Use of Simulink to evaluate the air-quality and energy performance of HRV-equipped residences in Fairbanks, Alaska

Mechanical ventilation systems in residences usually serve a single purpose, providing only a relatively small benefit compared to the capital cost. Polluted areas use mechanical ventilation to filter incoming air, cold regions use it to be able to recover the heat from the stale air going out. However, both issues – energy and air-quality – can be beneficially addressed together using one ventilation system in cold climate regions with air pollution problems, such as Fairbanks, Alaska. This paper presents a dynamic model for evaluating indoor PM_2.5 levels and energy consumption associated with ventilation. The model was verified by comparing the model-predicted real-time indoor PM_2.5 level with the actual level measured in a Fairbanks home and a good agreement (r = 0.95) was found. Then, the model was used to study three ventilation scenarios of a typical home in Fairbanks—natural ventilation, using an HRV, and using an HRV with an additional particulate filter. The external cost associated with breathing the indoor PM_2.5 was also evaluated. The scenario with an HRV and an additional filter was shown to have about $380 lower annual energy cost than the scenario with natural ventilation and the saving in the PM_2.5 associated external cost was about $690 annually. The savings were shown to exceed the operational costs of the ventilation system.     

The influence of polarized 3D display on autonomic nervous activities

The spectral analysis of Heart Rate Variability (HRV) can be used for assessing the autonomic nervous activities and further the physiological conditions of subjects. This study intended to explore whether or not people would have fatigue, faintness and other kinds of uncomfortable conditions after watching a 3D film by using HRV measures as the objective physiological indices, in addition to other subjective physiological indices.Twenty men aged 22 ± 2 experienced watching 3D films and 2D films and were served as the controls of themselves. As the controls, the subjects had to rest at the same place. All subjects were are randomized for taking different experiences, and the electrocardiographic (ECG) signals were recorded during the whole process. The researchers could obtain the indices of the autonomic nervous activities before and after experiencing 3D and 2D movies with the help of spectral HRV analyses, along with the objective physiological information. The subjects were requested to fill out the questionnaire for the subjective feelings after the movie experiences.It was found that the subjects’ high-frequency power (HFP) representing parasympathetic nervous activities decreases after watching a 3D film. The sympathetic and parasympathetic nerve activities before and after watching a 2D film were not significantly different. The subjects complained that they felt dizzy, had headaches, and got visual fatigue while watching a 3D film.This study found that the subjects’ parasympathetic nerve activities were reduced after watching a 3D film, indicating that watching a 3D film would make people uncomfortable and tired. This result was the same as that of the questionnaire. Thus, HRV analyses could be an objective physiological index for discomfort as viewing 3D films.     

Ontological fuzzy agent for electrocardiogram application

The electrocardiogram (ECG) signal is adopted extensively as a low-cost diagnostic procedure to provide information concerning the healthy status of the heart. However, the QRS complex must be calculated accurately before proceeding with the heart rate variability (HRV). In particular, the R peak needs to be detected reliably. This study presents an adaptive fuzzy detector to detect the R peak correctly. Additionally, an ontological fuzzy agent is presented to process the collection of ECG signals. The required knowledge is stored in the ontology, which comprises some personal ontologies and predefined by domain experts. The ontological fuzzy agent retrieves the ECG signals with R peaks marked for HRV analysis and ECG further applications. It contains a personal fuzzy filter, an HRV analysis mechanism, and a fuzzy normed inference engine. Moreover, the ECG fuzzy signal space and some important properties are presented to define the working environment of the agent. An experimental platform has been constructed to test the performance of the agent. The results indicate that the proposed method can work effectively.     

基于HRV分析的可穿戴心电仪精神疲劳检测

精神疲劳是许多慢性疾病如心血管疾病,糖尿病和癌症的关键原因,然而又难以量化评估及测量。本研究提出了一种通过智能穿戴设备检测脑力劳动者疲劳程度的工程可行性的方案。为了检测脑力疲劳程度,本文通过Man-Whitney U检验评估了HRV各项指标在判断精神疲劳状态的统计显著性,并使用随机森林进行特征选择以确定HRV各项指标的重要性。本文研究发现,最重要的HRV指标分别是NN.mean,PNN50,VLF,LF和TP。最后本文采用SVM、Na?ve Bayes、KNN和逻辑回归四种机器学习算法对进行疲劳状态进行识别,实验证明了KNN分类器最为有效,其交叉验证准确率为75.5％和AUC为0.74。     

啤酒泡沫蛋白质的初步分离及测定

研究了啤酒泡沫蛋白质的简便测定方法，发现Bradford法用于啤酒泡沫蛋白质的测定具有一定的可行性，测定结果与泡持值有着良好的线性关系。同时对啤酒泡沫蛋白质进行了凝胶色谱分离（Sephdex G-50)，并得到了主要的三种泡沫高分子蛋白质。     

基于情绪体验的ASD儿童干预APP角色造型研究

目的 面对我国ASD儿童数量逐年上升的情况,为干预APP角色造型设计提供优化设计思路,提出一种基于情绪体验的卡通角色造型设计研究方法。方法 首先,结合文献从20余项HRV指标中筛选出8项代表性HRV指标,通过HRV实验收集8项指标数据;其次,结合改进的语义量表整理情绪体验得分;再次,运用逐步回归分析法筛选出对情绪体验得分影响最大的3项HRV主因子;最后,建立多元回归方程,探求情绪体验得分与3项HRV指标间的回归关系。结果 研究发现,ASD儿童对三类角色中的器物角色的情绪体验反馈最为明显,且具有方圆形主体轮廓和梯形、圆形组合部件的器物角色能够提高LF/HF、SDNN,并降低M–HRT,进而获得愉悦的情绪体验。结论 融合改进的语义量表建立情绪体验与3项HRV指标间的多元线性回归模型,为ASD儿童干预APP卡通角色造型的设计与方案评价提供了理论依据,且符合ASD儿童情绪体验的角色造型设计能够激发儿童兴趣、提升交互体验、增强干预效果。     

Application level performance evaluation of wearable devices for stress classification with explainable AI

Stress has become one of the most prominent problems of modern societies and a key contributor to major health issues. Dealing with stress effectively requires detecting it in real-time, informing the user, and giving instructions on how to manage it. Over the past few years, wearable devices equipped with biosensors that can be utilized for stress detection have become increasingly popular. Since they come with various designs and technologies and acquire biosignals from different body locations, choosing a suitable device for a particular application has become a challenge for researchers and end-users. This study compares seven common wearable biosensors for stress detection applications. This was accomplished by collecting physiological sensor data during Baseline, Stress, Recovery, and Cycling sessions from 32 participants. Machine learning algorithms were used to classify four stress classes, and the results obtained from all wearables were compared. Following this, a state-of-the-art explainable artificial intelligence method was employed to clarify our models’ predictions and investigate the influence different features have on the models’ outputs. Despite the results showing that ECG wearables perform slightly better than the rest of the devices, adding a second biosignal (EDA) improved the results significantly, tipping the balance toward multisensor wearables. Finally, we concluded that although the output results of each model can be affected by various factors, in most cases, there is no significant difference in the accuracy of stress detection by different wearables. However, the decision to select a particular wearable for stress detection applications must be made carefully considering the trade-off between the users’ expectations and preferences and the pros and cons of each device.     

Sliding mode based trajectory linearization control for hypersonic reentry vehicle via extended disturbance observer

This paper proposes a novel hybrid control framework by combing observer-based sliding mode control (SMC) with trajectory linearization control (TLC) for hypersonic reentry vehicle (HRV) attitude tracking problem. First, fewer control consumption is achieved using nonlinear tracking differentiator (TD) in the attitude loop. Second, a novel SMC that employs extended disturbance observer (EDO) to counteract the effect of uncertainties using a new sliding surface which includes the estimation error is integrated to address the tracking error stabilization issues in the attitude and angular rate loop, respectively. In addition, new results associated with EDO are examined in terms of dynamic response and noise-tolerant performance, as well as estimation accuracy. The key feature of the proposed compound control approach is that chattering free tracking performance with high accuracy can be ensured for HRV in the presence of multiple uncertainties under control constraints. Based on finite time convergence stability theory, the stability of the resulting closed-loop system is well established. Also, comparisons and extensive simulation results are presented to demonstrate the effectiveness of the control strategy.     

不同天气状况下驾驶员驾驶工作负荷分析

为有效提高驾驶员在不良天气条件下的运行安全,针对晴天、中雨、中雨+雾(能见度100 m)和中雨+雾(能见度50 m)4种不同驾驶条件,利用心率变异性(HRV)指标,对驾驶员驾驶工作负荷特性进行研究.通过随机抽取3名驾驶员,在室内进行对于高速公路不同天气场景的仿真实验,对获得的数据进行统计分析发现,驾驶员在不同天气条件下驾驶工作负荷是有差异的,驾驶工作负荷随天气条件的恶劣程度增加,驾驶环境的能见度对驾驶员驾驶工作负荷影响最大.