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A Machine Learning Enabled Mobile Application to Analyse Ambient-Body Correlations

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Abstract

Ambient factors and living conditions have the capacity to cause mental and/or physical diseases if they are not properly managed. This paper studies the impact of ambient factors on body parameters. For this, we design a mobile application that collects ambient features and body data samples via a Bluetooth-enabled sensory system to train machine learning prediction models. It uses random forest, linear regression, and boosted tree techniques to predict the body parameters based on the ambient conditions. The machine learning models are evaluated and compared in terms of prediction accuracy and RMSE to find the best-fitted prediction approach. According to the results, the boosted tree model outperforms random forest and linear regression and gives the best prediction accuracy.

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References

  1. Public health and environment. Website. https://www.who.int/data/gho/data/themes/public-health-and-environment. 2016. Accessed July 2021.

  2. Gülşen M, Aydıngülü N, Arslan S. Physiological and psychological effects of ambient noise in operating room on medical staff. ANZ J Surg. 2021;91(5):847–53. https://doi.org/10.1111/ans.16582.

    Article  Google Scholar 

  3. Yao Y, Lian Z, Liu W, Shen Q. Experimental study on physiological responses and thermal comfort under various ambient temperatures. Physiol Behav. 2008;93(1–2):310–21. https://doi.org/10.1016/j.physbeh.2007.09.012.

    Article  Google Scholar 

  4. Goad N, Gawkrodger DJ. Ambient humidity and the skin: the impact of air humidity in healthy and diseased states. J Eur Acad Dermatol Venereol. 2016;30(8):1285–94. https://doi.org/10.1111/jdv.13707.

    Article  Google Scholar 

  5. Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E. Environmental and health impacts of air pollution: a review. Front Public Health. 2020. https://doi.org/10.3389/fpubh.2020.00014.

    Article  Google Scholar 

  6. Moses JC, Adibi S, Islam SMS, Wickramasinghe N, Nguyen L. Application of smartphone technologies in disease monitoring: a systematic review. Healthcare. 2021;9(7):889. https://doi.org/10.3390/healthcare9070889.

    Article  Google Scholar 

  7. Pongnumkul S, Chaovalit P, Surasvadi N. Applications of smartphone-based sensors in agriculture: a systematic review of research. J Sens. 2015;2015:1–18. https://doi.org/10.1155/2015/195308.

    Article  Google Scholar 

  8. Ardakani SP. Wireless sensor network routing protocols for data aggregation. PhD thesis, Computer Science, University of Bath, UK. 014.

  9. Ardakani SP. MINDS: mobile agent itinerary planning using named data networking in wireless sensor networks. J Sens Actuator Netw. 2021;10(2):28. https://doi.org/10.3390/jsan10020028.

    Article  Google Scholar 

  10. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y. LightGBM: a highly efficient gradient boosting ecision tree. In: 31st conference on neural information processing systems (NIPS 2017), Long Beach, December 2017. p. 4–9.

  11. Brownlee J. Linear regression for machine learning. Machine Learning Mastery. https://machinelearningmastery.com/linear-regression-for-machine-learning/. 2016. Accessed July 2021.

  12. Bahnsen A. Machine learning algorithms: introduction to random forests. Dataversity. https://www.dataversity.net/machine-learning-algorithms-introduction-random-forests/. 2017. Accessed July 2021. 2017.

  13. Choi S-G, Cho S-B. Bayesian networks + reinforcement learning: controlling group emotion from sensory stimuli. Neurocomputing. 2020;391:355–64. https://doi.org/10.1016/j.neucom.2018.09.109.

    Article  Google Scholar 

  14. Ijzerman H, Heine ECE, Nagel SK, Pronk TM. Modernizing relationship therapy through social thermoregulation theory: evidence, hypotheses, and explorations. Front Psychol. 2017. https://doi.org/10.3389/fpsyg.2017.00635.

    Article  Google Scholar 

  15. Min J, Min K. Night noise exposure and risk of death by suicide in adults living in metropolitan areas. Depress Anxiety. 2018;35(9):876–83. https://doi.org/10.1002/da.22789.

    Article  Google Scholar 

  16. Yao X, Song Y, Vink P. Effect of scent on comfort of aircraft passengers. Work. 2021;68:273–80. https://doi.org/10.3233/WOR-208025.

    Article  Google Scholar 

  17. Bogicevic V, Yang W, Cobanoglu C, Bilgihan A, Bujisic M. Traveler anxiety and enjoyment: the effect of airport environment on traveler’s emotions. J Air Transp Manag. 2016;57:122–9. https://doi.org/10.1016/j.jairtraman.2016.07.019.

    Article  Google Scholar 

  18. Heat and Health. Website. https://www.who.int/news-room/fact-sheets/detail/climate-change-heat-and-health. 2018. Accessed July 2021.

  19. Mullins JT, White C. Temperature and mental health: evidence from the spectrum of mental health outcomes. J Health Econ. 2019;68:102240. https://doi.org/10.1016/j.jhealeco.2019.102240.

    Article  Google Scholar 

  20. Sarofim M, Saha S, Hawkins M, Mills D, Hess J, Horton R, Kinney P, Schwartz J, Juliana AS, et al. Ch. 2: temperature-related death and illness. Technical report, US Global Change Research Program, Washington, DC. 2016.

  21. Xiong J, Lian Z, Zhang H, Yoshino H. Correlation between health discomforts and temperature steps in winter of China. Build Environ. 2017;114:387–96. https://doi.org/10.1016/j.buildenv.2016.11.038.

    Article  Google Scholar 

  22. Mallick J, Rahman A. Impact of population density on the surface temperature and micro-climate of Delhi. Curr Science. 2012;1708–13.

  23. Ardakani SP, Liu X, Xie H. A data-driven study to highlight the correlations between ambient factors and emotion. In: 5th EAI international conference on computer science: applications in engineering and health services (COMPSE 2021), Mexico City, Mexico (online). 2021. p. 29–30.

  24. Khanjani N, Bahrampour A. Temperature and cardiovascular and respiratory mortality in desert climate. A case study of Kerman, Iran. Iran J Environ Health Sci Eng. 2013. https://doi.org/10.1186/1735-2746-10-11.

    Article  Google Scholar 

  25. Hunashal RB, Patil YB. Assessment of noise pollution indices in the city of Kolhapur, India. Proc Soc Behav Sci. 2012;37:448–57.

    Article  Google Scholar 

  26. Jariwala HJ, Syed HS, Pandya MJ, Gajera YM. Noise pollution & human health: a review. noise and air pollutions: challenges and opportunities. Ahmedabad: LD College of Engineering; 2017.

    Google Scholar 

  27. Beutel ME, Jünger C, Klein EM, Wild P, Lackner K, Blettner M, Binder H, Michal M, Wiltink J, Brähler E, Münzel T. Noise annoyance is associated with depression and anxiety in the general population—the contribution of aircraft noise. PLoS One. 2016;11(5):0155357. https://doi.org/10.1371/journal.pone.0155357.

    Article  Google Scholar 

  28. Rouch I, Marescaux C, Padovan C, D’Amato T, Saitta B, Laurent B, Rey R, Lepetit A, Dorey JM. Hospitalisation for bipolar disorder: comparison between young and elderly patients. Psychology. 2015;06(01):126–31. https://doi.org/10.4236/psych.2015.61011.

    Article  Google Scholar 

  29. Hjetland GJ, Kolberg E, Pallesen S, Thun E, Nordhus IH, Bjorvatn B, Flo-Groeneboom E. Ambient bright light treatment improved proxy-rated sleep but not sleep measured by actigraphy in nursing home patients with dementia: a placebo-controlled randomised trial. BMC Geriatr. 2021. https://doi.org/10.1186/s12877-021-02236-4.

    Article  Google Scholar 

  30. Dobres J, Chahine N, Reimer B. Effects of ambient illumination, contrast polarity, and letter size on text legibility under glance-like reading. Appl Ergon. 2017;60:68–73. https://doi.org/10.1016/j.apergo.2016.11.001.

    Article  Google Scholar 

  31. Amato F. Non-exhaust emissions: an urban air quality problem for public health; impact and mitigation measures. London: Academic Press; 2018.

    Google Scholar 

  32. WHO: Review of evidence on health aspects of air pollution–revihaap project. Technical report. 2013.

  33. Wei F, Wu M, Qian S, Li D, Jin M, Wang J, Shui L, Lin H, Tang M, Chen K. Association between short-term exposure to ambient air pollution and hospital visits for depression in China. Sci Total Environ. 2020. https://doi.org/10.1016/j.scitotenv.2020.138207.

    Article  Google Scholar 

  34. Banzi M, Shiloh M. Getting started with arduino: the open source electronics prototyping platform. Sebastopol: Maker Media, Inc.; 2014.

    Google Scholar 

  35. Gay W. Dht11 sensor. Advanced Raspberry Pi. 2018. p. 399–418.

  36. Bakker J, Pechenizkiy M, Sidorova N. What’s your current stress level? Detection of stress patterns from gsr sensor data. In: 2011 IEEE 11th international conference on data mining workshops. IEEE: 2011. p. 573–80.

  37. Rianti M. Rancang bangun alat ukur intensitas cahaya dengan menggunakan sensor bh1750 berbasis arduino. 2017.

  38. Mankar J, Darode C, Trivedi K, Kanoje M, Shahare P. Review of i2c protocol. Int J Res Advent Technol. 2014;2(1).

  39. Wang L, Luo H. Design of intelligent vehicle for distribution system based on speech recognition. In: 2017 3rd IEEE international conference on computer and communications (ICCC). IEEE; 2017. p. 2807–2811.

  40. Tosi J, Taffoni F, Santacatterina M, Sannino R, Formica D. Performance evaluation of bluetooth low energy: a systematic review. Sensors. 2017;17(12):2898.

    Article  Google Scholar 

  41. Geert R. Pearson correlations—quick introduction. SPSS Tutorials. https://www.spss-tutorials.com/pearson-correlation-coefficient/. 2020. Accessed July 2021.

  42. Chai T, Draxler RR. Root mean square error (rmse) or mean absolute error (mae). Geosci Model Dev Discuss. 2014;7(1):1525–34.

    Google Scholar 

  43. Coulston JW, Blinn CE, Thomas VA, Wynne RH. Approximating prediction uncertainty for random forest regression models. Photogramm Eng Remote Sens. 2016;82(3):189–97.

    Article  Google Scholar 

  44. Elith J, Leathwick JR, Hastie T. A working guide to boosted regression trees. J Anim Ecol. 2008;77(4):802–13.

    Article  Google Scholar 

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Author notes

  1. Hongcheng Xie and Saeid Pourroostaei Ardakani contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science, University of Nottingham Ningbo China, 199 Taikang Street, Ningbo, 315100, Zheijiang, China

    Hongcheng Xie & Saeid Pourroostaei Ardakani

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  1. Hongcheng Xie
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  2. Saeid Pourroostaei Ardakani
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Correspondence to Saeid Pourroostaei Ardakani.

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Xie, H., Ardakani, S.P. A Machine Learning Enabled Mobile Application to Analyse Ambient-Body Correlations. SN COMPUT. SCI. 3, 144 (2022). https://doi.org/10.1007/s42979-022-01027-x

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