A novel approach for discovering human behavior patterns using unsupervised methods

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• 作者：Dr. Ju Wang (1) (2)
  J眉rgen Bauer (3)
  Marcus Becker (4)
  Petra Bente (5)
  Lena Dasenbrock (3)
  Katharina Elbers (6)
  Andreas Hein (7)
  Martin Kohlmann (1)
  Gerald Kolb (3)
  Christopher Lammel-Polchau (4)
  Michael Marschollek (4)
  Markus Meis (8)
  Hartmut Remmers (9)
  Hubertus Meyer zu Schwabedissen (5)
  Mareike Schulze (4)
  Enno-Edzard Steen (7)
  Reinhold Haux (1)
  Klaus-Hendrik Wolf (1)
  
• 关键词：Health assessment ; Ageing ; Sensor ; enhanced living environment ; Pervasive healthcare ; Geriatrics ; Gesundheitliches Assessment ; Altern ; Sensorgest眉tztes Lebensumfeld ; Pervasive Computing in der Gesundheitsversorgung ; Geriatrie
• 刊名：Zeitschrift f篓鹿r Gerontologie und Geriatrie
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：47
• 期：8
• 页码：648-660
• 全文大小：1,542 KB
• 参考文献：1. Department of Economic and Social Affairs and Population Division (2010) World population ageing 2009. Report. Dec, United Nations, New York
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  15. Thielke SM, Mattek NC, Hayes TL, Dodge HH, Quiones AR, Austin D, Petersen J, Kaye JA (2014) Associations between observed in-home behaviors and self-reported low mood in community-dwelling older adults. J Am Geriatr Soc 62(4):685鈥?89 CrossRef
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  23. R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
  
• 作者单位：Dr. Ju Wang (1) (2)
  J眉rgen Bauer (3)
  Marcus Becker (4)
  Petra Bente (5)
  Lena Dasenbrock (3)
  Katharina Elbers (6)
  Andreas Hein (7)
  Martin Kohlmann (1)
  Gerald Kolb (3)
  Christopher Lammel-Polchau (4)
  Michael Marschollek (4)
  Markus Meis (8)
  Hartmut Remmers (9)
  Hubertus Meyer zu Schwabedissen (5)
  Mareike Schulze (4)
  Enno-Edzard Steen (7)
  Reinhold Haux (1)
  Klaus-Hendrik Wolf (1)
  
  1. Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig 鈥?Institute of Technology and Hannover Medical School, Braunschweig, Germany
  2. Institute of Medical Information & Library, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
  3. Klinikum Oldenburg, Geriatrisches Zentrum, Oldenburg, Germany
  4. Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig 鈥?Institute of Technology and Hannover Medical School, Hannover, Germany
  5. St盲tisches Klinikum Braunschweig, Klinikum f眉r Rheuma- und Alterserkrankungen, Braunschweig, Germany
  6. Fachbereich Geriatrie, St. Bonifatius Hospital Lingen, Lingen, Germany
  7. OFFIS-Institut f眉r Informationstechnologie, Oldenburg, Germany
  8. H枚rzentrum Oldenburg, Oldenburg, Germany
  9. Fachgebiet Pflegewissenschaft, Universit盲t Osnabr眉ck, Osnabr眉ck, Germany
  
• ISSN：1435-1269

文摘

Background As is well known, elderly people gradually lose the ability of self-care. The decline can be reflected in changes in their daily life behavior. A solution to assess their health status is to design sensor-enhanced living environments to observe their behavior, in which unobtrusive sensors are usually used. With respect to information extraction from the dataset collected by means of these kinds of sensors, unsupervised methods have to be relied on for practical application. Under the assumption that human lifestyle is associated with health status, this study intends to propose a novel approach to discover behavior patterns using unsupervised methods. Methods To evaluate the feasibility of this approach it was applied to datasets collected in the GAL-NATARS study. The study is part of the Lower Saxony research network Design of Environments for Aging (GAL) and conducted in subjects鈥?home environments. The subjects recruited in GAL-NATARS study are older people (age鈥夆墺鈥?0 years), who are discharged from hospital to live alone again at their homes after treatment of a femoral fracture. Results The change of lifestyle regularity is measured. By analyzing the correlation between the extracted information and medical assessment results of four subjects, two of them exhibited impressive association and the other two showed less association. Conclusions The approach may provide complementary information for health assessment; however, the dominant relationship between the change of behavior patterns and the health status has to be shown and datasets from more subjects must be collected in future studies. Limitations Merely environmental data were used and no wearable sensor for activity detection or vital parameter measurement is taken into account. Therefore, this cannot comprehensively reflect reality.