TY - JOUR
T1 - Personal Health System architecture for stress monitoring and support to clinical decisions
AU - Tartarisco, Gennaro
AU - Baldus, Giovanni
AU - Corda, Daniele
AU - Raso, Rossella
AU - Arnao, Antonino
AU - Ferro, Marcello
AU - Gaggioli, Andrea
AU - Pioggia, Giovanni
PY - 2012/6/15
Y1 - 2012/6/15
N2 - Developments in computational techniques including clinical decision support systems, information processing, wireless communication and data mining hold new premises in Personal Health Systems. Pervasive Healthcare system architecture finds today an effective application and represents in perspective a real technological breakthrough promoting a paradigm shift from diagnosis and treatment of patients based on symptoms to diagnosis and treatment based on risk assessment. Such architectures must be able to collect and manage a large quantity of data supporting the physicians in their decision process through a continuous pervasive remote monitoring model aimed to enhance the understanding of the dynamic disease evolution and personal risk. In this work an automatic simple, compact, wireless, personalized and cost efficient pervasive architecture for the evaluation of the stress state of individual subjects suitable for prolonged stress monitoring during normal activity is described. A novel integrated processing approach based on an autoregressive model, artificial neural networks and fuzzy logic modeling allows stress conditions to be automatically identified with a mobile setting analysing features of the electrocardiographic signals and human motion. The performances of the reported architecture were assessed in terms of classification of stress conditions.
AB - Developments in computational techniques including clinical decision support systems, information processing, wireless communication and data mining hold new premises in Personal Health Systems. Pervasive Healthcare system architecture finds today an effective application and represents in perspective a real technological breakthrough promoting a paradigm shift from diagnosis and treatment of patients based on symptoms to diagnosis and treatment based on risk assessment. Such architectures must be able to collect and manage a large quantity of data supporting the physicians in their decision process through a continuous pervasive remote monitoring model aimed to enhance the understanding of the dynamic disease evolution and personal risk. In this work an automatic simple, compact, wireless, personalized and cost efficient pervasive architecture for the evaluation of the stress state of individual subjects suitable for prolonged stress monitoring during normal activity is described. A novel integrated processing approach based on an autoregressive model, artificial neural networks and fuzzy logic modeling allows stress conditions to be automatically identified with a mobile setting analysing features of the electrocardiographic signals and human motion. The performances of the reported architecture were assessed in terms of classification of stress conditions.
KW - Autonomic sympathovagal balance
KW - Autoregressive model
KW - Clinical decision support system
KW - Pervasive healthcare architecture
KW - Stress detection
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UR - http://www.scopus.com/inward/citedby.url?scp=84861999702&partnerID=8YFLogxK
U2 - 10.1016/j.comcom.2011.11.015
DO - 10.1016/j.comcom.2011.11.015
M3 - Article
AN - SCOPUS:84861999702
VL - 35
SP - 1296
EP - 1305
JO - Computer Communications
JF - Computer Communications
SN - 0140-3664
IS - 11
ER -