Skip to main content
SpringerLink
Log in

Machine learning enabled film pressure sensor to identify surface contacts: An application in surface transmission of infectious disease

  • Research Article
  • Architecture and Human Behavior
  • Published:
Building Simulation Aims and scope Submit manuscript

Abstract

The global prevalence of infectious diseases has emerged as a significant challenge in recent years. Surface transmission is a potential transmission route of most gastrointestinal and respiratory infectious diseases, which is related to surface touch behaviors. Manual observation, the traditional method of surface touching data collection, is characterized by limited accuracy and high labor costs. In this work, we proposed a methodology based on machine learning technologies aimed at obtaining high-accuracy and low-labor-cost surface touch behavioral data by means of sensor-based contact data. The touch sensing device, primarily utilizing a film pressure sensor and Arduino board, is designed to automatically detect and collect surface contact data, encompassing pressure, duration and position. To make certain the surface touch behavior and to describe the behavioral data more accurately, six classification algorithms (e.g. Support Vector Machine and Random Forest) have been trained and tested on an experimentally available dataset containing more than 500 surface contacts. The classification results reported the accuracy of above 85% for all the six classifiers and indicated that Random Forest performed best in identifying surface touch behaviors, with 91.8% accuracy, 91.9% precision and 0.98 AUC. The study conclusively demonstrated the feasibility of identifying surface touch behaviors through film pressure sensor-based data, offering robust support for the calculation of viral load and exposure risk associated with surface transmission.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aftab HB, Zia B, Zahid MF, et al. (2016). Knowledge, attitude, and practices of healthcare personnel regarding the transmission of pathogens via fomites at a tertiary care hospital in Karachi, Pakistan. Open Forum Infectious Diseases, 3: ofv208.

    Article  Google Scholar 

  • Ahmad HF, Khaloofi H, Azhar Z, et al. (2021). An improved COVID-19 forecasting by infectious disease modelling using machine learning. Applied Sciences, 11: 11426.

    Article  Google Scholar 

  • Alsharif R, Arashpour M, Golafshani E, et al. (2023). Ensemble machine learning framework for daylight modelling of various building layouts. Building Simulation, 16: 2049–2061.

    Article  Google Scholar 

  • Alsunaidi SJ, Almuhaideb AM, Ibrahim NM, et al. (2021). Applications of big data analytics to control COVID-19 pandemic. Sensors, 21: 2282.

    Article  Google Scholar 

  • Ban MJ, Lee DH, Shin SW, et al. (2022). Identifying the acute toxicity of contaminated sediments using machine learning models. Environmental Pollution, 312: 120086.

    Article  Google Scholar 

  • Belgiu M, Drăgu L (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24–31.

    Article  Google Scholar 

  • Bhardwaj T, Somvanshi P (2019). Machine learning toward infectious disease treatment. In: Tanveer M, Pachori R (eds), Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing. Singapore: Springer. pp. 683–693.

    Chapter  Google Scholar 

  • Bhouri MA, Costabal FS, Wang H, et al. (2021). COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior. Computer Methods in Applied Mechanics and Engineering, 382: 113891.

    Article  MathSciNet  Google Scholar 

  • Bozdağ A, Dokuz Y, Gökçek ÖB (2020). Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey. Environmental Pollution, 263: 114635.

    Article  Google Scholar 

  • Bozdog IA, Daniel-Nicusor T, Antal M, et al. (2021). Human behavior and anomaly detection using machine learning and wearable sensors. In: Proceedings of 2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.

  • Carraturo F, Del Giudice C, Morelli M, et al. (2020). Persistence of SARS-CoV-2 in the environment and COVID-19 transmission risk from environmental matrices and surfaces. Environmental Pollution, 265: 115010.

    Article  Google Scholar 

  • Casanova V, Sousa FH, Stevens C, et al. (2018). Antiviral therapeutic approaches for human rhinovirus infections. Future Virology, 13: 505–518.

    Article  Google Scholar 

  • Chen W, Liu L, Hang J, et al. (2023). Predominance of inhalation route in short-range transmission of respiratory viruses: investigation based on computational fluid dynamics. Building Simulation, 16: 765–780.

    Article  Google Scholar 

  • Cheng P, Luo K, Xiao S, et al. (2022). Predominant airborne transmission and insignificant fomite transmission of SARS-CoV-2 in a two-bus COVID-19 outbreak originating from the same pre-symptomatic index case. Journal of Hazardous Materials, 425: 128051.

    Article  Google Scholar 

  • Cui S, Gao Y, Huang Y, et al. (2023). Advances and applications of machine learning and deep learning in environmental ecology and health. Environmental Pollution, 335: 122358.

    Article  Google Scholar 

  • Deng Q, Wang J, Hillebrand K, et al. (2020). Prediction performance of lane changing behaviors: A study of combining environmental and eye-tracking data in a driving simulator. IEEE Transactions on Intelligent Transportation Systems, 21: 3561–3570.

    Article  Google Scholar 

  • Georgieva I, Stoyanova A, Angelova S, et al. (2023). Rhinovirus genotypes circulating in Bulgaria, 2018–2021. Viruses, 15: 1608.

    Article  Google Scholar 

  • Gómez-Pulido JA, Romero-Muelas JM, Gómez-Pulido JM, et al. (2020). In: Rojas I, Valenzuela O, Rojas F, Herrera L, Ortuño F (eds), Bioinformatics and Biomedical Engineering. Predicting infectious diseases by using machine learning classifiers. Cham: Springer International Publishing. pp. 590–599.

  • Guan R, Liu W, Li N, et al. (2023). Machine learning models based on residue interaction network for ABCG2 transportable compounds recognition. Environmental Pollution, 337: 122620.

    Article  Google Scholar 

  • Herdayanti A, Rahmatsyah, Manurung SR (2020). Development of aid tool using arduino uno sensor for dynamic fluid at senior high school. Journal of Physics: Conference Series, 1485: 012003.

    Google Scholar 

  • Hindson J (2020). COVID-19: faecal–oral transmission? Nature Reviews Gastroenterology & Hepatology, 17: 259.

    Article  Google Scholar 

  • Hossain Shuvo MM, Ahmed N, Nouduri K, et al. (2020). A hybrid approach for human activity recognition with support vector machine and 1D convolutional neural network. In: Proceedings of 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

  • Ignat T, De Falco N, Berger-Tal R, et al. (2021). A novel approach for long-term spectral monitoring of desert shrubs affected by an oil spill. Environmental Pollution, 289: 117788.

    Article  Google Scholar 

  • Jin T, Chen X, Nishio M, et al. (2022). Interventions to prevent surface transmission of an infectious virus based on real human touch behavior: A case study of the norovirus. International Journal of Infectious Diseases, 122: 83–92.

    Article  Google Scholar 

  • Kanamori H, Weber DJ, Rutala WA (2021). Role of the healthcare surface environment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and potential control measures. Clinical Infectious Diseases, 72: 2052–2061.

    Article  Google Scholar 

  • Kaur J, Bala A (2019). A hybrid energy management approach for home appliances using climatic forecasting. Building Simulation, 12: 1033–1045.

    Article  Google Scholar 

  • Keshavamurthy R, Dixon S, Pazdernik KT, et al. (2022). Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches. One Health, 15: 100439.

    Article  Google Scholar 

  • Kim SJ, Bae SJ, Jang MW (2022). Linear regression machine learning algorithms for estimating reference evapotranspiration using limited climate data. Sustainability, 14: 11674.

    Article  Google Scholar 

  • King M-F, López-García M, Atedoghu KP, et al. (2020). Bacterial transfer to fingertips during sequential surface contacts with and without gloves. Indoor Air, 30: 993–1004.

    Article  Google Scholar 

  • Koushik C, Shreyas Madhav AV, Singh RK (2021). An Efficient Approach to Microarray Data Classification using Elastic Net Feature Selection, SVM and RF. Journal of Physics: Conference Series, 1911: 012010.

    Google Scholar 

  • Lay KS, Li L, Okutsu M (2022). High altitude balloon testing of Arduino and environmental sensors for CubeSat prototype. HardwareX, 12: e00329.

    Article  Google Scholar 

  • Lei H, Li Y, Xiao S, et al. (2018). Routes of transmission of influenza A H1N1, SARS CoV, and norovirus in air cabin: Comparative analyses. Indoor Air, 28: 394–403.

    Article  Google Scholar 

  • Lei H, Xiao S, Cowling BJ, et al. (2020). Hand hygiene and surface cleaning should be paired for prevention of fomite transmission. Indoor Air, 30: 49–59.

    Article  Google Scholar 

  • Lindner C, Bromiley PA, Ionita MC, et al. (2015). Robust and accurate shape model matching using random forest regression-voting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37: 1862–1874.

    Article  Google Scholar 

  • Liu K-Y, Xu C-S, Jia K-M, et al. (2020). Measurement of earth pressures on curved surface of thin film pressure sensor. Chinese Journal of Geotechnical Engineering, 42(3): 584–591. (in Chinese)

    Google Scholar 

  • Liu Y, Tian W, Zhou X (2021). Energy and carbon performance of urban buildings using metamodeling variable importance techniques. Building Simulation, 14: 535–547.

    Article  Google Scholar 

  • Liu Y, Dong B (2024). Modeling urban scale human mobility through big data analysis and machine learning. Building Simulation, 17: 3–21.

    Article  Google Scholar 

  • Mansbridge N, Mitsch J, Bollard N, et al. (2018). Feature selection and comparison of machine learning algorithms in classification of grazing and rumination behaviour in sheep. Sensors, 18: 3532.

    Article  Google Scholar 

  • Mayer LM, Strich JR, Kadri SS, et al. (2022). Machine learning in infectious disease for risk factor identification and hypothesis generation: Proof of concept using invasive candidiasis. Open Forum Infectious Diseases, 9: ofac401.

    Article  Google Scholar 

  • Mikola E, Palomares O, Turunen R, et al. (2019). Rhinovirus species and tonsillar immune responses. Clinical and Translational Allergy, 9: 63.

    Article  Google Scholar 

  • Miura K, Owashi M, Mihara Y (2017). High durability thin-film pressure sensor development for engine sliding surface. In: Proceedings of the 9th International Conference on Modeling and Diagnostics for Advanced Engine Systems.

  • Mizukoshi A, Nakama C, Okumura J, et al. (2021). Assessing the risk of COVID-19 from multiple pathways of exposure to SARS-CoV-2: Modeling in health-care settings and effectiveness of nonpharmaceutical interventions. Environment International, 147: 106338.

    Article  Google Scholar 

  • Peiffer-Smadja N, Rawson TM, Ahmad R, et al. (2020). Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clinical Microbiology and Infection, 26: 584–595.

    Article  Google Scholar 

  • Praveena Rachel Kamala S, Gayathri S, Pillai NM, et al. (2023). Predictive analytics for heart disease detection: A machine learning approach. In: Proceedings of the 4th International Conference on Electronics and Sustainable Communication Systems (ICESC).

  • Qian L, Yu C (2023). Pressure distribution in the drafting zone measured by film pressure sensors. Textile Research Journal, 93: 1815–1823.

    Article  Google Scholar 

  • Ramesh Kumar P, Vijaya A (2022). Naïve Bayes machine learning model for image classification to assess the level of deformation of thin components. Materials Today: Proceedings, 68: 2265–2274.

    Google Scholar 

  • Razavi-Termeh SV, Sadeghi-Niaraki A, Naqvi RA, et al. (2023). Dust detection and susceptibility mapping by aiding satellite imagery time series and integration of ensemble machine learning with evolutionary algorithms. Environmental Pollution, 335: 122241.

    Article  Google Scholar 

  • Reis Pinheiro CA, Galati M, Summerville N, et al. (2021). Using network analysis and machine learning to identify virus spread trends in COVID-19. Big Data Research, 25: 100242.

    Article  Google Scholar 

  • Sheykhmousa M, Mahdianpari M, Ghanbari H, et al. (2020). Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 6308–6325.

    Article  Google Scholar 

  • Sowmya A, Pillai AS (2021). Human fall detection with wearable sensors using ML algorithms. In: Proceedings of the 2nd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India.

  • Sunarya U, Sun Hariyani Y, Cho T, et al. (2020). Feature analysis of smart shoe sensors for classification of gait patterns. Sensors, 20: 6253.

    Article  Google Scholar 

  • Susilo J, Febriani A, Rahmalisa U, et al. (2021). Car parking distance controller using ultrasonic sensors based on Arduino Uno. Journal of Robotics and Control (JRC), 2: 353–356.

    Article  Google Scholar 

  • Tian K, Sui G, Yang P, et al. (2020). Ultrasensitive thin-film pressure sensors with a broad dynamic response range and excellent versatility toward pressure, vibration, bending, and temperature. ACS Applied Materials & Interfaces, 12: 20998–21008.

    Article  Google Scholar 

  • Tian W, Zhu C, Sun Y, et al. (2021). Energy characteristics of urban buildings: Assessment by machine learning. Building Simulation, 14: 179–193.

    Article  Google Scholar 

  • Wang F, You R, Zhang T, et al. (2022). Recent progress on studies of airborne infectious disease transmission, air quality, and thermal comfort in the airliner cabin air environment. Indoor Air, 32: e13032.

    Article  Google Scholar 

  • Watanabe T, Bartrand TA, Weir MH, et al. (2010). Development of a dose-response model for SARS coronavirus. Risk Analysis, 30: 1129–1138.

    Article  Google Scholar 

  • WHO (2020). Global Health Estimates (GHE): The top 10 causes of death. World Health Organization. Available at https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death. Accessed 9 Dec 2020.

  • Wilson AM, King M-F, López-García M, et al. (2021). Effects of patient room layout on viral accruement on healthcare professionals’ hands. Indoor Air, 31: 1657–1672.

    Article  Google Scholar 

  • Xia L, Song B, Jing Z, et al. (2018). Dynamical interaction between information and disease spreading in populations of moving agents. Computers, Materials & Continua, 57: 123–144.

    Article  Google Scholar 

  • Xie Y, Ishida Y, Hu J, et al. (2022). A backpropagation neural network improved by a genetic algorithm for predicting the mean radiant temperature around buildings within the long-term period of the near future. Building Simulation, 15: 473–492.

    Article  Google Scholar 

  • Yan T, Zhou A, Shen S-L (2023). Prediction of long-term water quality using machine learning enhanced by Bayesian optimisation. Environmental Pollution, 318: 120870.

    Article  Google Scholar 

  • Yang X (2021). Survey for performance measure index of classification learning algorithm. Computer Science, 48(8): 209–219. (in Chinese)

    Google Scholar 

  • You R, Lin C-H, Wei D, et al. (2019). Evaluating the commercial airliner cabin environment with different air distribution systems. Indoor Air, 29: 840–853.

    Article  Google Scholar 

  • Zhang N, Li Y, Huang H (2018). Surface touch and its network growth in a graduate student office. Indoor Air, 28: 963–972.

    Article  Google Scholar 

  • Zhang N, Chen X, Jia W, et al. (2021a). Evidence for lack of transmission by close contact and surface touch in a restaurant outbreak of COVID-19. The Journal of Infection, 83: 207–216.

    Article  Google Scholar 

  • Zhang N, Wang P, Miao T, et al. (2021b). Real human surface touch behavior based quantitative analysis on infection spread via fomite route in an office. Building and Environment, 191: 107578.

    Article  Google Scholar 

  • Zhang J, Zhao T, Zhou X, et al. (2022). Room zonal location and activity intensity recognition model for residential occupant using passive-infrared sensors and machine learning. Building Simulation, 15: 1133–1144.

    Article  Google Scholar 

  • Zhao P, Li Y (2021). Modeling and experimental validation of microbial transfer via surface touch. Environmental Science & Technology, 55: 4148–4161.

    Article  Google Scholar 

  • Zhao P (2023). Analysis of COVID-19 clusters involving vertical transmission in residential buildings in Hong Kong. Building Simulation, 16: 701–711.

    Article  Google Scholar 

  • Zhuang L, Ding Y, Zhou L, et al. (2023). Fomite transmission in airports based on real human touch behaviors. Buildings, 13: 2582

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant No. 52108067).

Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, 100124, China

    Baotian Chang, Jianchao Zhang, Yingying Geng, Jiarui Li, Doudou Miao & Nan Zhang

Authors
  1. Baotian Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianchao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingying Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiarui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Doudou Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have contributed to the study reported in the manuscript. Literature collection and preparation were performed by Baotian Chang. Material preparation, data collection and analysis were performed by Baotian Chang, Jianchao Zhang, Yingying Geng, Jiarui Li and Doudou Miao. The manuscript was written by Baotian Chang, manuscript review and comment were conducted by Nan Zhang. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nan Zhang.

Ethics declarations

Declaration of competing interest

The authors have no competing interests to declare that are relevant to the content of this article.

Ethical approval

This experiment was authorized by the Ethics Committee of Beijing University of Technology (No. CJXB09).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, B., Zhang, J., Geng, Y. et al. Machine learning enabled film pressure sensor to identify surface contacts: An application in surface transmission of infectious disease. Build. Simul. (2024). https://doi.org/10.1007/s12273-024-1132-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12273-024-1132-7

Keywords

Search

Navigation