Personal Health System architecture for stress monitoring and support to clinical decisions Gennaro Tartarisco a,⇑ , Giovanni Baldus a , Daniele Corda a , Rossella Raso a , Antonino Arnao c , Marcello Ferro b , Andrea Gaggioli d , Giovanni Pioggia a a National Research Council of Italy (CNR), Institute of Clinical Physiology (IFC), via G. Moruzzi 1, 56124 Pisa, Italy b National Research Council of Italy (CNR), Institute of Computational Linguistic ‘‘Antonio Zampolli’’ (ILC), via G. Moruzzi 1, 56124 Pisa, Italy c Faculty of Statistical Science, University of Messina, viale Italia, 137 Messina, Italy d ATN-P Lab, Istituto Auxologico Italiano, Milan, Italy article info Article history: Received 12 February 2011 Received in revised form 17 November 2011 Accepted 18 November 2011 Available online 25 November 2011 Keywords: Pervasive healthcare architecture Stress detection Clinical decision support system Autonomic sympathovagal balance Autoregressive model abstract Developments in computational techniques including clinical decision support systems, information pro- cessing, wireless communication and data mining hold new premises in Personal Health Systems. Perva- sive 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, per- sonalized and cost efficient pervasive architecture for the evaluation of the stress state of individual sub- jects 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 mod- eling 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. Ó 2011 Elsevier B.V. All rights reserved. 1. Introduction The medical knowledge is frequently updated and re-evaluated comprising new risk factors identification, new drugs and diagnos- tic tests, new evidences from clinical studies [1]. The challenges faced today are to incorporate the most recent and evidence-based knowledge into Personal Health Systems [2,3] and to transform col- lected information into valuable knowledge and intelligence to sup- port the decision making process [4,5]. Several expert systems tailored to specific diseases are nowadays available in clinical research [6–11], often covering the topics addressed by European priorities [12]. Technology can play a key role to gain the continuity of care and a person-centric model, focusing on a knowledge-based approach integrating past and current data of each patient together with statistical evidences. In currently applied care practices, the emergence of clinical symptoms allows a disease to be discovered. Only then, a diagnosis is obtained and a treatment is provided. Cur- rently, different healthcare practice models are used [12–14]. In some models, the Hospitals is the core of the care and any level of technology available at the patient site may help in providing infor- mation useful for both monitoring, early diagnosis and preventive treatments. In other models dedicated call centers or point of care act as an intermediary between hospital/heath care professional and patients. Many of the solutions available today on the market follow the above-mentioned model and call center services or point of care are used by the patients just as a complement to the hospi- tal-centerd healthcare services [12–15]. In the more advanced Per- sonal Health Systems [16–20] model focused on the empowerment, the ownership of the care service is fully taken by the individual. This model is suitable for any of the stages of an individual’s care cycle, providing prevention, early diagnosis services and personal- ized chronic disease management. Under this model, the technolog- ical innovations can help each person to self engage and manage his/her own health status, minimizing any interaction with other health care actors. Solutions fully led by the patients are the overwhelming majority of those developed by research efforts 0140-3664/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.comcom.2011.11.015 ⇑ Corresponding author. Tel.: +39 050 3152703. E-mail addresses: gennaro.tartarisco@ifc.cnr.it (G. Tartarisco), giovanni.baldu- s@ifc.cnr.it (G. Baldus), daniele.corda@ifc.cnr.it (D. Corda), marcello.ferro@ilc.cnr.it (M. Ferro), andrea.gaggioli@auxologico.it (A. Gaggioli), giovanni.pioggia@ifc.cnr.it (G. Pioggia). Computer Communications 35 (2012) 1296–1305 Contents lists available at SciVerse ScienceDirect Computer Communications journal homepage: www.elsevier.com/locate/comcom