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

PurposeBreathing 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/approachThe 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.FindingsThe 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/valueThis 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.

[1]  J. Gliem,et al.  Calculating, Interpreting, And Reporting Cronbach’s Alpha Reliability Coefficient For Likert-Type Scales , 2003 .

[2]  Rosemary R. Seva,et al.  Designing an improved respirator for automotive painters , 2014 .

[3]  E. Newton,et al.  Effects of wearing N95 and surgical facemasks on heart rate, thermal stress and subjective sensations , 2005, International archives of occupational and environmental health.

[4]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

[5]  Jeffrey B. Powell,et al.  Evaluation of N95 respirator use with a surgical mask cover: effects on breathing resistance and inhaled carbon dioxide. , 2013, The Annals of occupational hygiene.

[6]  Guangyou Xu,et al.  Subject-independent natural action recognition , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[7]  J. Potvin.  Effects of muscle kinematics on surface EMG amplitude and frequency during fatiguing dynamic contractions. , 1997, Journal of applied physiology.

[8]  Qingyu Meng,et al.  What can individuals do to reduce personal health risks from air pollution? , 2015, Journal of thoracic disease.

[9]  Richard W. Bohannon.  Comfortable and maximum walking speed of adults aged 20-79 years: reference values and determinants. , 1997, Age and ageing.

[10]  W P Morgan,et al.  Psychological problems associated with the wearing of industrial respirators: a review. , 1983, American Industrial Hygiene Association journal.

[11]  Vahid Nourani,et al.  State-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming , 2018, Measurement.

[12]  Carlo J. De Luca,et al.  Use of the surface EMG signal for performance evaluation of back muscles , 1993 .

[13]  Yuewei Liu,et al.  Fitting Characteristics of N95 Filtering-Facepiece Respirators Used Widely in China , 2014, PloS one.

[14]  O. Kisi,et al.  Comparison of Gene Expression Programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain) , 2012 .

[15]  Amir Hossein Alavi,et al.  Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement , 2017 .

[16]  Mary-Louise McLaws,et al.  Protecting healthcare workers from pandemic influenza: N95 or surgical masks? , 2010, Critical care medicine.

[17]  R. Shaffer,et al.  Comparison of nanoparticle filtration performance of NIOSH-approved and CE-marked particulate filtering facepiece respirators. , 2009, The Annals of occupational hygiene.

[18]  Weimin Xiao,et al.  Evolving accurate and compact classification rules with gene expression programming , 2003, IEEE Trans. Evol. Comput..

[19]  Jianping Wang,et al.  Surface EMG based handgrip force predictions using gene expression programming , 2016, Neurocomputing.

[20]  Volkan Y. Senyurek,et al.  Performance Comparison of Artificial Neural Network and Gaussian Mixture Model in Classifying Hand Motions by Using sEMG Signals , 2013 .

[21]  Wolfgang Ellermeier,et al.  On the “absoluteness” of category and magnitude scales of pain , 1991, Perception & psychophysics.

[22]  Edward Newton,et al.  Evaluation on masks with exhaust valves and with exhaust holes from physiological and subjective responses. , 2008, Journal of physiological anthropology.

[23]  J G Jones,et al.  The physiological cost of wearing a disposable respirator. , 1991, American Industrial Hygiene Association journal.

[24]  Zhongliang Yang,et al.  A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity , 2017, Front. Neurosci..

[25]  Y Zhou,et al.  Face masks to prevent transmission of influenza virus: a systematic review , 2010, Epidemiology and Infection.

[26]  Lewis R Goldfrank,et al.  Novel H1N1 influenza and respiratory protection for health care workers. , 2009, The New England journal of medicine.

[27]  Harry W. Agius,et al.  Analysing user physiological responses for affective video summarisation , 2009, Displays.

[28]  Wenqi Shen,et al.  Validity and reliability of rating scales for seated pressure discomfort , 1997 .

[29]  Jianping Wang,et al.  Physiological and subjective responses to breathing resistance of N95 filtering facepiece respirators in still-sitting and walking , 2016 .

[30]  L. Cronbach,et al.  Construct validity in psychological tests. , 1955, Psychological bulletin.

[31]  Heow Pueh Lee,et al.  Objective assessment of increase in breathing resistance of N95 respirators on human subjects. , 2011, The Annals of occupational hygiene.

[32]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[33]  Jianping Wang,et al.  The human factors/ergonomics studies for respirators: a review and future work , 2015 .

[34]  Tiina Reponen,et al.  Filter Performance of N99 and N95 Facepiece Respirators Against Viruses and Ultrafine Particles , 2008, The Annals of occupational hygiene.