Smart Health pp 41-69

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8700) | Cite as

Spatial Health Systems

When Humans Move Around
  • Björn Gottfried
  • Hamid Aghajan
  • Kevin Bing-Yung Wong
  • Juan Carlos Augusto
  • Hans Werner Guesgen
  • Thomas Kirste
  • Michael Lawo
Chapter

Abstract

This chapter outlines spatial health systems and discusses issues regarding their technical implementation and employment. This concerns in particular diseases which manifest themselves in the spatiotemporal behaviours of patients, showing patterns that enable conclusions about their underlying well-being. While a general overview is given, as an example the case of patients suffering from Alzheimer’s disease is examined more carefully in order to treat different aspects detailed enough. Especially, wearable and ambient technologies, activity recognition techniques as well as ethical aspects are discussed. The given literature review ranges from basic methods of Artificial Intelligence research to commercial products which are already available from the industry.

References

  1. 1.
    Abraham Louis Perrelet. http://en.wikipedia.org/wiki/Abraham-Louis_Perrelet. Accessed 14 Jan 2014
  2. 2.
    Animazoo motion capture. http://www.animazoo.com/products/igs-180-range. Accessed 15 Dec 2013
  3. 3.
    Basque country reaches out to the elderly. http://www.cnbc.com/id/101198944. Accessed 15 Dec 2013
  4. 4.
    Chronious. http://www.chronious.eu. Accessed 29 Dec 2013
  5. 5.
    Fitbit. http://www.fitbit.com/. Accessed 15 Dec 2013
  6. 6.
  7. 7.
    Misfit wearables. http://www.misfitwearables.com/. Accessed 15 Dec 2013
  8. 8.
    Nike fuelband. http://www.nike.com/us/en_us/c/nikeplus-fuelband. Accessed 15 Dec 2013
  9. 9.
    Openstage. http://www.organicmotion.com/products/openstage. Accessed 15 Dec 2013
  10. 10.
    Vicon — markers and suits. http://www.vicon.com/System/Markers. Accessed 15 Dec 2013
  11. 11.
    Augusto, J., Nugent, C.: The use of temporal reasoning and management of complex events in smart homes. In: Proceedings of the ECAI 2004, Valencia, Spain, pp. 778–782 (2004)Google Scholar
  12. 12.
    Augusto, J.C., Huch, M., Kameas, A., Maitland, J., McCullagh, P., Roberts, J., Sixsmith, A., Wichert, R.: Handbook on Ambient Assisted Living - Technology for Healthcare Rehabilitation and Well-Being. The AISE Book Series, vol. 11. IOS Press, Amsterdam (2012)Google Scholar
  13. 13.
    Augusto, J.C., Nakashima, H., Aghajan, H.: Ambient intelligence and smart environments: a state of the art. In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds.) Handbook of Ambient Intelligence and Smart Environments, pp. 3–31. Springer, New York (2010)CrossRefGoogle Scholar
  14. 14.
    Aztiria, A., Augusto, J., Izaguirre, A., Cook, D.: Learning accurate temporal relations from user actions in intelligent environments. In: Proceedings of the 3rd Symposium of Ubiquitous Computing and Ambient Intelligence, Salamanca, Spain, pp. 274–283 (2008)Google Scholar
  15. 15.
    Ballemans, J., Kempen, G.I., Zijlstra, G.R.: Orientation and mobility training for partially-sighted older adults using an identification cane: a systematic review. Clin. Rehabil. 25, 880–891 (2011)CrossRefGoogle Scholar
  16. 16.
    Bengalur, M.D.: Human activity recognition using body pose features and support vector machine. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1970–1975 (2013)Google Scholar
  17. 17.
    Bohlken, W., Neumann, B., Hotz, L., Koopmann, P.: Ontology-based realtime activity monitoring using beam search. In: Crowley, J.L., Draper, B.A., Thonnat, M. (eds.) ICVS 2011. LNCS, vol. 6962, pp. 112–121. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  18. 18.
    Callejas Cuervo, M., Olaya, A., Salamanca, R.: Biomechanical motion capture methods focused on tele-physiotherapy. In: Health Care Exchanges (PAHCE), 2013 Pan American, pp. 1–6 (2013)Google Scholar
  19. 19.
    Camicioli, R., Howieson, D., Oken, B., Sexton, G., Kaye, J.: Motor slowing precedes cognitive impairment in the oldest old. Neurology 50(5), 1496–1498 (1998)CrossRefGoogle Scholar
  20. 20.
    Chakraborty, B., Bagdanov, A.D., Gonzàlez, J., Roca, F.X.: Human action recognition using an ensemble of body-part detectors. Expert Syst. 30(2), 101–114 (2013)CrossRefGoogle Scholar
  21. 21.
    Chua, S.-L., Marsland, S., Guesgen, H.: Unsupervised learning of patterns in data streams using compression and edit distance. In: Proceedings of IJCAI, Barcelona, Spain, pp. 1231–1236 (2011)Google Scholar
  22. 22.
    Cook, D.: Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 27(1), 32–38 (2012)CrossRefGoogle Scholar
  23. 23.
    Crews, J.E., Campbell, V.A.: Vision impairment and hearing loss among community-dwelling older Americans: implications for health and functioning. Am. J. Public Health 94(5), 823–829 (2004)CrossRefGoogle Scholar
  24. 24.
    Cummings, J.L.: The neuropsychiatric inventory: assessing psychopathology in dementia patients. Neurology 48(Suppl 6), S10–S16 (1997)CrossRefGoogle Scholar
  25. 25.
    David, R., Rivet, A., Robert, P.H., Mailland, V., Friedman, L., Zeitzer, J.M., Yesavage, J.: Ambulatory actigraphy correlates with apathy in mild Alzheimer’s disease. Dementia 9(4), 509–516 (2010)CrossRefGoogle Scholar
  26. 26.
    DeCarli, C.: Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment. Lancet Neurol. 2, 15–21 (2003)CrossRefGoogle Scholar
  27. 27.
    Dempster, A.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325–339 (1967)CrossRefMATHMathSciNetGoogle Scholar
  28. 28.
    Doukas, C., Maglogiannis, I.: Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components. IEEE Trans. Inf. Technol. Biomed. 15(2), 277–289 (2011)CrossRefGoogle Scholar
  29. 29.
    Downs, S.H., Black, N.: The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J. Epidemiol. Commun. Health 52, 377–384 (1998)CrossRefGoogle Scholar
  30. 30.
    Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, London (1980) MATHGoogle Scholar
  31. 31.
    Duong, T., Bui, H., Phung, D., Venkatesh, S.: Activity recognition and abnormality detection with the switching hidden semi-Markov model. In: Proceedings of CVPR 2005, San Diego, California, pp. 838–845 (2005)Google Scholar
  32. 32.
    Fernandez-Martinez, M., Molano, A., Castro, J., Zarranz, J.J.: Prevalence of neuropsychiatric symptoms in mild cognitive impairment and Alzheimer’s disease, and its relationship with cognitive impairment. Curr. Alzheimer Res. 7(6), 517–526 (2010)CrossRefGoogle Scholar
  33. 33.
    Folstein, M.F., Folstein, S.E., McHugh, P.R.: Mini-mental-state: a practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975)CrossRefGoogle Scholar
  34. 34.
    Foxlin, E.: Pedestrian tracking with shoe-mounted inertial sensors. IEEE Comput. Graph 25(6), 38–46 (2005)CrossRefGoogle Scholar
  35. 35.
    Friedman, J.H.: Regularized discriminant analysis. J. Am. Statist. Assoc. 84(405), 165–175 (1989)CrossRefGoogle Scholar
  36. 36.
    Giuberti, M., Ferrari, G., Contin, L., Cimolin, V., Cau, N., Galli, M., Azzaro, C., Albani, G., Mauro, A.: On the characterization of Leg Agility in patients with Parkinson’s Disease. In: 2013 IEEE International Conference on Body Sensor Networks, pp. 1–6, May 2013Google Scholar
  37. 37.
    Gopalratnam, K., Cook, D.: Active LeZi: an incremental parsing algorithm for sequential prediction. Int. J. Artif. Intell. Tools 14(1–2), 917–930 (2004)CrossRefGoogle Scholar
  38. 38.
    Gottfried, B.: Spatial health systems. In: Bardram, J.E., Chachques, J.C., Varshney, U. (eds.) 1st International Conference on Pervasive Computing Technologies for Healthcare (PCTH 2006), November 29–December 1, Innsbruck, Austria, pp. 7. IEEE Press (2006)Google Scholar
  39. 39.
    Gottfried, B.: Modelling spatiotemporal developments in spatial health systems. In: Olla, P., Tan, J. (eds.) Mobile Health Solutions for Biomedical Applications. IGI Global (Idea Group Publishing), April 2009Google Scholar
  40. 40.
    Gottfried, B.: Locomotion activities in smart environments. In: Nakashima, H., Aghajan, H.K., Augusto, J.C. (eds.) Handbook of Ambient Intelligence and Smart Environments, pp. 89–115. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  41. 41.
    Gottfried, B.: Interpreting motion events of pairs of moving objects. GeoInformatica 15(2), 247–271 (2011)CrossRefGoogle Scholar
  42. 42.
    Gottfried, B., Aghajan, H.: Behaviour Monitoring and Interpretation - Smart Environments. IOS Press, Amsterdam (2009)Google Scholar
  43. 43.
    Gottfried, B., W. Guesgen, H., Hübner, S.: Spatiotemporal reasoning for smart homes. In: Augusto, J.C., Nugent, C.D. (eds.) Designing Smart Homes. LNCS (LNAI), vol. 4008, pp. 16–34. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  44. 44.
    Guesgen, H., Marsland, S.: Spatio-temporal reasoning and context awareness. In: Nakashima, H., Aghajan, H., Augusto, J. (eds.) Handbook of Ambient Intelligence and Smart Environments, pp. 609–634. Springer, New York (2010)CrossRefGoogle Scholar
  45. 45.
    Hausdorff, J.M., Rios, D.A., Edelberg, H.K.: Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch. Phys. Med. Rehabil. 82(8), 1050–1056 (2001)CrossRefGoogle Scholar
  46. 46.
    Hoey, J., Plötz, T., Jackson, D., Monk, A., Pham, C., Olivier, P.: Rapid specification and automated generation of prompting systems to assist people with dementia. Pervasive Mob. Comput. 7(3), 299–318 (2011)CrossRefGoogle Scholar
  47. 47.
    Hoey, J., Poupart, P., Bertoldi, A.V., Craig, T., Boutilier, C., Mihailidis, A.: Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process. Comput. Vision Image Underst. 114(5), 503–519 (2010)CrossRefGoogle Scholar
  48. 48.
    Hoey, J., Yang, X., Quintana, E., Favela, J.: LaCasa: location and context-aware safety assistant. In: 6th International Conference Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 171–174 (2012)Google Scholar
  49. 49.
    Holzinger, A., Scherer, R., Seeber, M., Wagner, J., Müller-Putz, G.: Computational sensemaking on examples of knowledge discovery from neuroscience data: towards enhancing stroke rehabilitation. In: Böhm, C., Khuri, S., Lhotská, L., Renda, M.E. (eds.) ITBAM 2012. LNCS, vol. 7451, pp. 166–168. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  50. 50.
    Holzinger, A., Schwaberger, K., Weitlaner, M.: Ubiquitous computing for hospital applications: Rfid-applications to enable research in real-life environments. In: 29th Annual International Computer Software and Applications Conference (COMPSAC 2005), 25–28 July 2005, Edinburgh, Scotland, UK, pp. 19–20. IEEE Computer Society (2005)Google Scholar
  51. 51.
    Ito, T.: Walking motion analysis using 3D acceleration sensors. In: 2008 Second UKSIM European Symposium on Computer Modeling and Simulation, EMS 2008, pp. 123–128 (2008)Google Scholar
  52. 52.
    Jakkula, V., Cook, D.: Anomaly detection using temporal data mining in a smart home environment. Methods Inf. Med. 47(1), 70–75 (2008)Google Scholar
  53. 53.
    Jakkula, V.R., Cook, D.J.: Detecting anomalous sensor events in smart home data for enhancing the living experience. In: Artificial Intelligence and Smarter Living, volume WS-11-07 of AAAI Workshops. AAAI (2011)Google Scholar
  54. 54.
    Jung, P.-G., Lim, G., Kong, K.: A mobile motion capture system based on inertial sensors and smart shoes. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 692–697 (2013)Google Scholar
  55. 55.
    Kammoun, S., Macé, M.J.-M., Oriola, B., Jouffrais, C.: Towards a geographic information system facilitating navigation of visually impaired users. In: Miesenberger, K., Karshmer, A., Penaz, P., Zagler, W. (eds.) ICCHP 2012, Part II. LNCS, vol. 7383, pp. 521–528. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  56. 56.
    Kearns, W., Algase, D., Moore, D., Ahmed, S.: Ultra wideband radio: a novel method for measuring wandering in persons with dementia. Gerontechnology 7(1), 48–57 (2008)CrossRefGoogle Scholar
  57. 57.
    Kirste, T., Hoffmeyer, A., Koldrack, P., Bauer, A., Schubert, S., Schröder, S., Teipel, S.: Detecting the effect of Alzheimer’s disease on everyday motion behavior. J. Alzheimer’s Dis. 38(1), 121–132 (2014)Google Scholar
  58. 58.
    Kleinberger, T., Becker, M., Ras, E., Holzinger, A., Müller, P.: Ambient intelligence in assisted living: enable elderly people to handle future interfaces. In: Stephanidis, C. (ed.) UAHCI 2007 (Part II). LNCS, vol. 4555, pp. 103–112. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  59. 59.
    Klir, G., Folger, T.: Fuzzy Sets, Uncertainty, and Information. Prentice Hall, Englewood Cliffs (1988) MATHGoogle Scholar
  60. 60.
    Koss, E., Weiner, M., Ernesto, C., Cohen-Mansfield, J., Ferris, S.H., Grundman, M., Schafer, K., Sano, M., Thal, L.J., Thomas, R., Whitehouse, P.J.: Assessing patterns of agitation in Alzheimer’s disease patients with the Cohen-Mansfield Agitation Inventory. The Alzheimer’s disease cooperative study. Alzheimer Dis. Assoc. Disord. 11(Suppl 2), S45–50 (1997)CrossRefGoogle Scholar
  61. 61.
    Kuhlmei, A., Walther, B., Becker, T., Müller, U., Nikolaus, T.: Actigraphic daytime activity is reduced in patients with cognitive impairment and apathy. Eur. Psychiatry 28(2), 806–814 (2011)Google Scholar
  62. 62.
    Landau, R., Auslander, G.K., Werner, S., Shoval, N., Heinik, J.: Who should make the decision on the use of GPS for people with dementia? Aging Ment. Health 15, 78–84 (2011)CrossRefGoogle Scholar
  63. 63.
    Landau, R., Werner, S.: Ethical aspects of using GPS for tracking people with dementia: recommendations for practice. Int. Psychogeriatr. 24, 358–366 (2012)CrossRefGoogle Scholar
  64. 64.
    Landau, R., Werner, S., Auslander, G., Shoval, N., Heinik, J.: Attitudes of family and professional care-givers towards the use of GPS for tracking patients with dementia: an exploratory study. Br. J. Soc. Work 39(4), 670–692 (2009)CrossRefGoogle Scholar
  65. 65.
    Levy, E., Kalis, M., Vo, M., Lindisch, D., Cleary, K.: Feasibility of simultaneous respiratory function monitoring and determination of respiratory-related intrahepatic vessel excursion using the lifeshirt system. In: Lemke, H.U., Inamura, K., Doi, K., Vannier, M.W., Farman, A.G., Reiber, J.H.C. (eds.) Proceedings of the 18th International Congress and Exhibition Computer Assisted Radiology and Surgery, Chicago, USA, June 23–26, vol. 1268, International Congress Series, pp. 764–769. Elsevier (2004)Google Scholar
  66. 66.
    Li, L., Zhang, H., Jia, W., Nie, J., Zhang, W., Sun, M.: Automatic video analysis and motion estimation for physical activity classification. In: Bioengineering Conference, Proceedings of the 2010 IEEE 36th Annual Northeast, pp. 1–2 (2010)Google Scholar
  67. 67.
    Li, Q., Chen, S., Stankovic, J.A.: Multi-modal in-person interaction monitoring using smartphone and on-body sensors. In: 2013 IEEE International Conference on Body Sensor Networks, pp. 1–6, May 2013Google Scholar
  68. 68.
    Lo, G., Suresh, A., Stocco, L., Gonzalez-Valenzuela, S., Leung, V.C.M.: A wireless sensor system for motion analysis of parkinson’s disease patients. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 372–375 (2011)Google Scholar
  69. 69.
    Long, R.G., Boyette, L.W., Griffin-Shirley, N.: Older persons and community travel: the effect of visual impairment. J. Visual Impairment Blindness 90(4), 302–313 (1996)Google Scholar
  70. 70.
    Maki, B.E.: Gait changes in older adults: predictors of falls or indicators of fear. J. Am. Geriatr. Soc. 45(3), 313–320 (1997)Google Scholar
  71. 71.
    McKeever, S., Ye, J., Coyle, L., Bleakley, C., Dobson, S.: Activity recognition using temporal evidence theory. J. Ambient Intell. Smart Environ. 2(3), 253–269 (2010)Google Scholar
  72. 72.
    Miskelly, F.: A novel system of electronic tagging in patients with dementia and wandering. Age Ageing 33, 304–306 (2004)CrossRefGoogle Scholar
  73. 73.
    Nagels, G., Engelborghsand, S., Vloeberghs, E., Van Dam, D., Pickut, B.A., De Deyn, P.P.: Actigraphic measurement of agitated behaviour in dementia. Int. J. Geriatr. Psychiatry. 21(4), 388–393 (2006)CrossRefGoogle Scholar
  74. 74.
    Nguyen, H., Kreinovich, V., Tolbert, D.: On robustness of fuzzy logics. In: Proceedings of the 2nd IEEE International Conference on Fuzzy Systems, San Francisco, California, pp. 543–547 (1993)Google Scholar
  75. 75.
    Nissenbaum, H.: Privacy as contextual integrity. Wash. Law Rev. 79(1), 119–158 (2004)Google Scholar
  76. 76.
    Olsson, A., Engstrm, M., Skovdahl, K., Lampic, C.: My, your and our needs for safety and security: relatives’ reflections on using information and communication tech-nology in dementia care. Scand. J. Caring Sci. 26, 104–112 (2012)CrossRefGoogle Scholar
  77. 77.
    Oswald, F., Wahl, H.-W., Voss, E., Schilling, O., Freytag, T., Auslander, G., Shoval, N., Heinik, J., Landau, R.: The use of tracking technologies for the analysis of outdoor mobility in the face of dementia: first steps into a project and some illustrative findings from Germany. J. Hous. Elderly 24, 55–73 (2010)CrossRefGoogle Scholar
  78. 78.
    Paradiso, R., Loriga, G., Taccini, N.A.: A wearable health care system based on knitted integrated sensors. IEEE Trans. Inf. Technol. Biomed. 9, 337–344 (2005)CrossRefGoogle Scholar
  79. 79.
    Patterson, D.J., Liao, L., Gajos, K., Collier, M., Livic, N., Olson, K., Wang, S., Fox, D., Kautz, H.: Opportunity knocks: a system to provide cognitive assistance with transportation services. In: Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 433–450. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  80. 80.
    Plaza, I., Martín, L., Martin, S., Medrano, C.: Mobile applications in an aging so-ciety: status and trends. J. Syst. Softw. 84, 1977–1988 (2011)CrossRefGoogle Scholar
  81. 81.
    Pogorelc, B., Gams, M.: Diagnosing health problems from gait patterns of elderly. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2238–2241 (2010)Google Scholar
  82. 82.
    Pogorelc, B., Gams, M.: Medically driven data mining application: recognition of health problems from gait patterns of elderly. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 976–980 (2010)Google Scholar
  83. 83.
    Rashidi, P., Cook, D.J.: Mining and monitoring patterns of daily routines for assisted living in real world settings. In: Proceedings of the 1st ACM International Health Informatics Symposium, IHI 2010, pp. 336–345. ACM, New York (2010)Google Scholar
  84. 84.
    Rashidi, P., Cook, D.J., Holder, L.B., Schmitter-Edgecombe, M.: Discovering activities to recognize and track in a smart environment. IEEE Trans. Knowl. Data Eng. 23(4), 527–539 (2011)CrossRefGoogle Scholar
  85. 85.
    Renso, C., Baglioni, M., de Macêdo, J.A.F., Trasarti, R., Wachowicz, M.: How you move reveals who you are: understanding human behavior by analyzing trajectory data. Knowl. Inf. Syst. 37(2), 331–362 (2013)CrossRefGoogle Scholar
  86. 86.
    Rivera-Illingworth, F., Callaghan, V., Hagras, H.: Detection of normal and novel behaviours in ubiquitous domestic environments. Comput. J. 53(2), 142–151 (2010)CrossRefGoogle Scholar
  87. 87.
    Roley, S.S., DeLany, J.V., Barrows, C.J., Brownrigg, S., Honaker, D., Sava, D.I., Talley, V., Voelkerding, K., Amini, D.A., Smith, E., Toto, P., King, S., Lieberman, D., Baum, M.C., Cohen, E.S., Cleveland, P.A., Youngstrom, M.J.: Occupational therapy practice framework: domain & practice. Am. J. Occup. Ther. 62(6), 625–683 (2008). (2nd edn)CrossRefGoogle Scholar
  88. 88.
    Rosso, A.L., Auchincloss, A.H., Michael, Y.L.: The urban built environment and mobility in older adults: a comprehensive review. J. Aging Res. 2011, 10 (2011)CrossRefGoogle Scholar
  89. 89.
    Roy, A., Soni, Y., Dubey, S.: Enhancing effectiveness of motor rehabilitation using kinect motion sensing technology. In: 2013 IEEE Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS), pp. 298–304 (2013)Google Scholar
  90. 90.
    Runge, M., Hunter, G.: Determinants of musculoskeletal frailty and the risk of falls in old age. J. Musculoskelet. Neuronal Interact. 6(2), 167–173 (2006)Google Scholar
  91. 91.
    Salarian, A., Russmann, H., Vingerhoets, F.J.G., Dehollaini, C., Blanc, Y., Burkhard, P., Aminian, K.: Gait assessment in parkinson’s disease: toward an ambulatory system for long-term monitoring. IEEE Trans. Biomed. Eng. 51(8), 1434–1443 (2004)CrossRefGoogle Scholar
  92. 92.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976) MATHGoogle Scholar
  93. 93.
    Shoval, N., Auslander, G., Cohen-Shalom, K., Isaacson, M., Landau, R., Heinik, J.: What can we learn about the mobility of the elderly in the GPS era? J. Transport Geogr. 18, 603–612 (2010)CrossRefGoogle Scholar
  94. 94.
    Stone, E., Skubic, M.: Evaluation of an inexpensive depth camera for in-home gait assessment. J. Ambient Intell. Smart Environ. 3(4), 349–361 (2011)Google Scholar
  95. 95.
    Sundaresan, A., Chellappa, R.: Markerless motion capture using multiple cameras. Comput. Vision Interact. Intell. Environ. 2005, 15–26 (2005)CrossRefGoogle Scholar
  96. 96.
    Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  97. 97.
    Tavenard, R., Salah, A., Pauwels, E.: Searching for temporal patterns in ami sensor data. In: Mühlhäuser, M., Ferscha, A., Aitenbichler, E. (eds.) Constructing Ambient Intelligence. Communications in Computer and Information Science, vol. 11, pp. 53–62. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  98. 98.
    Tay, F.E.H., Nyan, M.N., Koh, T.H., Seah, K.H.W., Sitoh, Y.Y.: Smart shirt that can call for help after a fall. Int. J. Softw. Eng. Knowl. Eng. 15(2), 183–188 (2005)CrossRefGoogle Scholar
  99. 99.
    Teri, L., Larson, E.B., Reifler, B.V.: Behavioral disturbance in dementia of the Alzheimer’s type. J. Am. Geriatr. Soc. 36(1), 1–6 (1988)Google Scholar
  100. 100.
    Thome, J., Coogan, A.N., Woods, A.G., Darie, C.C., Hassler, F.: CLOCK genes and circadian rhythmicity in Alzheimer disease. J. Aging Res. Article ID 383091, 4 (2011)Google Scholar
  101. 101.
    Vahdatpour, A., Amini, N., Sarrafzadeh, M.: On-body device localization for health and medical monitoring applications. In: 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 37–44 (2011)Google Scholar
  102. 102.
    van den Noort, J.C., Scholtes, V.A., Becher, J.G., Harlaar, J.: Evaluation of the catch in spasticity assessment in children with cerebral palsy. Arch. Phys. Med. Rehabil. 91(4), 615–623 (2010)CrossRefGoogle Scholar
  103. 103.
    van den Noort, J.C., Scholtes, V.A., Harlaar, J.: Evaluation of clinical spasticity assessment in cerebral palsy using inertial sensors. Gait Posture 30(2), 138–143 (2009)CrossRefGoogle Scholar
  104. 104.
    van Someren, E.J.W., Lazeron, R.H.C., Vonk, B.F.M., Mirmiran, M., Swaab, D.F.: Gravitational artifact in frequency spectra of movement acceleration: implications for actigraphy in young and elderly subjects. J. Neurosci. Methods 65, 55–62 (1996)CrossRefGoogle Scholar
  105. 105.
    Wallhagen, M.I., Strawbridge, W.J., Shema, S.J., Kurata, J., Kaplan, G.A.: Comparative impact of hearing and vision impairment on subsequent functioning. J. Am. Geriatr. Soc. 49, 1086–1092 (2001)CrossRefGoogle Scholar
  106. 106.
    Wan, C., Yuan, B., Wang, L. Miao, Z.: Model-based markerless human body motion capture using active contour. In: 2008 9th International Conference on Signal Processing, ICSP 2008, pp. 1342–1345 (2008)Google Scholar
  107. 107.
    Weber, J.L., Blanc, D., Dittmar, A., Comet, B., Corroy, C., Noury, N., Baghai, R., Vaysse, S., Blinowska, A.: VTAM - a new “biocloth” for ambulatory telemonitoring. In: 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, pp. 299–301 (2003)Google Scholar
  108. 108.
    Weiner, M.F., Koss, E., Patterson, M., Jin, S., Teri, L., Thomas, R., Thal, L.J., Whitehouse, P.: A comparison of the Cohen-Mansfield agitation inventory with the CERAD behavioral rating scale for dementia in community-dwelling persons with Alzheimer’s disease. J. Psychiatr. Res. 32, 347–351 (1998)CrossRefGoogle Scholar
  109. 109.
    Weippl, E., Holzinger, A., Tjoa, A.M.: Security aspects of ubiquitous computing in health care. e & i. Elektrotechnik und Informationstechnik 123(4), 156–161 (2006)CrossRefGoogle Scholar
  110. 110.
    Wimo, A., Winblad, B., Aguero-Torres, H., von Strauss, E.: The magnitude of dementia occurrence in the world. Alzheimer Dis. Assoc. Disord. 17, 63–67 (2010)CrossRefGoogle Scholar
  111. 111.
    Wong, C., Zhang, Z., McKeague, S., Yang, G.-Z.: Multi-person vision-based head detector for markerless human motion capture. In: 2013 IEEE International Conference on Body Sensor Networks (BSN), pp. 1–6 (2013)Google Scholar
  112. 112.
    Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. 31(4), 65:1–65:8 (2012)CrossRefGoogle Scholar
  113. 113.
    Wu, Y.-H., Fischer, D.F., Kalsbeek, A., Garidou-Boof, M.-L., van der Vliet, J., van Heijningen, C., Liu, R.-Y., Zhou, J.-N., Swaab, D.F.: Pineal clock gene oscillation is disturbed in Alzheimer’s disease, due to functional disconnection from the “master clock”. FASEB J. 20(11), 1874–1876 (2006)CrossRefGoogle Scholar
  114. 114.
    Xing, X., Langer, H.: Medical knowledge representation and reasoning in the CHRONIOUS project. In: Behavior Monitoring and Interpretation - Well Being, Workshop on KI-2009, Paderborn (2009)Google Scholar
  115. 115.
    Ye, Y., Ci, S., Katsaggelos, A., Liu, Y.: A multi-camera motion capture system for remote healthcare monitoring. In: 2013 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2013)Google Scholar
  116. 116.
    Yesavage, J.A., Noda, A., Hernandez, B., Friedman, L., Cheng, J.J., Tinklenberg, J.R., Hallmayer, J., O’Hara, R., David, R., Robert, P., Landsverk, E., Zeitzer, J.M.: Circadian clock gene polymorphisms and sleep-wake disturbance in Alzheimer disease. Am. J. Geriatr. Psychiatry 19, 635–643 (2011)CrossRefGoogle Scholar
  117. 117.
    Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefMATHMathSciNetGoogle Scholar
  118. 118.
    Zhang, B., Jiang, S., Wei, D., Marschollek, M., Zhang, W.: State of the art in gait analysis using wearable sensors for healthcare applications. In: 2012 IEEE/ACIS 11th International Conference on Computer and Information Science (ICIS), pp. 213–218 (2012)Google Scholar
  119. 119.
    Zhang, Z., Wong, L., Wu, J.-K.: 3D upper limb motion modeling and estimation using wearable micro-sensors. In: 2010 International Conference on Body Sensor Networks (BSN), pp. 117–123 (2010)Google Scholar
  120. 120.
    Zhang, Z.-Q., Wong, W.-C., Wu, J.-K.: Ubiquitous human upper-limb motion estimation using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 15(4), 513–521 (2011)CrossRefGoogle Scholar
  121. 121.
    Ziefle, M., Klack, L., Wilkowska, W., Holzinger, A.: Acceptance of telemedical treatments – a medical professional point of view. In: Yamamoto, S. (ed.) HCI 2013, Part II. LNCS, vol. 8017, pp. 325–334. Springer, Heidelberg (2013) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Björn Gottfried
    • 1
  • Hamid Aghajan
    • 2
    • 3
  • Kevin Bing-Yung Wong
    • 2
    • 3
  • Juan Carlos Augusto
    • 4
  • Hans Werner Guesgen
    • 5
  • Thomas Kirste
    • 6
  • Michael Lawo
    • 1
  1. 1.Centre for Computing and Communication TechnologiesUniversity of BremenBremenGermany
  2. 2.AIR (Ambient Intelligence Research) LabStanford UniversityStanfordUSA
  3. 3.iMindsGhent UniversityGhentBelgium
  4. 4.Research Group on the Development of Intelligent EnvironmentsMiddlesex UniversityLondonUK
  5. 5.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand
  6. 6.Department of Computer ScienceUniversity of RostockRostockGermany

Personalised recommendations