TY - GEN
T1 - Pervasive real-time biomedical monitoring system
AU - Cheriyan, Ajay Mathews
AU - Jarvi, Albert
AU - Kalbarczyk, Zbigniew
AU - Iyer, Ravishankar K.
AU - Watkin, Kenneth Lloyd
PY - 2009
Y1 - 2009
N2 - With the tremendous advancements in low cost, power efficient hardware and the recent interest in biomedical embedded systems, numerous traditional biomedical systems can be replaced with smaller embedded systems that do real-time analysis to provide bio-feedback to the users. This paper presents a prototype of an embedded system which is capable of real-time data collection, using analog and digital sensors and processing, to compute physiological variables and metrics. These metrics in turn can be used to determine information about the user's general well being. The sensors provide motion, brain wave activity (EEG) and blood oxygenation (SpO2) information. The system presented automatically computes the application specific metrics and indicates the results of the detection scheme to the user and to a monitoring base station. The metrics being used have been validated using raw data from patients suffering epileptic seizures and from past research. The paper also deals with application scenarios for such systems and architecture for an FPGA based implementation is discussed.
AB - With the tremendous advancements in low cost, power efficient hardware and the recent interest in biomedical embedded systems, numerous traditional biomedical systems can be replaced with smaller embedded systems that do real-time analysis to provide bio-feedback to the users. This paper presents a prototype of an embedded system which is capable of real-time data collection, using analog and digital sensors and processing, to compute physiological variables and metrics. These metrics in turn can be used to determine information about the user's general well being. The sensors provide motion, brain wave activity (EEG) and blood oxygenation (SpO2) information. The system presented automatically computes the application specific metrics and indicates the results of the detection scheme to the user and to a monitoring base station. The metrics being used have been validated using raw data from patients suffering epileptic seizures and from past research. The paper also deals with application scenarios for such systems and architecture for an FPGA based implementation is discussed.
KW - Configurable hardware
KW - EEG
KW - Embedded systems
KW - Health monitoring
KW - SpO
UR - http://www.scopus.com/inward/record.url?scp=77950844359&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77950844359&partnerID=8YFLogxK
U2 - 10.1109/ICBPE.2009.5384101
DO - 10.1109/ICBPE.2009.5384101
M3 - Conference contribution
AN - SCOPUS:77950844359
SN - 9781424447640
T3 - 2nd International Conference on Biomedical and Pharmaceutical Engineering, ICBPE 2009 - Conference Proceedings
BT - 2nd International Conference on Biomedical and Pharmaceutical Engineering, ICBPE 2009 - Conference Proceedings
T2 - 2nd International Conference on Biomedical and Pharmaceutical Engineering, ICBPE 2009
Y2 - 2 December 2009 through 4 December 2009
ER -