Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies

Environmental Monitoring and Assessment

Volumn 190, Issue 3, 2018, Pages

  Stamatelopoulou, Asimina ^a,b   Chapizanis, D ^c   Karakitsios, S ^c   Kontoroupis, P ^c   Asimakopoulos, D N ^b   Maggos, T ^a   Sarigiannis, D ^c,d  

^a INSTITUTE OF NANOSCIENCE AND NANOTECHNOLOGY   (Greece)

^b UNIVERSITY OF ATHENS   (Greece)

^c ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^d SCUOLA UNIVERSITARIA SUPERIORE IUSS PAVIA   (Italy)

Author keywords

Activity sensors; Artificial Neural Network (ANN) model; Exposure; Location

Indexed keywords

NEURAL NETWORKS;

ADVANCED MODELING TECHNIQUES; ARTIFICIAL NEURAL NETWORK MODELS; ENVIRONMENTAL STRESSORS; EXPOSURE; EXPOSURE ASSESSMENT METHODOLOGY; MOBILE TECHNOLOGY; MOVEMENT TRAJECTORIES; REASONABLE ACCURACY;

LOCATION;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; COST ANALYSIS; DATA SET; GPS; LOCATION-ALLOCATION MODEL; MOBILE PHONE; SENSOR;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BODY TEMPERATURE MONITORING; CITY; CONTROLLED STUDY; COST; DATA ANALYSIS; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL TEMPERATURE; GLOBAL POSITIONING SYSTEM; GREECE; HUMAN; MOBILE APPLICATION; PHYSICAL ACTIVITY; PILOT STUDY; PREDICTION; TEMPERATURE; VELOCITY; DEVICES; ENVIRONMENTAL MONITORING; EVALUATION STUDY; EXERCISE; FEMALE; GEOGRAPHIC INFORMATION SYSTEM; PROCEDURES; SMARTPHONE;

ATHENS [ATTICA]; ATTICA; CENTRAL MACEDONIA; GREECE; THESSALONIKI [CENTRAL MACEDONIA]; THESSALONIKI [THESSALONIKI (DPR)];

ENVIRONMENTAL MONITORING; EXERCISE; FEMALE; GEOGRAPHIC INFORMATION SYSTEMS; GREECE; HUMANS; MOBILE APPLICATIONS; NEURAL NETWORKS (COMPUTER); PILOT PROJECTS; SMARTPHONE;

EID: 85042210179     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-018-6537-2     Document Type: Article

References (33)

1. Bassett, D. R. (2012). Device-based monitoring in physical activity and public health research. Physiological Measurement, 33, 1769–1783.
2. Breen, M., Long, T., Schultz, B., Crooks, J., Breen, M., et al. (2014). GPS-based microenvironment tracker (MicroTrac) model to estimate time–location of individuals for air pollution exposure assessments: Model evaluation in central North Carolina. Journal of Exposure Science and Environmental Epidemiology, 24(4), 412–420.
3. Briggs, D. (2005). The role of GIS: coping with space (and time) in air pollution exposure assessment. Journal of Toxicology and Environmental Health Part A, 68, 1243–1261.
4. Darwish, A., & Hassanien, A. E. (2011). Wearable and implantable wireless sensor network solutions for healthcare monitoring. Sensors, 11, 5561–5595.
5. Dennis, J.E., & Schnabel, R.B. (1983). Numerical methods for unconstrained optimization and nonlinear equations, Prentice-Hall, Inc.: Englewood Cliffs: NJ.
6. Dueker, D., Taher, M., Wilson, J., & McConnell, R. (2014). Evaluating children’s location using a personal GPS logging instrument: limitations and lessons learned. Journal of Exposure Science and Environmental Epidemiology, 24, 244–252.
7. Foresee, F.D. & Hagan, M.T. (1997). Gauss-Newton approximation to Bayesian regularization. In Proceedings of the IJCNN ‘97 (pp. 1930–1935). Piscataway, NJ.
8. Guo, F., Li, Y., Kankanhalli, M.S., Brown, M.S. (2013). An evaluation of wearable activity monitoring devices, in: Proceedings of the 1st ACM International Workshop on Personal Data Meets Distributed Multimedia, PDM ‘13 (pp. 31–34). ACM, New York.
9. Gusmer, R. J., Bosch, T. A., Watkins, A. N., Ostrem, J. D., & Dengel, D. R. (2014). Comparison of FitBit® ultra to ActiGraph™ GT1M for assessment of physical activity in young adults during treadmill walking. Open Sports Medicine Journal, 8, 11–15.
10. Hagan, M. T., & Menhaj, M. (1995). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5, 989–993.
11. Jantunen, M. J., Hänninen, O., Katsouyanni, K., Knöppel, H., Kuenzli, N., Lebret, E., Maroni, M., Saarela, K., Srám, R., & Zmirou, D. (1998). Air pollution exposure in European cities: the ‘EXPOLIS’ study. Journal of Exposure Analysis and Environmental Epidemiology, 8, 495–518.
12. Karakitsios, S. P., Papaloukas, C. L., Kassomenos, P. A., & Pilidis, G. A. (2006). Assessment and prediction of benzene concentrations in a street canyon using artificial neural networks and deterministic models: their response to “what if” scenarios. Ecological Modelling, 193, 253–270.
13. Karakitsios, S., Asikainen, A., Garden, C., Semple, S., Brouwere, K. D., Galea, K. S., Sánchez-Jiménez, A., Gotti, A., Jantunen, M., & Sarigiannis, D. (2014). Integrated exposure for risk assessment in indoor environments based on a review of concentration data on airborne chemical pollutants in domestic environments in Europe. Indoor and Built Environment, 24, 1110–1146.
14. Kaufman, A., Williams, R. Hunt, S., French, R., Wilson, H., Hagler, G., Vasu Kilaru, Fine, P., Katz, S., Robarge, G., Renwick, D. (2015). Highlights from the Air Sensors 2014 Workshop. Em: air and waste management associations magazine for environmental managers. Air & Waste Management Association, Pittsburgh, PA, 34–39.
15. Kleipeis, N. E. (1999). An introduction to the indirect exposure assessment approach: modeling human exposure using microenvironmental measurements and the recent National Human Activity Pattern Survey. Environmental Health Perspectives, 107, 365–374.
16. Mammen, G., Gardiner, S., Senthinathan, A., McClemont, L., Stone, M., & Faulkner, G. (2012). Is this bit fit? Measuring the quality of the Fitbit step-counter. Health and Fitness Journal of Canada, 5, 30–39.
17. Moller, M. F. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6, 525–533.
18. de Nazelle, A., Seto, E., Donaire-Gonzalez, D., Mendez, M., Matamala, M., Nieuwenhuijsen, M., & Jerrett, M. (2013). Improving estimates of air pollution exposure through ubiquitous sensing technologies. Environmental Pollution, 176, 92–99.
19. Nethery, E., Mallach, G., Rainham, D., Goldberg, M., & Wheeler. (2014). Using global positioning system (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: an automated method. Environmental Ηealth, 13, 33.
20. Noah, J., Spierer, D. K., Gu, J., & Bronner, S. (2013). Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. Journal of Medical Engineering and Technology, 37, 456–462.
21. Phillips, M., Hall, T., Esmen, N., Lynch, R., & Johnson, H. (2011). Use of global positioning system technology to track subject’s location during environmental exposure sampling. Journal of Exposure Analysis and Environmental Epidemiology, 11, 207–215.
22. Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. San Francisco: Proc. IEEE ICNN.
23. Sarigiannis, D. A., Karakitsios, S. P., Gotti, A., Papaloukas, C. L., Kassomenos, P. A., & Pilidis, G. A. (2009). Bayesian algorithm implementation in a real time exposure assessment model on benzene with calculation of associated cancer risks. Sensors, 9, 731–755.
24. Sarigiannis, D. A., Karakitsios, S. P., Antonakopoulou, M. P., & Gotti, A. (2012). Exposure analysis of accidental release of mercury from compact fluorescent lamps (CFLs). Science of the Total Environment, 435–436, 306–315.
25. Schweizer, C., Edwards, R. D., Bayer-Oglesby, L., Gauderman, W. J., Ilacqua, V., & Briggs, D. (2005). The role of GIS: coping with space (and time) in air pollution exposure assessment. Journal of Toxicology and Environmental Health Part A, 68, 1243–1261.
26. Steinle, S., Reis, S., & Sabel, C. E. (2012). Quantifying human exposure to air pollution—moving from static monitoring to spatio-temporally resolved personal exposure assessment. Science of the Total Environment, 443C(null), 184–193.
27. de Vries, S. I., Bakker, I., Hopman-Rock, M., Hirasing, R. A., & van Mechelen, W. (2006). Clinimetric review of motion sensors in children and adolescents. Journal of Clinical Epidemiology, 59, 670–680.
28. Wild, C. P. (2005). Complementing the genome with an ‘exposome’: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiology, Biomarkers and Prevention, 14, 1847–1850.
29. Wild, C. P. (2009). Environmental exposure measurement in cancer epidemiology. Mutagenesis, 24, 117–125.
30. Wild, C. P. (2011). Future research perspectives on environment and health: the requirement for a more expansive concept of translational cancer research. Environmental Health, 10(Suppl 1), S15.
31. Wild, C. P. (2012). The exposome: from concept to utility. International Journal of Epidemiology, 41, 24–32.
32. Wu, J., Jiang, C., Houston, D., Baker, D., & Delfino, R. (2011). Automated time activity classification based on global positioning system (GPS) tracking data. Environmental Health, 10, 101.
33. Yang, C.-C., & Hsu, Y.-L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors, 10, 7772–7788.