Activity Monitoring Systems in Health Care

  • Ben Kröse
  • Tim van Oosterhout
  • Tim van Kasteren


This chapter focuses on activity monitoring in a home setting for health care purposes. First the most current sensing systems are described, which consist of wearable and ambient sensors. Then several approaches for the monitoring of simple actions are discussed, like falls or therapies. After that, the recognition of more complex activities is discussed. A number of applications for the care givers is presented. The chapter concludes with a section on acceptance and privacy.


Wireless Sensor Network Activity Recognition Conditional Random Field Multiple Camera Fall Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research reported in this paper was supported by the Foundation Innovation Alliance SIA with funding from the Dutch Ministry of Education, Culture and Science (OCW), in the framework of the ‘Smart Systems for Smart Services’ project, and through the Pieken in de Delta-program by the Ministry of Economic Affairs and the cities of Utrecht and Lelystad and the provinces of Utrecht, Noord-Holland and Flevoland in the framework of the ‘Zorgen voor Morgen’ project.


  1. 1.
    Aggarwal, J., Park, S.: Human motion: modeling and recognition of actions and interactions. In: 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004. Proceedings, pp. 640–647. IEEE Press, New York (2004) CrossRefGoogle Scholar
  2. 2.
    Aipperspach, R.J., Woodruff, A., Anderson, K., Hooker, B.: Maps of our lives: Sensing people and objects together in the home. Technical Report UCB/EECS-2005-22, EECS Department, University of California, Berkeley, November 30 2005.
  3. 3.
    Alemdar, H.Ö., Yavuz, G.R., Özen, M.O., Kara, Y.E., Incel, Ö.D., Akarun, L., Ersoy, C.: Multi-modal fall detection within the WeCare framework. In: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp. 436–437. ACM, New York (2010) Google Scholar
  4. 4.
    Ali, R., Atallah, L., Lo, B., Yang, G.Z.: Transitional activity recognition with manifold embedding. In: Proc. of BSN09, vol. 1 (2009) Google Scholar
  5. 5.
    Allin, S., Mihailidis, A.: Sit to stand detection and analysis. In: AI in Eldercare: New Solutions to Old Problems: Papers from the AAAI Fall Symposium (2008) Google Scholar
  6. 6.
    Alwan, M., Dalal, S., Mack, D., Kell, S., Turner, B., Leachtenauer, J., Felder, R.: Impact of monitoring technology in assisted living: outcome pilot. IEEE Trans. Inf. Technol. Biomed. 10(1), 192–198 (2006) CrossRefGoogle Scholar
  7. 7.
    Anderson, D., Luke, R.H., Keller, J.M., Skubic, M., Rantz, M., Aud, M.: Linguistic summarization of video for fall detection using voxel person and fuzzy logic. Comput. Vis. Image Underst. 113(1), 80–89 (2009) CrossRefGoogle Scholar
  8. 8.
    Auvinet, E., Multon, F., St-Arnaud, A., Rousseau, J., Meunier, J.: Fall detection using body volume reconstruction and vertical repartition analysis. In: Image and Signal Processing, pp. 376–383 (2010) CrossRefGoogle Scholar
  9. 9.
    Auvinet, E., Multon, F., Saint-Arnaud, A., Rousseau, J., Meunier, J.: Fall detection with multiple cameras: An occlusion-resistant method based on 3D silhouette vertical distribution. IEEE Trans. Inf. Technol. Biomed. 15(2), 290–300 (2011) CrossRefGoogle Scholar
  10. 10.
    Aziz, O., Lo, B., King, R., Darzi, A., Yang, G.Z.: Pervasive body sensor network: an approach to monitoring the post-operative surgical patient. In: International Workshop on Wearable and Implantable Body Sensor Networks, 2006. BSN 2006, pp. 4–18. IEEE Press, New York (2006) Google Scholar
  11. 11.
    Barber, D.: Machine learning. A probabilistic approach (2006) Google Scholar
  12. 12.
    Bertera, E.M., Tran, B.Q., Wuertz, E.M., Bonner, A.: A study of the receptivity to telecare technology in a community-based elderly minority population. J. Telemed. Telecare 13(7), 327 (2007) CrossRefGoogle Scholar
  13. 13.
    Biswas, J., Zhang, D., Qiao, G., Foo, V., Qiang, Q., Philip, Y.: A system for activity monitoring and patient tracking in a smart hospital. In: Proceedings of 4th International Conference on Smart Home and Health Telematic Google Scholar
  14. 14.
    Bouchard, B., Giroux, S., Bouzouane, A.: A keyhole plan recognition model for Alzheimer’s patients: first results. Appl. Artif. Intell. 21(7), 623–658 (2007) CrossRefGoogle Scholar
  15. 15.
    Bourke, A.K., O’Brien, J.V., Lyons, G.M.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2), 194–199 (2007) CrossRefGoogle Scholar
  16. 16.
    Broch, J., Maltz, D.A., Johnson, D.B., Hu, Y.-C., Jetcheva, J.: A performance comparison of multi-hop wireless ad hoc network routing protocols. In: MobiCom ’98: Proceedings of the 4th Annual ACM/IEEE International Conference on Mobile Computing and Networking, pp. 85–97. ACM, New York (1998). doi: 10.1145/288235.288256 CrossRefGoogle Scholar
  17. 17.
    Canas, J.M., Marugán, S., Marrón, M., Garcia, J.: Visual fall detection for intelligent spaces. In: IEEE International Symposium on Intelligent Signal Processing (WISP 2009), pp. 157–162. IEEE Press, New York (2009) CrossRefGoogle Scholar
  18. 18.
    Chen, J., Kwong, K., Chang, D., Luk, J., Bajcsy, R.: Wearable sensors for reliable fall detection. In: 27th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (IEEE-EMBS 2005), pp. 3551–3554. IEEE Press, New York (2005) Google Scholar
  19. 19.
    Chen, D., Bharucha, A.J., Wactlar, H.D.: Intelligent video monitoring to improve safety of older persons. In: 29th Annual International Conference of the Engineering in Medicine and Biology Society (EMBS 2007), pp. 3814–3817. IEEE Press, New York (2007) CrossRefGoogle Scholar
  20. 20.
    Cho, Y., Nam, Y., Choi, Y.J., Cho, W.D.: SmartBuckle: human activity recognition using a 3-axis accelerometer and a wearable camera. In: Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments, pp. 1–3. ACM, New York (2008) CrossRefGoogle Scholar
  21. 21.
    Consolvo, S., McDonald, D.W., Toscos, T., Chen, M.Y., Froehlich, J., Harrison, B., Klasnja, P., LaMarca, A., LeGrand, L., Libby, R., Smith, I., Landay, J.A.: Activity sensing in the wild: a field trial of ubifit garden. In: CHI ’08: Proceeding of the Twenty-Sixth Annual SIGCHI Conference on Human Factors in Computing Systems, pp. 1797–1806. ACM, New York (2008). doi: 10.1145/1357054.1357335 CrossRefGoogle Scholar
  22. 22.
    Das, R.: RFID explained. IDTechEX White Paper (2005) Google Scholar
  23. 23.
    DiCenso, A., Cullum, N., Ciliska, D.: Implementing evidence-based nursing: some misconceptions. Evid.-Based Nurs. 1(2), 38 (1998) CrossRefGoogle Scholar
  24. 24.
    Diraco, G., Leone, A., Siciliano, P.: An active vision system for fall detection and posture recognition in elderly healthcare. In: Design, Automation & Test in Europe Conference & Exhibition (DATE 2010), pp. 1536–1541. IEEE Press, New York (2010) Google Scholar
  25. 25.
    Duong, T.V., Bui, H.H., Phung, D.Q., Venkatesh, S.: Activity recognition and abnormality detection with the switching hidden semi-Markov model. In: CVPR ’05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 838–845. IEEE Comput. Soc., Washington (2005). doi: 10.1109/CVPR.2005.61 Google Scholar
  26. 26.
    Duong, T.V., Bui, H.H., Phung, D.Q., Venkatesh, S.: Activity recognition and abnormality detection with the switching hidden semi-Markov model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 838–845. IEEE Press, New York (2005) CrossRefGoogle Scholar
  27. 27.
    Fishkin, K.P., Philipose, M., Rea, A.: Hands-on RFID: Wireless wearables for detecting use of objects. In: Proceedings of the Ninth IEEE International Symposium on Wearable Computers, pp. 38–41. IEEE Press, New York (2005) CrossRefGoogle Scholar
  28. 28.
    Fogarty, J., Au, C., Hudson, S.E.: Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition. In: UIST ’06: Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology, pp. 91–100. ACM, New York (2006). doi: 10.1145/1166253.1166269 CrossRefGoogle Scholar
  29. 29.
    Fu, Z., Culurciello, E., Lichtsteiner, P., Delbruck, T.: Fall detection using an address-event temporal contrast vision sensor. In: IEEE International Symposium on Circuits and Systems (ISCAS 2008), pp. 424–427. IEEE Press, New York (2008) Google Scholar
  30. 30.
    Garrod, R., Bestall, J., Paul, E., Wedzicha, J., Jones, P.: Development and validation of a standardized measure of activity of daily living in patients with severe COPD: the London Chest Activity of Daily Living scale (LCADL). Respir. Med. 94(6), 589–596 (2000) CrossRefGoogle Scholar
  31. 31.
    Gavrila, D.M.: The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73(1), 82–98 (1999) MATHCrossRefGoogle Scholar
  32. 32.
    Goldman, J., Hudson, Z.: Perspective: virtually exposed: privacy and e-health. Health Aff. 19(6), 140 (2000) CrossRefGoogle Scholar
  33. 33.
    Grassi, M., Lombardi, A., Rescio, G., Malcovati, P., Malfatti, M., Gonzo, L., Leone, A., Diraco, G., Distante, C., Siciliano, P., et al.: A hardware-software framework for high-reliability people fall detection. In: Sensors, 2008 IEEE, pp. 1328–1331 (2008) CrossRefGoogle Scholar
  34. 34.
    Győrbíró, N., Fábián, Á., Hományi, G.: An activity recognition system for mobile phones. Mob. Netw. Appl. 14(1), 82–91 (2009) CrossRefGoogle Scholar
  35. 35.
    Haigh, K.Z., Yanco, H.: Automation as caregiver: A survey of issues and technologies. In: Proceedings of the AAAI-02 Workshop “Automation as Caregiver”, pp. 39–53 (2002). AAAI Technical Report WS-02-02 Google Scholar
  36. 36.
    Hamel, M., Fontaine, R., Boissy, P.: In-home telerehabilitation for geriatric patients. IEEE Eng. Med. Biol. Mag. 27(4), 29–37 (2008) CrossRefGoogle Scholar
  37. 37.
    Hazelhoff, L., Han, J., de With, P.H.N.: Video-based fall detection in the home using principal component analysis. In: Advanced Concepts for Intelligent Vision Systems: 10th International Conference (ACIVS 2008), Juan-les-Pins, France, October 20–24, 2008, p. 298. Springer, New York (2008) CrossRefGoogle Scholar
  38. 38.
    Hill, J., Szewczyk, R., Woo, A., Hollar, S., Culler, D., Pister, K.: System architecture directions for networked sensors. ACM SIGPLAN Not. 35(11), 93–104 (2000). doi: 10.1145/356989.356998 CrossRefGoogle Scholar
  39. 39.
    Ho, L., Moh, M., Walker, Z., Hamada, T., Su, C.F.: A prototype on RFID and sensor networks for elder healthcare: progress report. In: Proceedings of the 2005 ACM SIGCOMM Workshop on Experimental Approaches to Wireless Network Design and Analysis, pp. 70–75. ACM, New York (2005) CrossRefGoogle Scholar
  40. 40.
    Hoey, J., Poupart, P., Boutilier, C., Mihailidis, A.: POMDP models for assistive technology. In: Proc. AAAI Fall Symposium on Caring Machines: AI in Eldercare (2005) Google Scholar
  41. 41.
    Hoey, J., Poupart, P., Bertoldi, A., Craig, T., Boutilier, C., Mihailidis, A.: Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process. Comput. Vis. Image Underst. 114(5), 503–519 (2010) CrossRefGoogle Scholar
  42. 42.
    Hong, Y.J., Kim, I.J., Ahn, S.C., Kim, H.G.: Mobile health monitoring system based on activity recognition using accelerometer. Simul. Model. Pract. Theory 18(4), 446–455 (2010) CrossRefGoogle Scholar
  43. 43.
    Hori, T., Nishida, Y., Murakami, S.: Pervasive sensor system for evidence based nursing care support. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1680–1685 (2006) Google Scholar
  44. 44.
    Intille, S.S.: A new research challenge: persuasive technology to motivate healthy aging. IEEE Trans. Inf. Technol. Biomed. 8(3), 235–237 (2004) CrossRefGoogle Scholar
  45. 45.
    Jansen, B., Deklerck, R.: Context aware inactivity recognition for visual fall detection. In: Pervasive Health Conference and Workshops, 2006, pp. 1–4. IEEE Press, New York (2007) Google Scholar
  46. 46.
    Jansen, B., Temmermans, F., Deklerck, R.: 3D human pose recognition for home monitoring of elderly. In: 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBS 2007), pp. 4049–4051. IEEE Press, New York (2007) CrossRefGoogle Scholar
  47. 47.
    Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 10(1), 156–167 (2006) CrossRefGoogle Scholar
  48. 48.
    Katz, S.: Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. J. Am. Geriatr. Soc. 31(12), 721–726 (1983) Google Scholar
  49. 49.
    Katz, J.E., Rice, R.E.: Public views of mobile medical devices and services: A US national survey of consumer sentiments towards RFID healthcare technology. Int. J. Med. Inform. 78(2), 104–114 (2009) CrossRefGoogle Scholar
  50. 50.
    Kautz, H., Arnstein, L., Borriello, G., Etzioni, O., Fox, D.: An overview of the assisted cognition project. In: AAAI-2002 Workshop on Automation as Caregiver: The Role of Intelligent Technology in Elder Care, pp. 60–65 (2002) Google Scholar
  51. 51.
    Kerr, K., White, J., Barr, D., Mollan, R.: Analysis of the sit-stand-sit movement cycle in normal subjects. Clin. Biomech. 12(4), 236–245 (1997) CrossRefGoogle Scholar
  52. 52.
    Kurz, X., Scuvee-Moreau, J., Rive, B., Dresse, A.: A new approach to the qualitative evaluation of functional disability in dementia. Int. J. Geriatr. Psychiatry 18(11), 1050–1055 (2003) CrossRefGoogle Scholar
  53. 53.
    Kwolek, B.: Face tracking system based on color, stereovision and elliptical shape features. In: IEEE Conference on Advanced Video and Signal Based Surveillance, p. 21. IEEE Comput. Soc., Los Alamitos (2003). doi: 10.1109/AVSS.2003.1217897 CrossRefGoogle Scholar
  54. 54.
    Liao, L., Fox, D., Kautz, H.: Extracting places and activities from gps traces using hierarchical conditional random fields. Int. J. Robot. Res. 26(1), 119 (2007) CrossRefGoogle Scholar
  55. 55.
    Living independently—quietcare system.
  56. 56.
    Londei, S.T., Rousseau, J., Ducharme, F., St-Arnaud, A., Meunier, J., Saint-Arnaud, J., Giroux, F.: An intelligent videomonitoring system for fall detection at home: perceptions of elderly people. J. Telemed. Telecare 15(8), 383 (2009) CrossRefGoogle Scholar
  57. 57.
    LoPresti, E.F., Mihailidis, A., Kirsch, N.: Assistive technology for cognitive rehabilitation: State of the art. Neuropsychol. Rehabil. 14(1–2), 5–39 (2004) CrossRefGoogle Scholar
  58. 58.
    Marin-Perianu, M., Lombriser, C., Amft, O., Havinga, P., Tröster, G.: Distributed activity recognition with fuzzy-enabled wireless sensor networks. In: DCOSS ’08: Proceedings of the 4th IEEE International Conference on Distributed Computing in Sensor Systems, pp. 296–313. Springer, Berlin (2008). doi: 10.1007/978-3-540-69170-9_20 Google Scholar
  59. 59.
    Messing, R., Pal, C., Kautz, H.: Activity recognition using the velocity histories of tracked keypoints. In: ICCV ’09: Proceedings of the Twelfth IEEE International Conference on Computer Vision. IEEE Comput. Soc., Washington (2009) Google Scholar
  60. 60.
    Messing, R., Pal, C., Kautz, H.: Activity recognition using the velocity histories of tracked keypoints. In: IEEE 12th International Conference on Computer Vision 2009, pp. 104–111. IEEE Press, New York (2010) Google Scholar
  61. 61.
    Mihailidis, A., Barbenel, J.C., Fernie, G.: The efficacy of an intelligent cognitive orthosis to facilitate handwashing by persons with moderate to severe dementia. Neuropsychol. Rehabil. 14(1–2), 135–171 (2004) CrossRefGoogle Scholar
  62. 62.
    Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(2–3), 90–126 (2006) CrossRefGoogle Scholar
  63. 63.
    Morency, L.P., Quattoni, A., Darrell, T.: Latent-dynamic discriminative models for continuous gesture recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE Press, New York (2007) CrossRefGoogle Scholar
  64. 64.
    Mynatt, E.D., Essa, I., Rogers, W.: Increasing the opportunities for aging in place. In: Proceedings of the 2000 Conference on Universal Usability, pp. 65–71. ACM, New York (2000) CrossRefGoogle Scholar
  65. 65.
    Mynatt, E.D., Rowan, J., Craighill, S., Jacobs, A.: Digital family portraits: supporting peace of mind for extended family members. In: CHI, pp. 333–340 (2001). Google Scholar
  66. 66.
    Nait-Charif, H., McKenna, S.J.: Activity summarisation and fall detection in a supportive home environment. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), pp. 323–326 (2004) CrossRefGoogle Scholar
  67. 67.
    Nevitt, M.C., Cummings, S.R., Hudes, E.S.: Risk factors for injurious falls: a prospective study. J. Gerontol. 46(5), 164 (1991) Google Scholar
  68. 68.
    Rijnboutt, J., Evers, V., Kröse, B.: Cliënten willen meer controle over de camera. In: ICT en Zorg, pp. 30–32 (2010) (In Dutch) Google Scholar
  69. 69.
    Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Monocular 3d head tracking to detect falls of elderly people. In: 28th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBS’06), pp. 6384–6387. IEEE Press, New York (2008) Google Scholar
  70. 70.
    Sangwan, R., Qiu, R., Jessen, D.: Using RFID tags for tracking patients, charts and medical equipment within an integrated health delivery network. In: Proc. IEEE Networking, Sensing and Control, pp. 1070–1074. IEEE Press, New York (2005) CrossRefGoogle Scholar
  71. 71.
    Sinha, A., Chandrakasan, A.: Dynamic power management in wireless sensor networks. IEEE Des. Test Comput. 18(2), 62–74 (2001). doi: 10.1109/54.914626 CrossRefGoogle Scholar
  72. 72.
    Sixsmith, A., Johnson, N.: A smart sensor to detect the falls of the elderly. IEEE Pervasive Comput. 42–47 (2004) Google Scholar
  73. 73.
    Sohrabi, K., Gao, J., Ailawadhi, V., Pottie, G.J.: Protocols for self-organization of a wireless sensor network. IEEE Pers. Commun. 7(5), 16–27 (2000) CrossRefGoogle Scholar
  74. 74.
    Song, W.J., Son, S.H., Choi, M., Kang, M.: Privacy and security control architecture for ubiquitous RFID healthcare system in wireless sensor networks. In: IEEE Int. Conf. Consumer Electronics, Digest of Technical Papers, pp. 239–240. IEEE Press, New York (2006) CrossRefGoogle Scholar
  75. 75.
    Stikic, M., Huynh, T., Van Laerhoven, K., Schiele, B.: ADL recognition based on the combination of RFID and accelerometer sensing. In: Second International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2008), pp. 258–263. IEEE Press, New York (2008) CrossRefGoogle Scholar
  76. 76.
    Tam, T., Dolan, A., Boger, J., Mihailidis, A.: An intelligent emergency response system: Preliminary development and testing of a functional health monitoring system. Gerontechnology 4, 209–222 (2006) CrossRefGoogle Scholar
  77. 77.
    Tapia, E.M., Intille, S.S., Lopez, L., Larson, K.: The design of a portable kit of wireless sensors for naturalistic data collection. In: Proceedings of the 4th International Conference on Pervasive Computing. Lecture Notes in Computer Science, vol. 3968, pp. 117–134. Springer, Berlin (2006) Google Scholar
  78. 78.
    Töreyin, B.U., Dedeoğlu, Y., Çetin, A.E.: HMM based falling person detection using both audio and video. In: Computer Vision in Human-Computer Interaction, pp. 211–220 (2005) CrossRefGoogle Scholar
  79. 79.
    Truyen, T.T., Phung, D.Q., Bui, H.H., Venkatesh, S.: Hierarchical semi-Markov conditional random fields for recursive sequential data. In: Neural Information Processing Systems (NIPS) (2008) Google Scholar
  80. 80.
    Turaga, P., Chellappa, R., Subrahmanian, V., Udrea, O.: Machine recognition of human activities: A survey. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1473–1488 (2008) CrossRefGoogle Scholar
  81. 81.
    Vail, D.L., Veloso, M.M., Lafferty, J.D.: Conditional random fields for activity recognition. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1–8. ACM, New York (2007) CrossRefGoogle Scholar
  82. 82.
    van Dam, T., Langendoen, K.: An adaptive energy-efficient mac protocol for wireless sensor networks. In: SenSys ’03: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, pp. 171–180. ACM, New York (2003). doi: 10.1145/958491.958512 Google Scholar
  83. 83.
    van Kasteren, T.L.M., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 1–9. ACM, New York (2008) CrossRefGoogle Scholar
  84. 84.
    van Kasteren, T.L.M., Noulas, A., Englebienne, G., Kröse, B.J.A.: Accurate activity recognition in a home setting. In: UbiComp ’08: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 1–9. ACM, New York (2008). doi: 10.1145/1409635.1409637 CrossRefGoogle Scholar
  85. 85.
    van Kasteren, T.L.M., Englebienne, G., Kröse, B.: Transferring knowledge of activity recognition across sensor networks. IEEE Pervasive Comput. 283–300 (2010) Google Scholar
  86. 86.
    van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Activity recognition using semi-Markov models on real world smart home datasets. J. Ambient Intell. Smart Environ. 2(3), 311–325 (2010) Google Scholar
  87. 87.
    van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: An activity monitoring system for elderly care using generative and discriminative models. Pers. Ubiquitous Comput. 14(6), 489–498 (2010) CrossRefGoogle Scholar
  88. 88.
    Virone, G., Alwan, M., Dalal, S., Kell, S.W., Turner, B., Stankovic, J.A., Felder, R.: Behavioral patterns of older adults in assisted living. IEEE Trans. Inf. Technol. Biomed. 12(3), 387–398 (2008) CrossRefGoogle Scholar
  89. 89.
    Visser, T., Vastenburg, M., Keyson, D.: SnowGlobe: the development of a prototype awareness system for longitudinal field studies. In: Proceedings of the 8th ACM Conference on Designing Interactive Systems, pp. 426–429. ACM, New York (2010) CrossRefGoogle Scholar
  90. 90.
    Wang, S., Skubic, M.: Density map visualization from motion sensors for monitoring activity level. In: 4th IET International Conference on Intelligent Environments (2008) Google Scholar
  91. 91.
    Wang, F., Stone, E., Dai, W., Skubic, M., Keller, J.: Gait analysis and validation using voxel data. In: Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC 2009), pp. 6127–6130. IEEE Press, New York (2009) CrossRefGoogle Scholar
  92. 92.
    Website: Bosch health buddy.
  93. 93.
  94. 94.
    Williams, A., Ganesan, D., Hanson, A.: Aging in place: fall detection and localization in a distributed smart camera network. In: Proceedings of the 15th International Conference on Multimedia, pp. 892–901. ACM, New York (2007) CrossRefGoogle Scholar
  95. 95.
    Wilson, D.H., Consolvo, S., Fishkin, K.P., Philipose, M.: Current practices for in-home monitoring of elders’ activities of daily living: A study of case managers. Technical report, Intel Research Seattle (2005) Google Scholar
  96. 96.
    Wilson, S., Davies, R., Stone, T., Hammerton, J., Ware, P., Mawson, S., Harris, N., Eccleston, C., Zheng, H., Black, N., et al.: Developing a telemonitoring system for stroke rehabilitation. Contemp. Ergon. 2007, 505 (2007) Google Scholar
  97. 97.
    Wren, C.R., Tapia, E.M.: Toward scalable activity recognition for sensor networks. In: LoCa (2006) Google Scholar
  98. 98.
    Wu, J., Osuntogun, A., Choudhury, T., Philipose, M., Rehg, J.M.: A scalable approach to activity recognition based on object use. In: IEEE 11th International Conference on Computer Vision (ICCV 2007), pp. 1–8. IEEE Press, New York (2007) CrossRefGoogle Scholar
  99. 99.
    Zhuang, X., Huang, J., Potamianos, G., Hasegawa-Johnson, M.: Acoustic fall detection using Gaussian mixture models and GMM supervectors. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 69–72 (2009). doi: 10.1109/ICASSP.2009.4959522 CrossRefGoogle Scholar
  100. 100.
    Xu, Y., Heidemann, J., Estrin, D.: Geography-informed energy conservation for ad hoc routing. In: MobiCom ’01: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, pp. 70–84. ACM, New York (2001). doi: 10.1145/381677.381685 CrossRefGoogle Scholar
  101. 101.
    Yu, M., Naqvi, S.M., Chambers, J.: Fall detection in the elderly by head tracking. In: IEEE/SP 15th Workshop on Statistical Signal Processing (SSP’09), pp. 357–360. IEEE Press, New York (2009) Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Ben Kröse
    • 1
    • 2
  • Tim van Oosterhout
    • 2
  • Tim van Kasteren
    • 3
  1. 1.University of AmsterdamAmsterdamThe Netherlands
  2. 2.Amsterdam University of Applied ScienceAmsterdamThe Netherlands
  3. 3.Boğaziçi UniversityBebek, IstanbulTurkey

Personalised recommendations