Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Dealing with sensor displacement in motion-based onbody activity recognition systems
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Body-coupled communication for body sensor networks
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Shake Well Before Use: Intuitive and Secure Pairing of Mobile Devices
IEEE Transactions on Mobile Computing
Activity Recognition from Accelerometer Data on a Mobile Phone
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Activity-aware ECG-based patient authentication for remote health monitoring
Proceedings of the 2009 international conference on Multimodal interfaces
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Where am i: recognizing on-body positions of wearable sensors
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
An amulet for trustworthy wearable mHealth
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
Who wears me? bioimpedance as a passive biometric
HealthSec'12 Proceedings of the 3rd USENIX conference on Health Security and Privacy
Body area network security: robust key establishment using human body channel
HealthSec'12 Proceedings of the 3rd USENIX conference on Health Security and Privacy
Privacy in mobile technology for personal healthcare
ACM Computing Surveys (CSUR)
Proceedings of the sixth ACM conference on Security and privacy in wireless and mobile networks
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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%.