Abstract
As personal health sensors become ubiquitous, we also expect them to become interoperable. That is, instead of closed, end-to-end personal health sensing systems, we envision standardized sensors wirelessly communicating their data to a device many people already carry today, the cellphone. In an open personal health sensing system, users will be able to seamlessly pair off-the-shelf sensors with their cellphone and expect the system to just work. However, this ubiquity of sensors creates the potential for users to accidentally wear sensors that are not necessarily paired with their own cellphone. A husband, for example, might mistakenly wear a heart-rate sensor that is actually paired with his wife’s cellphone. As long as the heart-rate sensor is within communication range, the wife’s cellphone will be receiving heart-rate data about her husband, data that is incorrectly entered into her own health record.
We provide a method to probabilistically detect this situation. Because accelerometers are relatively cheap and require little power, we imagine that the cellphone and each sensor will have a companion accelerometer embedded with the sensor itself. We extract standard features from these companion accelerometers, and use a pair-wise statistic – coherence, a measurement of how well two signals are related in the frequency domain – to determine how well features correlate for different locations on the body. We then use these feature coherences to train a classifier to recognize whether a pair of sensors – or a sensor and a cellphone – are on the same body. We evaluate our method over a dataset of several individuals walking around with sensors in various positions on their body and experimentally show that our method is capable of achieving an accuracies over 80%.
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References
Apex Fitness. BodyBugg (October 2010), http://www.bodybugg.com/
Bao, L., Intille, S.S.: Activity Recognition from User-Annotated Acceleration Data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)
Barth, A.T., Hanson, M.A., Harry, J., Powell, C., Unluer, D., Wilson, S.G., Lach, J.: Body-coupled communication for body sensor networks. In: Proceedings of the ICST 3rd International Conference on Body Area Networks, BodyNets 2008 (2008)
Brezmes, T., Gorricho, J.-L., Cotrina, J.: Activity Recognition from Accelerometer Data on a Mobile Phone. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 796–799. Springer, Heidelberg (2009)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), software http://www.csie.ntu.edu.tw/~cjlin/libsvm
Cornelius, C., Kotz, D.: On Usable Authentication for Wireless Body Area Networks. In: Proceedings of the First USENIX Workshop on Health Security and Privacy (HealthSec) (2010)
Fitbit, Inc. Fitbit (October 2010), http://www.fitbit.com/
Freescale Semiconductor. Freescale Xtrinsic accelerometers optimize resolution and battery life in consumer devices (September 2010), press release http://www.media.freescale.com/phoenix.zhtml?c=196520&p=irol-newsArticle&ID=1470583
Kunze, K.S., Lukowicz, P.: Dealing with sensor displacement in motion-based onbody activity recognition systems. In: Proceedings of the Tenth International Conference on Ubiquitous Computing (UbiComp), pp. 20–29 (2008)
Kunze, K.S., Lukowicz, P., Junker, H., Tröster, G.: Where am I: Recognizing On-body Positions of Wearable Sensors. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 264–275. Springer, Heidelberg (2005)
Lester, J., Hannaford, B., Borriello, G.: “Are You with Me?” - Using Accelerometers to Determine If Two Devices Are Carried by the Same Person. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 33–50. Springer, Heidelberg (2004)
Maurer, U., Smailagic, A., Siewiorek, D.P., Deisher, M.: Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions. In: Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks (BSN), pp. 113–116 (2006)
Mayrhofer, R., Gellersen, H.: Shake Well Before Use: Intuitive and Secure Pairing of Mobile Devices. IEEE Transactions on Mobile Computing 8(6), 792–806 (2009)
Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity Recognition from Accelerometer Data. In: Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI), pp. 1541–1546 (2005)
SparkFun Electronics. WiTilt v2.5 (October 2010), Data sheet http://www.sparkfun.com/datasheets/Sensors/WiTilt_V2_5.pdf
Sriram, J.C., Shin, M., Choudhury, T., Kotz, D.: Activity-aware ECG-based patient authentication for remote health monitoring. In: Proceedings of the Eleventh International Conference on Multimodal Interfaces (ICMI), pp. 297–304 (2009)
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Cornelius, C., Kotz, D. (2011). Recognizing Whether Sensors Are on the Same Body. In: Lyons, K., Hightower, J., Huang, E.M. (eds) Pervasive Computing. Pervasive 2011. Lecture Notes in Computer Science, vol 6696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21726-5_21
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DOI: https://doi.org/10.1007/978-3-642-21726-5_21
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