TY - GEN
T1 - A segmentation technique based on standard deviation in body sensor networks
AU - Guenterberg, Eric
AU - Ghasemzadeh, Hassan
AU - Jafari, Roozbeh
AU - Bajcsy, Ruzena
PY - 2007
Y1 - 2007
N2 - Pervasive health monitoring utilizing wearable wireless sensor nodes can greatly enhance the quality of care individuals receive. Such systems, while in terms of signal processing mostly depend on pattern recognition schemes, must operate independently of human interaction for extended periods. The lack of a general-purpose computationally inexpensive algorithm capable of segmenting sensor readings into discrete actions and non-actions has hindered the development of these systems. We examine a segmentation scheme based on standard deviation metric. We provide experimental verification of the method.
AB - Pervasive health monitoring utilizing wearable wireless sensor nodes can greatly enhance the quality of care individuals receive. Such systems, while in terms of signal processing mostly depend on pattern recognition schemes, must operate independently of human interaction for extended periods. The lack of a general-purpose computationally inexpensive algorithm capable of segmenting sensor readings into discrete actions and non-actions has hindered the development of these systems. We examine a segmentation scheme based on standard deviation metric. We provide experimental verification of the method.
UR - http://www.scopus.com/inward/record.url?scp=48949118114&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=48949118114&partnerID=8YFLogxK
U2 - 10.1109/EMBSW.2007.4454174
DO - 10.1109/EMBSW.2007.4454174
M3 - Conference contribution
AN - SCOPUS:48949118114
SN - 9781424416264
T3 - 2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS
SP - 63
EP - 66
BT - 2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS
T2 - 2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS
Y2 - 11 November 2007 through 12 November 2007
ER -