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
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In an open mobile health (mHealth) sensing system, users will be able to seamlessly pair sensors with their cellphone and expect the system to just work. This ubiquity of sensors, however, creates the potential for users to accidentally wear sensors that are not paired with their own cellphone. Our method probabilistically detects this situation by finding correlations between embedded accelerometers in the cellphone and sensor. We evaluate our method over a dataset of seven individuals with sensors in various positions on their body and experimentally show that our method is capable of achieving an accuracy of 85%.