Abstract
The availability of geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of geolocation data required to accurately recognise out-of-home activities. We collected geolocation data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each data collection day. For finding the minimum sampling rate, we derived datasets at lower sampling rates by down sampling the original data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based geolocation sensing.
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12 February 2020
Unfortunately the first author’s name was spelled incorrectly. In the contribution it read “Yan Zheng” but it should have read “Yan Zeng”.
References
Interim Ericsson Mobility Report (2018). https://www.ericsson.com/assets/local/mobility-report/documents/2018/emr-interim-feb-2018.pdf
Stanley, K., Yoo, E.-H., Paul, T., Bell, S.: How many days are enough?: capturing routine human mobility. Int. J. Geogr. Inf. Sci. 32, 1485–1504 (2018). https://doi.org/10.1080/13658816.2018.1434888
Raine, J., Withill, A., Morecock, E.: Literature Review of the Costs and benefits of Traveller Information Projects (2014)
Guo, B., Fujimura, R., Zhang, D., Imai, M.: Design-in-play: improving the variability of indoor pervasive games. Multimed. Tools Appl. 59, 259–277 (2012). https://doi.org/10.1007/s11042-010-0711-z
Royer, D., Deuker, A., Rannenberg, K.: Mobility and identity. In: Rannenberg, K., Royer, D., Deuker, A. (eds.) The Future of Identity in the Information Society, pp. 195–242. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01820-6_5
Zheng, Y., Zhang, L., Ma, Z., Xie, X., Ma, W.-Y.: Recommending friends and locations based on individual location history. ACM Trans. Web. 5, 5:1–5:44 (2011). https://doi.org/10.1145/1921591.1921596
Data Description: GeoLife User Guide 1.2. Microsoft Research Asia. 2, 31–34 (2011)
Daubal, M., Fajinmi, O., Jangaard, L.: Safe step: a real-time GPS tracking and analysis system for criminal activities using ankle bracelets. In: Proceedings 21st ACM SIGSPATIAL International Conference Advances in Geographic Information System, pp. 502–505 (2013). https://doi.org/10.1145/2525314.2525316
Wahl, H.W., et al.: Interplay of cognitive and motivational resources for out-of-home behavior in a sample of cognitively heterogeneous older adults: findings of the SenTra project. J. Gerontol. Ser. B Psychol. Sci. Soc. Sci. 68, 691–702 (2013). https://doi.org/10.1093/geronb/gbs106
Difrancesco, S., et al.: Out-of-home activity recognition from GPS data in schizophrenic patients. In: Proceedings of IEEE Symposium Computer Medical System, pp. 324–328, August 2016. https://doi.org/10.1109/cbms.2016.54
Torous, J., Kiang, M.V., Lorme, J., Onnela, J.-P.: New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment. Heal. 3, e16 (2016)
Bhattacharya, T., Kulik, L., Bailey, J.: Automatically recognizing places of interest from unreliable GPS data using spatio-temporal density estimation and line intersections. Pervasive Mob. Comput. 19, 86–107 (2015). https://doi.org/10.1016/j.pmcj.2014.08.003
Krenn, P.J., Titze, S., Oja, P., Jones, A., Ogilvie, D.: Use of global positioning systems to study physical activity and the environment: a systematic review. Am. J. Prev. Med. 41, 508–515 (2011) https://doi.org/10.1016/j.amepre.2011.06.046
Marmasse, N., Schmandt, C.: Location-aware information delivery with ComMotion. In: Thomas, P., Gellersen, H.-W. (eds.) HUC 2000. LNCS, vol. 1927, pp. 157–171. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-39959-3_12
Glasgow, M.L., et al.: Using smartphones to collect time–activity data for long-term personal-level air pollution exposure assessment. J. Expo. Sci. Environ. Epidemiol. 26, 356 (2016)
Boonstra, W.T., Nicholas, J., Wong, J.J.Q., Shaw, F., Townsend, S., Christensen, H.: Using mobile phone sensor technology for mental health research: integrated analysis to identify hidden challenges and potential solutions. J. Med. Internet Res. 20, e10131 (2018). https://doi.org/10.2196/10131
Google: Google Maps API (n.d.). http://www.webcitation.org/6ubEADyQl
OpenStreetMap API (n.d.). https://wiki.openstreetmap.org/wiki/API
Foursquare (n.d.). https://developer.foursquare.com/. Accessed 4 June 2018
GPSLogger for Android (n.d.). https://gpslogger.app/. Accessed 4 June, 2018
Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Pers. Ubiquit. Comput. 7, 275–286 (2003). https://doi.org/10.1007/s00779-003-0240-0
Fehér, M., Forstner, B.: Identifying and utilizing routines of human movement. In: Second Eastern European Regional Conference on the Engineering of Computer based System Identifying (2011). https://doi.org/10.1109/ecbs-eerc.2011.28
Zheng, K., Zheng, Y., Xie, X., Zhou, X.: Reducing uncertainty of low-sampling-rate trajectories. In: Proceedings of International Conference Data Engineering, pp. 1144–1155 (2012). https://doi.org/10.1109/icde.2012.42
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This project was funded by the Engineering and Physical Sciences Research Council (grant EP/P010148/1; The Wearable Clinic: Connecting Health, Self and Care).
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Zeng, Y., Fraccaro, P., Peek, N. (2019). The Minimum Sampling Rate and Sampling Duration When Applying Geolocation Data Technology to Human Activity Monitoring. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_29
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