural balance is affected under three conditions of lifting loads. Especially, we evaluate the human postural balance during the rising time. We use the spectrum analysis for evaluating the postural balance by using the Nintendo Wii balance board. Through all of experiments, we found the tendency that the heavier loads are, the faster and larger the body swing for balancing the posture during the rising time is. Additionally, the case excluding bending the knee shows the larger swing than that including bending the knee. From all of results, we could hypothesize that an expert of carrying loads may make a balance for the posture during rising time as soon as possible by using the smaller swing motion. We show all of experimental results.

Flint is a simulator that numerically integrates heterogeneous biophysical models described by a large set of ordinary differential equations. It uses an internal bytecode representation of simulation-related expressions to handle various biophysical models built for general purposes. We propose two acceleration methods for Flint using a graphics processing unit (GPU). The first method interprets multiple bytecodes in parallel on the GPU. It automatically parallelizes the simulation using a level scheduling algorithm. We implement an interpreter of the Flint bytecode that is suited for running on the GPU, which reduces both the number of memory accesses and divergent branches to achieve higher performance. The second method translates a model into a source code for both the CPU and the GPU through the internal bytecode, which speeds up the compilation of the generated source codes, because the code size is diminished because of bytecode unification. With large models such that tens of thousands or more expressions can be evaluated simultaneously, the translated code running on the GPU achieves computational performance of up to 2.7 higher than that running on a CPU. Otherwise, with small models, the CPU is faster than the GPU. Therefore, the translated code dynamically determines on which to run either the CPU or the GPU by profiling initial few iterations of the simulation.