BibTeX for a paper by David Kotz at Dartmouth College.
For more information about this paper, visit this web page:
https://kotz.cs.dartmouth.edu/research/cornelius-same-body/index.html

@InProceedings{cornelius:same-body,
  author =        {Cory Cornelius and David Kotz},
  title =         {{Recognizing whether sensors are on the same body}},
  booktitle =     {{Proceedings of the International Conference on Pervasive Computing (Pervasive)}},
  series =        {Lecture Notes in Computer Science},
  year =          2011,
  month =         {June},
  volume =        6696,
  pages =         {332--349},
  publisher =     {Springer},
  copyright =     {Springer-Verlag},
  DOI =           {10.1007/978-3-642-21726-5_21},
  URL =           {https://kotz.cs.dartmouth.edu/research/cornelius-same-body/index.html},
  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 \emph{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. \par  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\%.},
}