Recognizing whether sensors are on the same body
[cornelius:same-body]Cory Cornelius and David Kotz. Recognizing whether sensors are on the same body. Proceedings of the International Conference on Pervasive Computing (Pervasive), volume 6696 in Lecture Notes in Computer Science, pages 332–349. Springer-Verlag, June 2011. doi:10.1007/978-3-642-21726-5_21. ©Copyright Springer-Verlag.
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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Keywords: [iot] [mhealth] [security] [sensors] [wearable]
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