Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
The E-CARE Project - Removing the Wires
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
The nesC language: A holistic approach to networked embedded systems
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
MIThril 2003: Applications and Architecture
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Habitat monitoring with sensor networks
Communications of the ACM - Wireless sensor networks
Sensor Networks for Emergency Response: Challenges and Opportunities
IEEE Pervasive Computing
IEEE Pervasive Computing
A Hybrid HMM/Kalman Filter for Tracking Hip Angle in Gait Cycle
IEICE - Transactions on Information and Systems
Vineyard Computing: Sensor Networks in Agricultural Production
IEEE Pervasive Computing
Managing care through the air [remote health monitoring]
IEEE Spectrum
Grammar-based, posture- and context-cognitive detection for falls with different activity levels
Proceedings of the 2nd Conference on Wireless Health
Modelling correlations for body sensor network information
Proceedings of the 7th International Conference on Body Area Networks
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
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This article introduces MobiSense, a novel mobile health monitoring system for ambulatory patients. MobiSense resides in a mobile device, communicates with a set of body sensor devices attached to the wearer, and processes data from these sensors. MobiSense is able to detect body postures such as lying, sitting, and standing, and walking speed, by utilizing our rule-based heuristic activity classification scheme based on the extended Kalman (EK) Filtering algorithm. Furthermore, the proposed system is capable of controlling each of the sensor devices, and performing resource reconfiguration and management schemes (sensor sleep/wake-up mode). The architecture of MobiSense is highlighted and discussed in depth. The system has been implemented, and its prototype is showcased. We have also carried out rigorous performance measurements of the system including real-time and query latency as well as the power consumption of the sensor nodes. The accuracy of our activity classifier scheme has been evaluated by involving several human subjects, and we found promising results.