A methodological framework for the analysis of highly intensive, multimodal and heterogeneous data in the context of health-enabling technologies and ambient-assisted living

References

• United Nations, Department of Economic and Social Affairs, Population Division. World population prospects: the 2010 revision. Available from: http://esa.un.org/unpd/wpp/index.htm [last accessed 2013 October 27]
   Google Scholar
• Arnrich B, Mayora O, Bardram J, Tröster G. Pervasive healthcare paving the way for a pervasive, user-centered and preventive healthcare model. Methods Inf Med 2010;49:67–73
   PubMed Web of Science ®Google Scholar
• Frieling H, Bleich S, Marschollek M. Psychiatry and informatics – joining forces to improve mental health. Methods Inf Med 2012;51:1–2
   PubMedGoogle Scholar
• Akar SA, Kara S, Bilgiç V. Respiratory variability during different auditory stimulation periods in schizophrenia patients. Methods Inf Med 2012;51:29–38
   PubMedGoogle Scholar
• Yoshino K, Matsuoka K. Personal adaptive method to assess mental tension during daily life using heart rate variability. Methods Inf Med 2012;51:39–44
   PubMedGoogle Scholar
• Matic A, Mehta P, Rehg JM, et al. Monitoring dressing activity failures through RFID and video. Methods Inf Med 2012;51:45–54
   PubMedGoogle Scholar
• Kohlmann M, Gietzelt M, Marschollek M, et al. High-intensity, multimodality and incoherence: grand challenges in the analysis of data for health-enabling technologies. Proceedings of the 14th World Congress on Medical and Health Informatics (MEDINFO); 2013 Aug 20–23; Copenhagen, Denmark. p. 967 (Abstract)
   Google Scholar
• Leiner F, Gaus W, Haux R, et al. Medizinische. Dokumentation. 6th edition. Stuttgart: Schattauer; 2012
   Google Scholar
• ACM Computing Classification System. Available from: http://www.acm.org/about/class/ [last accessed 2013 October 27]
   Google Scholar
• IUPAC Nomenclature of Organic Chemistry, recommandations 1993. Available from: http://www.acdlabs.com/iupac/nomenclature/93/r93_108.htm [last accessed 2013 October 27]
   Google Scholar
• International Commission of Zoological Nomenclature. Available from: http://iczn.org/ [last accessed 2013 October 27]
   Google Scholar
• GenBank. Available from: http://www.ncbi.nlm.nih.gov/genbank/ [last accessed 2013 October 27]
   Google Scholar
• Physionet. Available from: http://www.physionet.org/citations.shtml [last accessed 2013 October 27]
   Google Scholar
• ProFaNE Taxonomy. Available from: http://www.profane.eu.org/documents/Falls_Taxonomy.pdf [last accessed 2013 October 27]
   Google Scholar
• de la Calle G, García-Remesal M, Nkumu-Mbomio N, et al. e-MIR2: a public online inventory of medical informatics resources. BMC Med Inform and Decis Mak 2012;12:82
   Google Scholar
• The Electronic Medical Informatics Repository of Resources (e-MIR2). http://www.gib.fi.upm.es/eMIR2 [last accessed 2013 October 27]
   Google Scholar
• Logan B, Healey J, Philipose M, et al. A long-term evaluation of sensing modalities for activity recognition. Proceedings of UbiComp 2007; 2007Berlin Heidelberg: Springer-Verlag, pp. 483–500
   Google Scholar
• Song B, Wolf KH, Gietzelt M, et al. Decision support for teletraining of COPD patients. Methods Inf Med 2010;49:96–102
   PubMed Web of Science ®Google Scholar
• Schulze M, Song B, Gietzelt M, et al. Supporting rehabilitation training of COPD patients through multivariate sensor-based monitoring and autonomous control using a Bayesian network: prototype and results of a feasibility study. Inform Health Soc Care 2010;35:144–56
   PubMed Web of Science ®Google Scholar
• Haux R, Hein A, Eichelberg M, et al. The Lower Saxony research network design of environments for ageing: towards interdisciplinary research on information and communication technologies in ageing societies. Inform Health Soc Care 2010;35:92–103
   PubMed Web of Science ®Google Scholar
• Lester J, Choudhury T, Borriello G. A practical approach to recognize physical activies. In: Fishkin KP, Schiele B, Nixon P, Quigley A. (eds.). Proceedings of Pervasive 2006, vol. LNCS 3968; 2006, Berlin Heidelberg: Springer-Verlag, pp. 1–16
   Google Scholar
• Wang J, Wolf KH, Gietzelt M, Haux R. The projection algorithm applied for activity pattern (motif) discovery in smart home. Proceedings of the 7th Asia Pacific Association for Medical Informatics conference (APAMI); 2012 Oct 22–25; Beijing, China. Published on CD-ROM
   Google Scholar
• Intille SS, Larson K, Tapia EM, et al. Using a live-in laboratory for ubiquitous computing research. In: Fishkin KP, Schiele B, Nixon P, Quigley A. (eds.). Proceedings of Pervasive 2006, vol. LNCS 3968; 2006, Berlin Heidelberg: Springer-Verlag, pp. 349–65
   Google Scholar
• Hein A, Winkelbach S, Martens B, et al. Monitoring systems for the support of home care. Inform Health Soc Care 2010;35:157–76
   PubMed Web of Science ®Google Scholar
• Wolf KH, Marschollek M, Bott OJ, et al. Sensors for health-related parameters and data fusion approaches. In: Hein A, Thoben W, Appelrath HJ, Jensch P (eds.). European Conference on eHealth 2007, Vol. P-118. GI, Oldenburg; 2007, pp. 155–61
   Google Scholar
• Wilken O, Gietzelt M, Spehr J, Hein A. An approach to the fusion of probabilities of activities for the robust identification of activities of daily living (ADL). In: BMBF (Hrsg.). 4. Deutscher AAL-Kongress. AAL 2011: Tagungsband Ambient-assisted Living; 2011 Jan 24–26; Berlin, Germany
   Google Scholar
• Duda RO, Hart PE, Stork DG. Pattern classification. New York: John Wiley & Sons; 2000
   Google Scholar
• Feiler S, Müller KG, Müller A, et al. Using interaction graphs for analysing the therapy process. Psychother Psychosom 2005;74:93–9
   PubMedGoogle Scholar
• Murthy SK. Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Discov 1998;2:345–89
   Web of Science ®Google Scholar
• Quinlan JR. C4.5: programs for machine learning. San Francisco, CA: Morgan Kaufmann Publishers; 1993
   Google Scholar
• Theodoridis S, Koutroumbas K. Pattern recognition. 4. Auflage. New York, NY: Academic Press; 2008
   Google Scholar
• Wild B, Eichler M, Friederich HC, et al. A graphical vector autoregressive modelling approach to the analysis of electronic diary data. BMC Med Res Methodol 2010;1:10–28
   Google Scholar
• Witten IH, Frank E. Data mining: practical machine learning tools and techniques. 2. Ausgabe. San Francisco, CA: Morgan Kaufmann; 2005
   Google Scholar
• Hall DL, Llinas J. An introduction to multisensor data fusion. Proc IEEE 1997;85:6–23
   Web of Science ®Google Scholar
• Liu Z, Forsyth DS, Komorowski JP, et al. Survey: state of the Art in NDE data fusion techniques. IEEE Trans Instrum Meas 2007;56:2435–51
   Web of Science ®Google Scholar
• Luo RC, Chang CC, Lai CC. Multisensor fusion and integration – theories, applications, and its perspectives. IEEE Sens J 2011;11:3122–38
   Web of Science ®Google Scholar
• Gietzelt M, Wolf KH, Haux R. A nomenclature for the analysis of continuous sensor and other data in the context of health-enabling technologies. Stud Health Technol Inform 2011;169:460–4
   PubMedGoogle Scholar
• Kohlmann M, Gietzelt M, Haux R, et al. A collaboration tool based on SNOCAP-HET. J Med Syst 2014;38:9996--10005
   PubMedGoogle Scholar