Machine Learning
PSFQ: a reliable transport protocol for wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Dynamic Power Management in Wireless Sensor Networks
IEEE Design & Test
Application-specific protocol architectures for wireless networks
Application-specific protocol architectures for wireless networks
Modeling and allocation of connections in next-generation wireless networks
Modeling and allocation of connections in next-generation wireless networks
Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
An implementation of hierarchical signal processing on wireless sensor in TinyOS environment
Proceedings of the 43rd annual Southeast regional conference - Volume 2
Error-resistant RFID-assisted wireless sensor networks for cardiac telehealthcare
Wireless Communications & Mobile Computing
IEEE Transactions on Information Technology in Biomedicine
IEEE Communications Magazine
IEEE Transactions on Information Technology in Biomedicine
A 36μW heartbeat-detection processor for a wireless sensor node
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Implantable medical device communication security: pattern vs. signal encryption
HealthSec'11 Proceedings of the 2nd USENIX conference on Health security and privacy
Wireless telemedicine and m-health: technologies, applications and research issues
International Journal of Sensor Networks
Locating and monitoring emergency responder using a wearable device
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Large scale simulation for human evacuation and rescue
Computers & Mathematics with Applications
A comparison of mobile patient monitoring systems
HIS'13 Proceedings of the second international conference on Health Information Science
Wireless Personal Communications: An International Journal
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Many elder patients have multiple health conditions such as heart attacks (of various kinds), brain problems (such as seizure, mental disorder, etc.), high blood pressure, etc. Monitoring those conditions needs different types of sensors for analog signal data acquisition, such as electrocardiogram (ECG) for heart beats, electroencephalogram (EEG) for brain signals, and electromyogram (EMG) for muscles motions. To reduce mobile-health (m-health) cost, the above sensors should be made in tiny size, low memory, and long-term battery operations. We have designed a series of medical sensors with wireless networking capabilities. In this paper, we report our work in three aspects: (1) networked embedded system design, (2) network congestion reduction, and (3) network loss compensation. First, for networked embedded system design, we have designed an integrated wireless sensor network hardware/ software platform for multi-condition patient monitoring. Such a system integrates ECG/EEG/other sensors with Radio Frequency Identification (RFID) into a Radio Frequency (RF) board through a programmable interface chip, called PSoc. Second, for network congestion reduction, the interface chip can use compressive signal processing to extract bio-signal feature parameters and only transmit those parameters. This provides an alternative approach to sensor network congestion reduction that aims to alleviate "hot spot" issues. Third, for network loss compensation, we have designed wireless loss recovery schemes for different situations as follows. (1) If original sensor data streams are transmitted, network congestion will be a big concern due to the heavy traffic. A receiver-only loss prediction will be a good solution. (2) If the signal parameters are transmitted, the transmission loss mandates a 100% recovery rate. We have comprehensively compared the performance of those schemes. The proposed mechanisms for m-health system have potentially significant impacts on today's elder nursing home management and other mobile patient monitoring applications.