@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},
    year = {2011},
    month = {June},
    series = {Lecture Notes in Computer Science},
    volume = {6696},
    pages = {332--349},
    publisher = {Springer-Verlag},
    copyright = {Springer-Verlag},
    doi = {10.1007/978-3-642-21726-5_21},
    url = {http://www.cs.dartmouth.edu/~dfk/papers/cornelius-same-body.pdf},
    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 {\it 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\%.}
}