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The purpose of this study is to develop a piezoresistive absolute micro-pressure sensor for altimetry. For this application, both high sensitivity and high overload resistance are required. To develop a piezoresistive absolute micro-pressure sensor for altimetry, both high sensitivity and high-overload resistance are required. The structure design and optimization are critical for achieving the purpose. Besides, the study of dynamic performances is important for providing a solution to improve the accuracy under vibration environments.

An improved structure is studied through incorporating sensitive beams into the twin-island-diaphragm structure. Equations about surface stress and deflection of the sensor are established by multivariate fittings based on the ANSYS simulation results. Structure dimensions are determined by MATLAB optimization. The silicon bulk micromachining technology is utilized to fabricate the sensor prototype. The performances under both static and dynamic conditions are tested.

Compared with flat diaphragm and twin-island-diaphragm structures, the sensor features a relatively high sensitivity with the capacity of suffering atmosphere due to the introduction of sensitive beams and the optimization method used.

An improved sensor prototype is raised and optimized for achieving the high sensitivity and the capacity of suffering atmosphere simultaneously. A general optimization method is proposed based on the multivariate fitting results. To simplify the calculation, a method to linearize the nonlinear fitting and optimization problems is presented. Moreover, a differential readout scheme attempting to decrease the dynamic interference is designed.

Investigating the subjective breathing resistance of wearing respirators requires a valid and reliable technique to measure breathing resistance. The purpose of this study is to test the validity and reliability of several rating scales and select the best for investigation of breathing resistance.

The authors designed three scales, that is, BRX scale, CP-100 scale and RVAS scale, and 30 subjects were separated into three groups, each group with a different scale. They sat for 5 min and walked for 5 min while wearing three models of respirators. After each trial, subjects were asked to complete subjective ratings of breathing resistance. Reliability was examined by the coefficient of Cronbach’s α, and validity was examined through content validity, discriminant validity and criterion validity. Generally, subjects were capable of reporting their sensation of breathing resistance by using the rating scale technique. However, the accuracy of rating strongly depended upon the properties of the scale.

The CP-100 scale was found to be highly reliable and most valid for rating subjective breath resistance. The validated CP-100 scale is very sensitive and accurate.

This is the first paper to select the best subjective scale for investigation of breathing resistance of respirators. The CP-100 scale will find wide applications in subjective breathing resistance evaluation for the use of respirators in industrial benchmarking activities. It will introduce the human factor engineering into the respirator manufacturing to improve the comfort of respirators.

Breathing resistance is the main factor that influences the wearing comfort of respirators. This paper aims to demonstrate the feasibility of using the gene expression programming (GEP) for the purpose of predicting subjective perceptions of breathing resistances of wearing respirators via surface electromyography (sEMG) and respiratory signals (RSP) sensors.

The authors developed a physiological signal monitoring system with a specific garment. The inputs included seven physical measures extracted from (RSP) and (sEMG) signals. The output was the subjective index of breathing resistances of wearing respirators derived from the category partitioning-100 scale with proven levels of reliability and validity. The prediction model was developed and validated using data collected from 30 subjects and 24 test combinations (12 respirator conditions × 2 motion conditions). The subjects evaluated 24 conditions of breathing resistances in repeated measures fashion.

The results show that the GEP model can provide good prediction performance (R² = 0.71, RMSE = 0.11). This study demonstrates that subjective perceptions of breathing resistance of wearing respirators on the human body can be predicted using the GEP via sEMG and RSP in real-time, at little cost, non-invasively and automatically.

This is the first paper suggesting that subjective perceptions of subjective breathing resistances can be predicted from sEMG and RSP sensors using a GEP model, which will remain helpful to the scientific community to start further human-centered research work and product development using wearable biosensors and evolutionary algorithms.

The purpose of this paper is to provide an overview of the human factors/ergonomics (HFE) studies for respirator.

This review paper describes and discusses the various factors and methodologies of HFE, for the purpose of better considering human factors, used in respirator studies and further human-centered product development.

Many attempts have been made to study human factors for respirators mainly including fit, human performance, comfort, and mood. Physical, psychological, and physiological indices of people are extremely valuable to HFE studies for respirator. Objective and subjective measures were methodologies widely used. Quantitative and qualitative approaches were adopted to illustrate the human performance and well-being influenced by respirators. A summary table presented with major methods used for indices of respirators in the field of HFE. According to the current researches, this review indicated three particular challenges facing HFE studies of respirators now.

With the ever increasing role of protection from air pollution in society, respirator has become an increasingly important part of our daily lives. HFE intervene in optimizing the relationships between respirators and the human using them. Plenty of efforts have been dedicated for the development of protection capability, but HFE studies for respirators are lacking. In recent years, there has been a tremendous interest in introducing HFE research methods that can evaluate respirators from the perspective of human and translate them into constraints for designing human-centered respirators.

This is a first paper in the field of HFE studies for respirator, which will remain helpful to the scientific community to start further human-centered research work and product development.

Human gait analysis is based on a significant part of the musculoskeletal, nervous and respiratory systems. Gait analysis is widely adopted to help patients increase community involvement and independent living.

This paper presents a system for the classification of abnormal human gaits using a Markerless 3D Motion Capture device. This study aims at examining and estimating the spatiotemporal and kinematic parameters obtained by 3D gait analysis in diverse groups of gait-impaired subjects and compares the parameters with that of healthy participants to interpret the gait patterns.

The classification is based on mathematical models that distinguish between normal and abnormal gait patterns depending on the deviations in the gait parameters. The difference between the gait measures of the control and each disease group was examined using 95% limits of agreement by the Bland and Altman method. The scatter plots demonstrated gait variability in Parkinsonian and ataxia gait and knee joint angle variation in hemiplegic gait when compared with those of healthy controls. To prove the validity of the Kinect camera, significant correlations were detected between Kinect- and inertial-based gait tests.

The various techniques used for gait assessments are often high in price and have existing limitations like the hindrance of components. The results suggest that the Kinect-based gait assessment techniques can be used as a low-cost, less-intrusive alternative to expensive infrastructure gait lab tests in the clinical environment.