Working with a domestic assessment system to estimate the need of support and care of elderly and disabled persons: results from field studies

REFERENCES

• Statistisches Bundesamt (Federal Statistical Office) [Internet]. Bevölkerung Deutschlands bis 2060 (Germany’s population by 2060). 12. koordinierte Bevölkerungsvorausberechnung (12th coodinated population projection). Wiesbaden, Germany, 2009. Available from: https://www.destatis.de/DE/Publikationen/Thematisch/Bevoelkerung/VorausberechnungBevoelkerung/BevoelkerungDeutschland2060Presse5124204099004.pdf (last accessed 2014 February 25) (in German)
   Google Scholar
• Frenken T, Lipprandt M, Brell M, et al. Novel approach to unsupervised mobility assessment tests: field trial for aTUG. 6th IEEE International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), San Diego, CA; 2012:131–8
   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
• Levenstein S, Prantera C, Varvo V, et al. Development of the perceived stress questionnaire: a new tool for psychosomatic research. J Psychosomatic Res 1993;37:19–32
   PubMed Web of Science ®Google Scholar
• Fliege H, Rose M, Arck P, et al. Validierung des “Perceived Stress Questionnaire” (PSQ) an einer deutschen Stichprobe (Validation of PSQ on a german sample). Diagnostica 2001;47:142–52 (in German)
   Web of Science ®Google Scholar
• Li Q, Stankovic JA, Hanson M, et al. Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. Sixth International Workshop on Wearable and Implantable Body Sensor Networks; 2009, Washington, IEEE Computer Society, pp. 38–43
   Google Scholar
• Scanaill CN, Carew S, Barralon P, et al. A review of approaches to mobility telemonitoring of the elderly in their living environment. Ann Biomed Eng 2006;34:547–63
   PubMed Web of Science ®Google Scholar
• Anderson D, Luke RH, Keller JM, et al. Linguistic summarization of video for fall detection using voxel person and fuzzy logic. Computer Vis Image Understand 2009;113:80–9
   PubMedGoogle Scholar
• Spehr J, Islami M, Winkelbach S, Wahl FM. Recognition of human behavior patterns using depth information and Gaussian feature maps. Lecture Notes in Informatics (LNI) – Proceedings – Series of the Gesellschaft für Informatik P-208. GI; 2012, pp. 1405–14
   Google Scholar
• Steen EE, Frenken T, Eichelberg M, et al. Modeling individual healthy behavior using home automation sensor data: results from a field trial. J Ambient Intelligence Smart Environ 2013;5:503–23
   Web of Science ®Google Scholar
• Nazerfard E, Rashidi P, Cook DJ. Using association rule mining to discover temporal relations of daily activities. Proceedings of the 9th International Conference on Toward Useful Services for Elderly and People with Disabilities: Smart Homes and Health Telematics. ICOST ’11, 2011, Berlin, Heidelberg, Springer-Verlag, pp. 49–56
   Google Scholar
• Barger T, Brown D, Alwan M. Health status monitoring through analysis of behavioral patterns. 8th Congress of the Italian Association for Artificial Intelligence (AI*IA) on Ambient Intelligence; 2003, Springer-Verlag, pp. 22–7
   Google Scholar
• Floeck M, Litz L. Activity- and inactivity-based approaches to analyze an assisted living environment. Proceedings of the 2008 Second International Conference on Emerging Security Information, Systems and Technologies. SECURWARE’08; 2008, Washington, IEEE Computer Society, pp. 311–16
   Google Scholar
• Floeck M, Litz L. Eine Methodik zur automatischen Generierung von Alarmen für Gesundheitsgefahren aus den Sensorsignalen der Hausautomatisierung (A methodology for automatic generation of alarms for health risks based on events from home automation sensors). Ambient Assisted Living – 2. Deutscher AAL-Kongress; 2009, Berlin, VDE-Verlag; (in German)
   Google Scholar
• Floeck M, Litz L, Rodner T. An ambient approach to emergency detection based on location tracking. Proceedings of the 9th International Conference on Toward Useful Services for Elderly and People with Disabilities: Smart Homes and Health Telematics. ICOST ’11; 2011, Berlin, Heidelberg, Springer-Verlag, pp. 296–302
   Google Scholar
• Hayes TL, Pavel M, Larimer N, et al. Distributed healthcare: simultaneous assessment of multiple individuals. Perv Comput IEEE 2007;6:36–43
   Google Scholar
• Pavel M, Hayes TL, Adami A, et al. Unobtrusive assessment of mobility. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2006, pp. 6277–80
   Google Scholar
• Frenken T, Steen EE, Brell F, et al. Motion pattern generation and recognition for mobility assessments in domestic environments. AAL 2011 – Proceedings of the 1st International Living Usability Lab Workshop on AAL Latest Solutions, Trends and Applications. SciTePress; 2011, pp. 3–12
   Google Scholar
• Pallejà T, Teixidó M, Tresanchez M, Palacín J. Measuring gait using a ground laser range sensor. Sensors 2009;9:9133–46
   PubMedGoogle Scholar
• Stone EE, Skubic M. Passive, in-home gait measurement using an inexpensive depth camera: initial results. 6th IEEE International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), San Diego, CA; 2012, pp. 183–6
   Google Scholar
• Klack L, Möllering C, Ziefle M, Schmitz-Rode T. Future care floor: a sensitive floor for movement monitoring and fall detection in home environments. In: Lin C, Nikita KS, eds. Wireless mobile communication and healthcare. Heidelberg: Springer; 2011:211–18
   Google Scholar
• Lauterbach C, Steinhage A, Techmer A. A large-area sensor system underneath the floor for ambient assisted living applications. In: Mukhopadhyay SC, Postolache O, editors. Pervasive and mobile sensing and computing for healthcare. Berlin, Heidelberg: Springer; 2013:69–87
   Google Scholar
• Grams-Homolová, V. Assessment für die häusliche Versorgung und Pflege (Assessment for home care). Resident Assessment Instrument – HomeCare RAI HC 2.0. Bern: Huber; 2002. 235 p. (in German)
   Google Scholar
• Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Quart 2003;27:425–78
   Web of Science ®Google Scholar
• Neyer FJ, Felber J, Gebhardt C. Entwicklung und Validierung einer Kurzskala zur Erfassung von Technikbereitschaft (Development and validation of a brief measure of technology commitment). Diagnostica 2012;58:87–99 (in German)
   Web of Science ®Google Scholar
• Revermann C, Gerlinger K. Technologien im Kontext von Behinderung. Bausteine für Teilhabe in Alltag und Beruf (Technologies in the context of disabilities. Components for participation in everyday life and job). Berlin: Ed. Sigma; 2010 (in German)
   Google Scholar