Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms
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BACKGROUND: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. METHODS: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. RESULTS: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. CONCLUSION: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel.
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Research has shown that an accelerometer installed on smartphones is accurate for tracking step count and that GPS signals can be used to estimate activity levels.
“For instance, research has shown that an accelerometer installed on smartphones is accurate for tracking step count [9] and that GPS signals can be used to estimate activity levels [87].”
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Cites
Real-time human pose recognition in parts from single depth images
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A. W., Finocchio, M., Blake, A., Cook, M., & Moore, R. (2013) · Communications of the ACM · doi:10.1145/2398356.2398381 · Cited by 2,050 · see citing works
Using mobile phones to determine transportation modes
Reddy, S., Mun, M., Burke, J., Estrin, D. L., Hansen, M. H., & Srivastava, M. B. (2010) · ACM Transactions on Sensor Networks · doi:10.1145/1689239.1689243 · Cited by 852 · see citing works
Learning and inferring transportation routines
Liao, L., Patterson, D. J., Fox, D., & Kautz, H. (2007) · Artificial Intelligence · doi:10.1016/j.artint.2007.01.006 · Cited by 798 · see citing works
Walking, Cycling, and Obesity Rates in Europe, North America, and Australia
Bassett, D. R., Pucher, J. R., Buehler, R., Thompson, D. L., & Crouter, S. E. (2008) · Journal of Physical Activity and Health · doi:10.1123/jpah.5.6.795 · Cited by 631 · see citing works
Learning transportation mode from raw gps data for geographic applications on the web
Zheng, Y., Liu, L., Wang, L., & Xie, X. (2008) · Proceedings of the 17th international conference on World Wide Web · doi:10.1145/1367497.1367532 · Cited by 616 · see citing works
Kerr, J., Rosenberg, D. E., Sallis, J. F., Saelens, B. E., Frank, L. D., & Conway, T. L. (2006) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000210208.63565.73 · Cited by 548 · see citing works
Self-citationInferring High-Level Behavior from Low-Level Sensors
Patterson, D. J., Liao, L., Fox, D., & Kautz, H. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39653-6_6 · Cited by 521 · see citing works
Bohte, W., & Maat, K. (2009) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2008.11.004 · Cited by 509 · see citing works
Tudor‐Locke, C., Ainsworth, B. E., & Popkin, B. M. (2001) · Sports Medicine · doi:10.2165/00007256-200131050-00001 · Cited by 366 · see citing works
Staudenmayer, J., Pober, D., Crouter, S., Bassett, D., & Freedson, P. (2009) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00465.2009 · Cited by 361 · see citing works
Self-citationHuman Activity Recognition from Accelerometer Data Using a Wearable Device
Casale, P., Pujol, O., & Radeva, P. I. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21257-4_36 · Cited by 341 · see citing works
SURVEY METHODS FOR TRANSPORT PLANNING
Richardson, A. J., Ampt, E. S., & Meyburg, A. H. (1995) · Medical Entomology and Zoology · Cited by 337 · see citing works
Processing Raw Data from Global Positioning Systems without Additional Information
Rieser-Schüssler, N., & Axhausen, K. W. (2009) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2105-04 · Cited by 327 · see citing works
Assessing the accuracy of the Sydney Household Travel Survey with GPS
Stopher, P., FitzGerald, C., & Xu, M. (2007) · Transportation · doi:10.1007/s11116-007-9126-8 · Cited by 292 · see citing works
Using Global Positioning Systems in Health Research
Kerr, J., Duncan, S., & Schipperijn, J. (2011) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2011.07.017 · Cited by 242 · see citing works
Self-citationDevelopment of Novel Techniques to Classify Physical Activity Mode Using Accelerometers
POBER, D. M., STAUDENMAYER, J. W., Raphael, C. S., & Freedson, P. S. (2006) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000227542.43669.45 · Cited by 210 · see citing works
Self-citationSahlqvist, S., Song, Y., & Ogilvie, D. (2012) · Preventive Medicine · doi:10.1016/j.ypmed.2012.06.028 · Cited by 198 · see citing works
Fall Detection and Activity Recognition with Machine Learning
Luštrek, M., & Kaluža, B. (2009) · Informatica · Cited by 190 · see citing works
(1998) · Cited by 181 · see citing works
A Trip Reconstruction Tool for GPS-based Personal Travel Surveys
Chung, E.-H., & Shalaby, A. S. (2005) · Transportation Planning and Technology · doi:10.1080/03081060500322599 · Cited by 163 · see citing works
Du, J., & Aultman-Hall, L. (2006) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2006.05.001 · Cited by 144 · see citing works
Kelly, P., Doherty, A., Berry, E., Hodges, S. E., Batterham, A. M., & Foster, C. E. (2011) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/1479-5868-8-44 · Cited by 130 · see citing works
Prediction of Activity Mode with Global Positioning System and Accelerometer Data
Troped, P. J., OLIVEIRA, M. S., Matthews, C. E., Cromley, E. K., Melly, S. J., & Craig, B. Α. (2008) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e318164c407 · Cited by 123 · see citing works
Ambulation: A Tool for Monitoring Mobility Patterns over Time Using Mobile Phones
Ryder, J., Longstaff, B., Reddy, S., & Estrin, D. (2009) · doi:10.1109/cse.2009.312 · Cited by 111 · see citing works
Processing GPS data from travel surveys
Stopher, P., Jiang, Q., & FitzGerald, C. (2005) · Transport Research Forum · Cited by 76 · see citing works
Deducing mode and purpose from GPS data
Stopher, P., Clifford, E., Zhang, J., & FitzGerald, C. (2008) · The Sydney eScholarship Repository (The University of Sydney) · Cited by 75 · see citing works
Real-Time Transportation Mode Detection via Tracking Global Positioning System Mobile Devices
Byon, Y.-J., Abdulhai, B., & Shalaby, A. S. (2009) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450903287781 · Cited by 70 · see citing works
Public Health Perspectives on Household Travel Surveys
Merom, D., van der Ploeg, H. P., Corpuz, G., & Bauman, A. E. (2010) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2010.04.007 · Cited by 68 · see citing works
Rajan Transform and its Uses in Pattern Recognition
Mandalapu, E. N., & Rajan, E. G. (2009) · Informatica · Cited by 55 · see citing works
Smartphone Interruptibility Using Density-Weighted Uncertainty Sampling with Reinforcement Learning
Fisher, R., & Simmons, R. (2011) · 2011 10th International Conference on Machine Learning and Applications and Workshops · doi:10.1109/icmla.2011.128 · Cited by 42 · cited 1× · see citing works
“Although recent studies have attempted to circumvent these issues, solutions appear largely experimental or prototypical (30, 31).”
BackgroundExamining commute routes: applications of GIS and GPS technology
Badland, H. M., Duncan, M. J., Oliver, M., Duncan, S., & Mavoa, S. (2010) · Environmental Health and Preventive Medicine · doi:10.1007/s12199-010-0138-1 · Cited by 41 · see citing works
Movement detection for power-efficient smartphone WLAN localization
Shafer, I., & Chang, M. L. (2010) · ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) · doi:10.1145/1868521.1868536 · Cited by 41 · see citing works
Identifying travel mode from GPS trajectories through fuzzy pattern recognition
Xu, C., Ji, M., Chen, W., & Zhang, Z. (2010) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2010.5569105 · Cited by 38 · see citing works
PALMS: A Modern Coevolution of Community and Computing Using Policy Driven Development
Demchak, B., Kerr, J., Raab, F., Patrick, K., & Krüger, I. H. (2012) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2012.464 · Cited by 26 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Machine learning in healthcare
Habehh, H., & Gohel, S. (2021) · Current Genomics · doi:10.2174/1389202922666210705124359 · Cited by 544 · see citing works
Zhang, J., Oh, Y. J., Lange, P., Yu, Z., & Fukuoka, Y. (2020) · Journal of Medical Internet Research · doi:10.2196/22845 · Cited by 363 · see citing works
Ellis, K., Kerr, J., Godbole, S. V., Lanckriet, G. R. G., Wing, D., & Marshall, S. J. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/11/2191 · Cited by 344 · see citing works
Self-citationDeep learning methods in transportation domain: a review
Nguyên, H., Kieu, M., Wen, T., & Cai, C. (2018) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2018.0064 · Cited by 287 · see citing works
The Pace of Technologic Change
Patrick, K., Hekler, E. B., Estrin, D., Mohr, D. C., Riper, H., Crane, D., Godino, J., & Riley, W. T. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.05.001 · Cited by 169 · see citing works
A Framework for Using GPS Data in Physical Activity and Sedentary Behavior Studies
Jankowska, M. M., Schipperijn, J., & Kerr, J. (2014) · Exercise and Sport Sciences Reviews · doi:10.1249/jes.0000000000000035 · Cited by 192 · see citing works
Self-citationHirsch, J. A., Winters, M., Clarke, P. J., & McKay, H. A. (2014) · International Journal of Health Geographics · doi:10.1186/1476-072x-13-51 · Cited by 185 · see citing works
Mobile Sensing in Environmental Health and Neighborhood Research
Chaix, B. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-013731 · Cited by 141 · see citing works
Validity of PALMS GPS Scoring of Active and Passive Travel Compared with SenseCam
Carlson, J., Jankowska, M. M., Meseck, K., Godbole, S. V., Natarajan, L., Raab, F., Demchak, B., Patrick, K., & Kerr, J. (2014) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000446 · Cited by 139 · see citing works
Self-citationEmerging Technology in Promoting Physical Activity and Health: Challenges and Opportunities
Gao, Z., & Lee, J. E. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8111830 · Cited by 105 · see citing works
GPS-Based Exposure to Greenness and Walkability and Accelerometry-Based Physical Activity
James, P., Hart, J. E., Hipp, J. A., Mitchell, J. A., Kerr, J., Hurvitz, P. M., Glanz, K., & Laden, F. (2017) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-16-0925 · Cited by 104 · see citing works
Self-citationBellettiere, J., Carlson, J., Rosenberg, D. E., Singhania, A., Natarajan, L., Berardi, V., LaCroix, A. Z., Sears, D. D., Moran, K., Crist, K., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0136161 · Cited by 92 · see citing works
Self-citationAI-Based Sensor Information Fusion for Supporting Deep Supervised Learning
Leung, C. K.-S., Braun, P., & Cuzzocrea, A. (2019) · Sensors · doi:10.3390/s19061345 · Cited by 79 · see citing works
Objective Assessment of Physical Activity
Kerr, J., Patterson, R. E., Ellis, K., Godbole, S. V., JOHNSON, E., Lanckriet, G. R. G., & STAUDENMAYER, J. W. (2015) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000841 · Cited by 78 · see citing works
Self-citationJames, P., Jankowska, M. M., Marx, C. M., Hart, J. E., Berrigan, D. A., Kerr, J., Hurvitz, P. M., Hipp, J. A., & Laden, F. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.06.006 · Cited by 78 · see citing works
Self-citationO'Brien, M. K., Shawen, N., Mummidisetty, C. K., Kaur, S., Bo, X., Poellabauer, C., Kording, K., & Jayaraman, A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7385 · Cited by 78 · see citing works
Lee, K., & Kwan, M. (2017) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2017.09.012 · Cited by 78 · see citing works
Travel Mode Detection with Varying Smartphone Data Collection Frequencies
Shafique, M. A., & Hato, E. (2016) · Sensors · doi:10.3390/s16050716 · Cited by 77 · see citing works
Urban Analytics of Big Transportation Data for Supporting Smart Cities
Leung, C. K.-S., Braun, P., Hoi, C. S. H., Souza, J., & Cuzzocrea, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27520-4_3 · Cited by 77 · see citing works
Modular and Personalized Smart Health Application Design in a Smart City Environment
Venkatesh, J., Akşanlı, B., Chan, C. S., Akyürek, A. S., & Simunic, T. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2712558 · Cited by 77 · see citing works
Loveday, A., Sherar, L. B., Sanders, J. P., Sanderson, P. W., & Esliger, D. W. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4761 · Cited by 70 · see citing works
Costa, S., Ogilvie, D., Dalton, A. M., Westgate, K. L., Brage, S., & Panter, J. (2015) · Preventive Medicine · doi:10.1016/j.ypmed.2015.09.022 · Cited by 64 · see citing works
Martin, B. D., Addona, V., Wolfson, J. A., Adomavičius, G., & Fan, Y. (2017) · Sensors · doi:10.3390/s17092058 · Cited by 64 · see citing works
Jeantet, L., Planas-Bielsa, V., Benhamou, S., Geiger, S., Martin, J., Siegwalt, F., Lelong, P., Gresser, J., Etienne, D., Hiélard, G., et al. (2020) · Royal Society Open Science · doi:10.1098/rsos.200139 · Cited by 61 · see citing works
Quantification of Free-Living Community Mobility in Healthy Older Adults Using Wearable Sensors
Boissy, P., Blamoutier, M., Brière, S., & Duval, C. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00216 · Cited by 54 · see citing works
Making pervasive sensing possible: Effective travel mode sensing based on smartphones
Zhou, X., Yu, W., & SULLIVAN, W. C. (2016) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2016.03.001 · Cited by 51 · see citing works
Self-reported versus GPS-derived indicators of daily mobility in a sample of healthy older adults
Fillekes, M. P., Röcke, C., Katana, M., & Weibel, R. (2018) · Social Science & Medicine · doi:10.1016/j.socscimed.2018.11.010 · Cited by 50 · see citing works
Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods
Kerr, J., Carlson, J., Godbole, S. V., Cadmus‐Bertram, L., Bellettiere, J., & Hartman, S. J. (2018) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001578 · Cited by 47 · see citing works
Self-citationJeantet, L., Dell’Amico, F., Forin-Wiart, M.-A., Coutant, M., Bonola, M., Etienne, D., Gresser, J., Régis, S., Lecerf, N., Lefebvre, F., et al. (2018) · Journal of Experimental Biology · doi:10.1242/jeb.177378 · Cited by 44 · see citing works
Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes
Brondeel, R., Pannier, B., & Chaix, B. (2015) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000704 · Cited by 43 · see citing works
Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection
Allahbakhshi, H., Conrow, L., Naimi, B., & Weibel, R. (2020) · Sensors · doi:10.3390/s20030588 · Cited by 41 · see citing works
Technologies to Measure and Modify Physical Activity and Eating Environments
King, A. C., Glanz, K., & Patrick, K. (2015) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2014.10.005 · Cited by 41 · see citing works
Wullems, J. A., Verschueren, S., Degens, H., Morse, C. I., & Onambélé-Pearson, G. L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188215 · Cited by 40 · see citing works
Greenwood-Hickman, M. A., Nakandala, S., Jankowska, M. M., Rosenberg, D. E., Tuz‐Zahra, F., Bellettiere, J., Carlson, J., Hibbing, P. R., Zou, J., LaCroix, A. Z., et al. (2021) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002705 · Cited by 38 · see citing works
Assessing the role of geographic context in transportation mode detection from GPS data
Roy, A., Fuller, D., NELSON, T. A., & Kedron, P. (2022) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2022.103330 · Cited by 35 · see citing works
Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price
Li, D., Moghaddam, M. R., Monjezi, M., Armaghani, D. J., & Mehrdanesh, A. (2020) · Applied Sciences · doi:10.3390/app10072364 · Cited by 32 · see citing works
Burkhard, O., Becker, H., Weibel, R., & Axhausen, K. W. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.01.021 · Cited by 31 · see citing works
Lee, K., & Kwan, M. (2018) · Transactions in GIS · doi:10.1111/tgis.12485 · Cited by 30 · see citing works
Jain, P., Bellettiere, J., Glass, N., LaMonte, M. J., Di, C., Wild, R. A., Evenson, K. R., & LaCroix, A. Z. (2020) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaa227 · Cited by 29 · see citing works
Diagnosis of transportation modes on mobile phone using logistic regression classification
Ballı, S., & Sağbaş, E. A. (2018) · IET Software · doi:10.1049/iet-sen.2017.0035 · Cited by 29 · see citing works
Effective Classification of Ground Transportation Modes for Urban Data Mining in Smart Cities
Leung, C. K.-S., Braun, P., & Pazdor, A. G. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-98539-8_7 · Cited by 28 · see citing works
Graham, D. J., & Hipp, J. A. (2014) · Frontiers in Public Health · doi:10.3389/fpubh.2014.00066 · Cited by 28 · see citing works
Kang, M.-G., Moudon, A. V., Hurvitz, P. M., & Saelens, B. E. (2018) · International Journal of Health Geographics · doi:10.1186/s12942-018-0161-9 · Cited by 27 · see citing works
An open-source tool to identify active travel from hip-worn accelerometer, GPS and GIS data
Procter, D., Page, A. S., Cooper, A. R., Nightingale, C. M., Ram, B., Rudnicka, A. R., Whincup, P. H., Clary, C. M., Lewis, D. J., Cummins, S., et al. (2018) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-018-0724-y · Cited by 24 · see citing works
Transportation Mode Detection by Embedded Sensors Based on Ensemble Learning
Alotaibi, B. (2020) · IEEE Access · doi:10.1109/access.2020.3014901 · Cited by 23 · see citing works
Jankowska, M. M., Sears, D. D., Natarajan, L., Martinez, E., Anderson, C. A. M., Sallis, J. F., Matthews, S. A., Crist, K., Dillon, L. W., Johnson, E. M., et al. (2019) · BMC Public Health · doi:10.1186/s12889-019-6501-2 · Cited by 23 · see citing works
Self-citationDevelopment of Novel Big Data Analytics Framework for Smart Clothing
Ahsan, M., Teay, S. H., & Albarbar, A. (2020) · IEEE Access · doi:10.1109/access.2020.3015152 · Cited by 21 · see citing works
Accelerometry predicts prey-capture rates in the deep-diving king penguin Aptenodytes patagonicus
Brisson‐Curadeau, É., Handrich, Y., Elliott, K. H., & Bost, C. (2021) · Marine Biology · doi:10.1007/s00227-021-03968-y · Cited by 21 · see citing works
Adolescent school travel: Is online mapping a practical alternative to GPS-assessed travel routes?
Stewart, T. I., Schipperijn, J., Snizek, B., & Duncan, S. (2016) · Journal of Transport & Health · doi:10.1016/j.jth.2016.10.001 · Cited by 20 · see citing works
Refining Time-Activity Classification of Human Subjects Using the Global Positioning System
Hu, M., Li, W., Li, L., Houston, D., & Wu, J. Y. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148875 · Cited by 19 · see citing works
Nakandala, S., Jankowska, M. M., Tuz‐Zahra, F., Bellettiere, J., Carlson, J., LaCroix, A. Z., Hartman, S. J., Rosenberg, D. E., Zou, J., Kumar, A., et al. (2021) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2020-0016 · Cited by 19 · see citing works
Data mining techniques to analyze the factors influencing active commuting to school
Ferri‐García, R., Fernández-Luna, J. M., Rodríguez‐López, C., & Chillón, P. (2019) · International Journal of Sustainable Transportation · doi:10.1080/15568318.2018.1547465 · Cited by 19 · see citing works
Cho, S., Aiello, E. M., Özaslan, B., Riddell, M. C., Calhoun, P. M., Gal, R. L., & Doyle, F. J. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231153896 · Cited by 18 · see citing works
Actigraphy features for predicting mobility disability in older adults
Kheirkhahan, M., Tudor-Locke, C. E., Axtell, R. S., Buman, M. P., Fielding, R. A., Glynn, N. W., Guralnik, J. M., King, A. C., White, D. K., Miller, M. E., et al. (2016) · Physiological Measurement · doi:10.1088/0967-3334/37/10/1813 · Cited by 18 · see citing works
Pryss, R. C., John, D. T., Reichert, M., Hoppenstedt, B., Schmid, L., Schlee, W., Spiliopoulou, M., Schobel, J., Kraft, R., Schickler, M., et al. (2019) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2019.00061 · Cited by 16 · see citing works
Chaix, B., Bista, S., Wang, L., Benmarhnia, T., Dureau, C., & Duncan, D. T. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-048706 · Cited by 17 · see citing works
Phenome-Wide Association Studies: Embracing Complexity for Discovery
Pendergrass, S. A., Verma, A., Okula, A., Hall, M. A., Crawford, D. C., & Ritchie, M. D. (2015) · Human Heredity · doi:10.1159/000381851 · Cited by 19 · see citing works
Alberico, C. O., Schipperijn, J., & Reis, R. S. (2016) · Preventive Medicine · doi:10.1016/j.ypmed.2016.12.026 · Cited by 17 · see citing works
Online Classification of Unstructured Free-Living Exercise Sessions in People with Type 1 Diabetes
Fushimi, E., Aiello, E. M., Cho, S., Riddell, M. C., Gal, R. L., Martin, C. K., Patton, S. R., Rickels, M. R., & Doyle, F. J. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0528 · Cited by 16 · see citing works
Ojeda, L., Adamczyk, P. G., Rebula, J. R., NYQUIST, L. V., Strasburg, D. M., & Alexander, N. B. (2018) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2018.12.008 · Cited by 16 · see citing works
Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors
Quinn, C. W., Anderson, G. B., Magzamen, S. L., Henry, C. S., & Volckens, J. (2020) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/s41370-019-0198-2 · Cited by 15 · see citing works
Limb, E. S., Procter, D., Cooper, A. R., Page, A. S., Nightingale, C. M., Ram, B., Shankar, A., Clary, C. M., Lewis, D. J., Cummins, S., et al. (2020) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-020-0916-0 · Cited by 15 · see citing works
Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning
Mardini, M., Bai, C., Wanigatunga, A. A., Saldana, S. J., Casanova, R., & Manini, T. M. (2021) · Sensors · doi:10.3390/s21103352 · Cited by 13 · see citing works
A Modular Approach to Context-Aware IoT Applications
Venkatesh, J., Chan, C. S., Akyürek, A. S., & Simunic, T. (2016) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1109/iotdi.2015.13 · Cited by 14 · see citing works
Delclòs‐Alió, X., Vich, G., & Miralles‐Guasch, C. (2020) · Landscape Research · doi:10.1080/01426397.2019.1702937 · Cited by 14 · see citing works
Transportation mode detection by using smartphone sensors and machine learning
Sağbaş, E. A., & Ballı, S. (2016) · Pamukkale University Journal of Engineering Sciences · doi:10.5505/pajes.2015.63308 · Cited by 13 · see citing works
A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity
Zhuparris, A., Maleki, G., van Londen, L., Koopmans, I. W., Aalten, V., Yocarini, I. E., Exadaktylos, V., van Hemert, A. M., Cohen, A. F., Gal, P., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-46075-2 · Cited by 14 · see citing works
Giri, S., Brondeel, R., Aarbaoui, T. E., & Chaix, B. (2022) · International Journal of Health Geographics · doi:10.1186/s12942-022-00319-y · Cited by 12 · see citing works
Possible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology
Aden, D., Zaheer, S., & Khan, S. Q. (2024) · Revista Española de Patología · doi:10.1016/j.patol.2024.04.003 · Cited by 10 · see citing works
Ryu, R. H., Larsen, B. A., LaCroix, A. Z., Nguyen, S., Posis, A., Schumacher, B. T., Danhauer, S., Tindle, H., & Bellettiere, J. (2023) · Journal of Happiness Studies · doi:10.1007/s10902-022-00612-4 · Cited by 10 · see citing works
Mohan, D., Bashingwa, J. J. H., Dane, P., Chamberlain, S., Tiffin, N., & LeFevre, A. E. (2019) · JMIR Research Protocols · doi:10.2196/11456 · Cited by 11 · see citing works
Liu, W., Chambers, T. J., Clevenger, K. A., Pfeiffer, K. A., Rzotkiewicz, Z., Park, H., & Pearson, A. L. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299943 · Cited by 9 · see citing works
Accelerometer and GPS Analysis of Trail Use and Associations With Physical Activity
Tamura, K., Wilson, J. S., Puett, R. C., Klenosky, D. B., Harper, W. A., & Troped, P. J. (2018) · Journal of Physical Activity and Health · doi:10.1123/jpah.2016-0667 · Cited by 9 · see citing works
Real-world and traffic-adjusted physical activity levels of micromobility modes in Barcelona
Bretones, A., Miralles‐Guasch, C., & Marquet, O. (2023) · Journal of Transport & Health · doi:10.1016/j.jth.2023.101732 · Cited by 8 · see citing works
Roy, A., Fuller, D., Stanley, K. G., & NELSON, T. A. (2020) · Findings · doi:10.32866/001c.14520 · Cited by 8 · see citing works
Jara-Diaz, S. R., & Rosales-Salas, J. (2020) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2020.03.025 · Cited by 8 · see citing works
Owen, C. G., Limb, E. S., Nightingale, C. M., Rudnicka, A. R., Ram, B., Shankar, A., Cummins, S., Lewis, D. J., Clary, C. M., Cooper, A. R., et al. (2020) · Public Health Research · doi:10.3310/phr08120 · Cited by 7 · see citing works
Nguyen, L. M. P., & Mertens, L. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18052647 · Cited by 7 · see citing works
Crist, K., Benmarhnia, T., Zamora, S. M., Yang, J., Sears, D. D., Natarajan, L., Dillon, L. W., Sallis, J. F., & Jankowska, M. M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18083909 · Cited by 8 · see citing works
Cognitive infocommunication for monitoring and improving well-being of people
Vágner, A. (2017) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2017.8268224 · Cited by 6 · see citing works
Wismadi, M. T., Frøyen, Y. K., & Rasheed, A. (2025) · Sustainability · doi:10.3390/su17031276 · Cited by 5 · see citing works
Advances in artificial neural networks as a disease prediction tool
Taylor, M. A., Cl, B., Schoen, M. W., & Hoque, S. (2021) · Journal of Cancer Research & Therapy · doi:10.14312/2052-4994.2021-1 · Cited by 5 · see citing works
Forum, I. T. (2017) · International transport forum policy papers · doi:10.1787/996cc49e-en · Cited by 5 · see citing works
Automated detection of sleep-boundary times using wrist-worn accelerometryPreprint
O’Donnell, J., Hollowell, S., Salimi‐Khorshidi, G., Velardo, C., Sexton, C. E., Rahimi, K., Johansen‐Berg, H., Tarassenko, L., & Doherty, A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/225516 · Cited by 5 · see citing works
Image-based research methods for mapping tourist behaviour: smart photos
Zhan, X., Guo, F. B., & Roberts, E. (2023) · Tourism Recreation Research · doi:10.1080/02508281.2023.2196487 · Cited by 4 · see citing works
Identifying the Sport Activity of GPS Tracks
Ferri, C. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.345 · Cited by 4 · see citing works
Applying machine learning algorithms for deriving personality traits in social network
Araújo, E. F. D. M., Simoski, B., & Klein, M. C. A. (2018) · ACM Symposium on Applied Computing · doi:10.1145/3167132.3167377 · Cited by 4 · see citing works
Pollard, B., McDonald, G., Held, F. P., & Engelen, L. (2021) · Ergonomics · doi:10.1080/00140139.2021.1980115 · Cited by 4 · see citing works
Misslin, R., Charreire, H., Weber, C., Enaux, C., Bastian, T., Simon, C., & Oppert, J. (2015) · Cybergeo · doi:10.4000/cybergeo.26711 · Cited by 4 · see citing works
Naito, D., HONDA, K., Sekiguchi, Y., Izumi, S., & Ebihara, S. (2025) · Sensors · doi:10.3390/s25092689 · Cited by 3 · see citing works
Cameras and crowds in transportation tracking
Hipp, J. A., Manteiga, A. M., Burgess, A. R., Stylianou, A., & Pless, R. B. (2015) · Wireless Health · doi:10.1145/2811780.2811941 · Cited by 3 · see citing works
An Effective Multi-Swarm Algorithm for Optimizing Hyperparameters of DNN
Fang, Z.-Y., Xiao, Z.-W., & Tsai, C. (2020) · International Conference on Intelligent Computing and Its Emerging Applications (ICEA) · doi:10.1145/3440943.3444722 · Cited by 3 · see citing works
Steel, C., Crist, K., Grimes, A. R., Bejarano, C. M., Ortega, A., Hibbing, P. R., Schipperijn, J., & Carlson, J. (2021) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2021-0019 · Cited by 3 · see citing works
Proximity Based Social Networking in Urban Environments: Applications, Architectures and Frameworks
Mocktoolah, A., & Khedo, K. K. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-71288-4_4 · Cited by 3 · see citing works
Lachapelle, U., Carpentier-Laberge, D., Gelb, J., Apparicio, P., & Cloutier, M. (2025) · Transportation research procedia · doi:10.1016/j.trpro.2024.12.200 · Cited by 2 · see citing works
Manzoor, A. F., Mollee, J. S., van Halteren, A., & Klein, M. C. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67074-4_46 · Cited by 2 · see citing works
Ye, Q., Xu, G., Yuan, H., Mi, J., Xie, Y., Li, H., Li, Z., Huang, G., Chen, X., Li, W., et al. (2023) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s445341 · Cited by 2 · see citing works
Prediction and Analysis of Digital Health Records, Geonomics, and Radiology Using Machine Learning
Raj, S., Agarwal, A. P., Tripathi, S., & Gupta, N. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815305128124010005 · Cited by 2 · see citing works
Sasirekha, B., & Gunavathi, C. (2025) · Journal of Applied Security Research · doi:10.1080/19361610.2025.2562401 · Cited by 2 · see citing works
Algorithmic geolocation of harvest in hand‐picked agriculture
Srivastava, N., Maneykowski, P., & Sowers, R. B. (2018) · Natural Resource Modeling · doi:10.1111/nrm.12158 · Cited by 2 · see citing works
- Authors
- Ellis, K., Godbole, S., Marshall, S., Lanckriet, G., Staudenmayer, J., & Kerr, J.
- Affiliations
- University of California San Diego (US)
- University of Massachusetts Amherst (US)
- Corresponding author
- Ellis, Katherine
- Venue
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Published
- 2014-04-22
- Volume
- 2
- Pages
- 36-36
- ISSN
- 2296-2565 (ISSN-L)
- PMC
- PMC4001067
- OpenAlex
- W2134430874
- Cited by
- 123
- References
- 34
- Citations by year
- 2025: 5 · 2024: 8 · 2023: 7 · 2022: 6 · 2021: 12 · 2020: 18 · 2019: 8 · 2018: 18 · 2017: 12 · 2016: 13 · 2015: 10 · 2014: 5
- FWCI
- 11.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Public Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of California, San Diego
- National Cancer Institute (R01CA164993, U01CA130771, U54CA155435)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Urban Transport and Accessibility
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- active travel, GPS data, accelerometer data, cycling, walking, vehicle travel, physical activity measurement, random forest, moving average smoothing
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