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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.

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.

A three-dimensional (3D) printed custom-fit respirator mask has been proposed as a promising solution to alleviate mask-related injuries and supply shortages during COVID-19. However, creating a custom-fit computer-aided design (CAD) model for each mask is currently a manual process and thereby not scalable for a pandemic crisis. This paper aims to develop a novel design process to reduce overall design cost and time, thus enabling the mass customisation of 3D printed respirator masks.

Four data acquisition methods were used to collect 3D facial data from five volunteers. Geometric accuracy, equipment cost and acquisition time of each method were evaluated to identify the most suitable acquisition method for a pandemic crisis. Subsequently, a novel three-step design process was developed and scripted to generate respirator mask CAD models for each volunteer. Computational time was evaluated and geometric accuracy of the masks was evaluated via one-sided Hausdorff distance.

Respirator masks were successfully generated from all meshes, taking <2 min/mask for meshes of 50,000∼100,000 vertices and <4 min for meshes of ∼500,000 vertices. The average geometric accuracy of the mask ranged from 0.3 mm to 1.35 mm, depending on the acquisition method. The average geometric accuracy of mesh obtained from different acquisition methods ranged from 0.56 mm to 1.35 mm. A smartphone with a depth sensor was found to be the most appropriate acquisition method.

A novel and scalable mass customisation design process was presented, which can automatically generate CAD models of custom-fit respirator masks in a few minutes from a raw 3D facial mesh. Four acquisition methods, including the use of a statistical shape model, a smartphone with a depth sensor, a light stage and a structured light scanner were compared; one method was recommended for use in a pandemic crisis considering equipment cost, acquisition time and geometric accuracy.

THE partial pressure of the oxygen content in the atmosphere decreases directly with the atmospheric pressure, i.e., with the altitude, the proportion of oxygen in the atmosphere (about 20·9 per cent) remaining practically constant in the substratosphere.

Given the COVID-19 Pandemic outbreak and the role of medical textiles for protection, this study aims to identify the leading research foci on using textile materials for personal protection in pandemic situations.

A systematic review and systemic analysis of the literature on the subject were performed using the process knowledge development – constructivist (ProKnow-C) methodology.

A bibliographic portfolio with 16 relevant studies was obtained. This portfolio represents the main focus of this research field, including the main filtration mechanisms, ways of disinfecting N95 respirators and proposed methods to evaluate the filtration efficiency of different materials with potential for mask development.

To the best of the authors’ knowledge, this is the first time the ProKnow-C methodology was used in the textile field. Thus, future studies can benefit from using the Proknow-C for selecting and analyzing relevant textile studies following a systematic approach.

The purpose of this paper is to determine which criteria should be taken into account while choosing face masks for pandemic times and to what extent their effects are.

Nine face mask alternatives were evaluated based on the assessments of their performance with respect to twelve attributes. Seven experts were asked to evaluate the mask alternatives and the influences among attributes. In gathering expert judgments, spherical fuzzy number-based linguistic terms were utilized in the study to provide a more comprehensive representation domain to them.

According to the results, the most important attributes are found as material type, cost and bacteria–virus protection level. The best face mask is N95, which is followed by respirators and surgical masks.

The implication of the research is to evaluate face masks in terms of criteria such as physical, performance, protection and cost to decide on what basis they were selected as a personal protective equipment (PPE) based on expert assessments. This is useful in selection of the right face mask with optimum performance and provides guidance to the general public and profession specific groups for this purpose. The face mask companies might be also benefitted from the implications of the present study in their design and research and development (R&D) operations.

The preference ranking of the face mask alternatives has not been studied in detail yet in the literature. Focusing on this issue, the present study provides a comprehensive assessment of the selection criteria of face masks in the pandemic era.

The Secretary of State in exercise of powers conferred by sections 139(1) and 180(3) of the Factories Act 1961 and now vested in him and of all other powers enabling him in that behalf, hereby makes the following Order:—