Recognizing the breathing resistances of wearing respirators from respiratory and sEMG signals with artificial neural networks

Zhong, Yang; Chen, Yumiao; Wang, Jianping; Gong, Rong

doi:10.1016/j.ergon.2017.02.001

Cited by 9 publications

(7 citation statements)

References 45 publications

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“…In the same study, it is also emphasized that there is an average decrease of 37% in air exchange volume due to the use of N95 masks. In their study, Yang et al used surface EMG signals and respiratory signals to reveal the breathing resistance that occurs due to the N95 mask [ 18 ]. All these studies prove that breathing performed through masks differs from physiological respiration.…”

Section: Discussionmentioning

confidence: 99%

The effect of N95 and surgical masks on mucociliary clearance function and sinonasal complaints

Cengiz

Can

2021

Eur Arch Otorhinolaryngol

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Purpose The aim of this study was to reveal the effect of N95 and surgical masks on mucociliary clearance function and sinonasal complaints. Methods Sixty participants were enrolled in this study, including 30 people in N95 mask group and 30 people in surgical mask group. Two interviews, three days apart, were performed with all participants. The participants were asked not to use any mask before the first interview while they were asked to use the determined mask just before the second interview for 8 h. In both interviews, the mucociliary clearance times (MCTs) were measured and participants were asked to score ten distinct sinonasal complaints using visual analog scale (VAS). Data obtained from first interview were named pre-mask data, data obtained from second interview were called after-mask data. In both groups, pre-mask MCTs and VAS scores were compared with after-mask MCTs and VAS scores. Results After-mask MCTs (mean = 13.03 ± 6.05 min) were significantly longer than pre-mask MCTs (mean = 10.19 ± 4.21 min) in N95 mask group ( p = 0.002). No significant difference was found between after-mask and pre-mask MCTs (mean = 12.05 ± 5.21 min, mean = 11.00 ± 5.44 min, respectively) in surgical mask group ( p = 0.234). When after-mask VAS scores were compared with pre-mask VAS scores, it was found that N95 mask use increased nasal blockage and postnasal discharge, surgical mask usage increased nasal blockage. Conclusion While the use of N95 mask leads to nasal blockage and postnasal discharge, surgical mask use results in nasal blockage. N95 masks cause impairment in mucociliary clearance function. But all these effects are mild. Surgical masks have not been found to have any effect on mucociliary clearance function.

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Section: Discussionmentioning

confidence: 99%

The effect of N95 and surgical masks on mucociliary clearance function and sinonasal complaints

Cengiz

Can

2021

Eur Arch Otorhinolaryngol

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“…The garment contains holes for connecting with electrodes to collect sEMG signals and the location of the holes are based on the selected experimental muscles. According to our previous research studies, the abdominal and the scalene are the two primary respiratory muscles affected by using N95 FFRs (Chen et al , 2016; Yang et al , 2017). Thus, the respiratory muscles selected in this research are the abdominal and the scalene muscles.…”

Section: Methodsmentioning

confidence: 99%

“…From sEMG signals, time domain features such as aEMG, VAR and RMS (Baspinar et al , 2013; Potvin, 1997; Rengasamy et al , 2009; Ren et al , 2004) were recorded as input parameters. The formulas can be found in our previous research studies (Chen et al , 2016; Yang et al , 2017).…”

Section: Methodsmentioning

confidence: 99%

“…According to our previous research (Chen et al , 2016), the relationship between respiratory muscle and respiratory signal responses and breathing resistances are very difficult to describe quantitatively by linear regression due to the nonlinearity of the parameters and a large number of variables involved. Using non-linear techniques in this area has been verified to be feasible (Yang et al , 2017). The GEP has many advantages in comparison with other non-linear models.…”

Section: Methodsmentioning

confidence: 99%

“…As the development of biosensor, the objective methods based on physiological data from actual human subjects have the advantages of low cost, high efficiency, non-invasive, arising the attentions from many researchers (Chen et al , 2016; Yang et al , 2017). Our previous research (Yang et al , 2017) indicated that artificial neural networks are effective for recognizing different airway resistances of wearing respirators from respiratory signals (RSP) and surface electromyography (sEMG). In this study, we developed a monitoring system and investigated the use of the gene expression programming (GEP) to construct the predictive model of subjective breathing resistances of wearing respirators via sEMG and RSP.…”

Section: Introductionmentioning

confidence: 99%

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GEP-based predictive modeling of breathing resistances of wearing respirators on human body via sEMG and RSP sensors

Chen

Yang

2019

Self Cite

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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

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Effect of shoulder angle variation on sEMG-based elbow joint angle estimation

Tang

Yang

Zhang

et al. 2018

International Journal of Industrial Ergonomics

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Recognizing the breathing resistances of wearing respirators from respiratory and sEMG signals with artificial neural networks

Cited by 9 publications

References 45 publications

The effect of N95 and surgical masks on mucociliary clearance function and sinonasal complaints

The effect of N95 and surgical masks on mucociliary clearance function and sinonasal complaints

GEP-based predictive modeling of breathing resistances of wearing respirators on human body via sEMG and RSP sensors

Effect of shoulder angle variation on sEMG-based elbow joint angle estimation

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