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Kangas, M., et al. (2008). Comparison of low-complexity fall detection algorithms for body attached
accelerometers. Gait & Posture 28(2): 285–291.
Kleinberger, T., et al. (2007). Ambient Intelligence in Assisted Living: Enable elderly people to handle
future interfaces, In C. Stephanidis (ed.), Universal Access in HCI, Part II, HCII, LNCS 4555,
pp. 103–112. Springer, New York.
Klenk, J., et al. (2011). Comparison of acceleration signals of simulated and real-world backward
falls. Medical Engineering & Physics 33(3): 368–373.
Kwiatkowska, M., et al. (2004). PRISM 2.0: A Tool for Probabilistic model checking. In Proceedings of
the First International Conference on Quantitative Evaluation of Systems (QEST’04), pp. 322–323.
IEEE Computer Society, Washington, DC.
Li, Q., et al. (2009, June). Accurate, fast fall detection using gyroscopes and accelerometer-derived
posture information. In 2009 Sixth International Workshop on Wearable and Implantable Body Sensor
Networks, pp. 138–143. IEEE, Piscataway, NJ.
Lopez, F., et al. (2011, June). Cognitive wireless sensor network device for AAL scenarios. International
Workshop on Ambient Assisted Living, pp. 116–121. Springer, Berlin.
Luštrek, M., et al. (2009). Fall detection and activity recognition with machine learning. Informatica
33(2): 205–212
Manyika, J., et al. (2013). Disruptive Technologies: Advances that will Transform Life, Business, and the
Global Economy. Global Institute report. McKinsey, San Fransisco, CA.
Metcalf, C., et al. (2009). Fabric-based strain sensors for measuring movement in wearable tele monitoring
applications. IET Conference on Assisted Living, p. 13. London, UK.
Oregon Health. (2010). In-Home Sensors Sport Dementia Signs in Elderly, Oregon Health and Science
University. http://www.ohsu.edu/ohsuedu/newspub/releases.
Palanque, P., et al. (2007). Improving interactive systems usability using formal description
techniques: Application to Health care. In Proceedings of 3rd Human—Computer Interaction and
Usability Engineering of the Austrian Computer Society Conference HCI and Usability for Medicine
and Health Care, pp. 21–40. Springer-Verlag, Heidelberg.
Rodrigues, G. N., et al. (2012), Dependability analysis in the ambient assisted living domain:
An exploratory case study. The Journal of System and Software 85: 112–131.
Rougier, C., et al. (2011, June). Fall detection from depth map video sequences. International Conference
on Smart Homes and Health Telematics, pp. 121–128. Springer, Berlin.
Schindhelm, C. K., et al. (2011). Overview of indoor positioning technologies for context aware AAL
applications. In Ambient Assisted Living, pp. 273–291. Springer, Berlin.
Segal-Gidan, F., et al. (2011). Alzheimer’s disease management guideline: Update 2008. Alzheimer’s
and Dementia 7(3): e51–e59. DOI:10.1016/j.jalz.2010.07.005.
Slyper, R., and Hodgins, J. K. (2008, July). Action capture with accelerometers. In Proceedings of the 2008
ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 193–199. Eurographics
Association, San Diego, CA.
Tabar, A. M., et al. (2006, October). Smart home care network using sensor fusion and distributed
vision-based reasoning. In Proceedings of the 4th ACM International Workshop on Video Surveillance
and Sensor Networks, pp. 145–154. ACM, San Diego, CA.
Tunca, C., et al. (2014), Multimodal wireless sensor network-based ambient assisted living in real
homes with multiple residents. Sensors (Basel) 14(6): 9692–9719. DOI: 10.3390/s140609692.
Virone, G., et al. (2006, April). An assisted living oriented information system based on a residential
wireless sensor network. In 1st Transdisciplinary Conference on Distributed Diagnosis and Home
Healthcare. D2H2, pp. 95–100. IEEE, Piscataway, NJ.
Ye, J., et al. (2012). Situation identification techniques in pervasive computing: A review. Pervasive and
Mobile Computing 8(1): 36-66. DOI: 10.1016/j.pmcj.2011.01.004.
Yu, H., et al. (2003). An adaptive shared control system for an intelligent mobility aid for the elderly.
Autonomous Robots 15: 53–66.
Aspects of Ambient Assisted Living and Its Applications
Kangas, M., et al. (2008). Comparison of low-complexity fall detection algorithms for body attached
accelerometers. Gait & Posture 28(2): 285–291.
Kleinberger, T., et al. (2007). Ambient Intelligence in Assisted Living: Enable elderly people to handle
future interfaces, In C. Stephanidis (ed.), Universal Access in HCI, Part II, HCII, LNCS 4555,
pp. 103–112. Springer, New York.
Klenk, J., et al. (2011). Comparison of acceleration signals of simulated and real-world backward
falls. Medical Engineering & Physics 33(3): 368–373.
Kwiatkowska, M., et al. (2004). PRISM 2.0: A Tool for Probabilistic model checking. In Proceedings of
the First International Conference on Quantitative Evaluation of Systems (QEST’04), pp. 322–323.
IEEE Computer Society, Washington, DC.
Li, Q., et al. (2009, June). Accurate, fast fall detection using gyroscopes and accelerometer-derived
posture information. In 2009 Sixth International Workshop on Wearable and Implantable Body Sensor
Networks, pp. 138–143. IEEE, Piscataway, NJ.
Lopez, F., et al. (2011, June). Cognitive wireless sensor network device for AAL scenarios. International
Workshop on Ambient Assisted Living, pp. 116–121. Springer, Berlin.
Luštrek, M., et al. (2009). Fall detection and activity recognition with machine learning. Informatica
33(2): 205–212
Manyika, J., et al. (2013). Disruptive Technologies: Advances that will Transform Life, Business, and the
Global Economy. Global Institute report. McKinsey, San Fransisco, CA.
Metcalf, C., et al. (2009). Fabric-based strain sensors for measuring movement in wearable tele monitoring
applications. IET Conference on Assisted Living, p. 13. London, UK.
Oregon Health. (2010). In-Home Sensors Sport Dementia Signs in Elderly, Oregon Health and Science
University. http://www.ohsu.edu/ohsuedu/newspub/releases.
Palanque, P., et al. (2007). Improving interactive systems usability using formal description
techniques: Application to Health care. In Proceedings of 3rd Human—Computer Interaction and
Usability Engineering of the Austrian Computer Society Conference HCI and Usability for Medicine
and Health Care, pp. 21–40. Springer-Verlag, Heidelberg.
Rodrigues, G. N., et al. (2012), Dependability analysis in the ambient assisted living domain:
An exploratory case study. The Journal of System and Software 85: 112–131.
Rougier, C., et al. (2011, June). Fall detection from depth map video sequences. International Conference
on Smart Homes and Health Telematics, pp. 121–128. Springer, Berlin.
Schindhelm, C. K., et al. (2011). Overview of indoor positioning technologies for context aware AAL
applications. In Ambient Assisted Living, pp. 273–291. Springer, Berlin.
Segal-Gidan, F., et al. (2011). Alzheimer’s disease management guideline: Update 2008. Alzheimer’s
and Dementia 7(3): e51–e59. DOI:10.1016/j.jalz.2010.07.005.
Slyper, R., and Hodgins, J. K. (2008, July). Action capture with accelerometers. In Proceedings of the 2008
ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 193–199. Eurographics
Association, San Diego, CA.
Tabar, A. M., et al. (2006, October). Smart home care network using sensor fusion and distributed
vision-based reasoning. In Proceedings of the 4th ACM International Workshop on Video Surveillance
and Sensor Networks, pp. 145–154. ACM, San Diego, CA.
Tunca, C., et al. (2014), Multimodal wireless sensor network-based ambient assisted living in real
homes with multiple residents. Sensors (Basel) 14(6): 9692–9719. DOI: 10.3390/s140609692.
Virone, G., et al. (2006, April). An assisted living oriented information system based on a residential
wireless sensor network. In 1st Transdisciplinary Conference on Distributed Diagnosis and Home
Healthcare. D2H2, pp. 95–100. IEEE, Piscataway, NJ.
Ye, J., et al. (2012). Situation identification techniques in pervasive computing: A review. Pervasive and
Mobile Computing 8(1): 36-66. DOI: 10.1016/j.pmcj.2011.01.004.
Yu, H., et al. (2003). An adaptive shared control system for an intelligent mobility aid for the elderly.
Autonomous Robots 15: 53–66.