Smart care home system: a platform for eAssistance

• Aerotel Medical Systems (2015) Mdkeeper. http://www.aerotel.com. Accessed 1 Feb 2018

• Alsinglawi B, Nguyen QV, Gunawardana U, Maeder A, Simoff S (2017) Rfid systems in healthcare settings and activity of daily living in smart homes: a review. E-Health Telecommun Syst Netw 6:1–17

  Article  Google Scholar 

• AMD Global Telemedicine (2015) Carecompanion. http://www.amdtelemedicine.com/. Accessed 1 Feb 2018

• Applica CESEP Alphametrics (2007) Men and women with disabilities in the EU

• Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. Pervasive Comput pp 1–17

• Beltrán A, Forn R, Garicano L, Martínez M, Vázquez P (2009) Impulsar un cambio posible en el sistema sanitario. McKinsey & Company y FEDEA, Madrid

  Google Scholar 

• Bosch (2015) Bosch healthcare. http://www.bosch-telehealth.com. Accessed 1 Feb 2018

• Cabrera M, Arredondo MT, Villalar J, Naranjo J, Karaseitanidis I (2004) Mobile systems as a mean to achieve e-inclusion. In: Electrotechnical conference, 2004. MELECON 2004. Proceedings of the 12th IEEE Mediterranean, Dubrovnik, Croatia, vol 2, pp 653–656

• Cook DJ (2012) Learning setting-generalized activity models for smart spaces. IEEE Intell Syst 27(1):32–38. https://doi.org/10.1109/MIS.2010.112

  Article  Google Scholar 

• Das B, Krishnan NC, Cook DJ (2012) Automated activity interventions to assist with activities of daily living. In: Bosse T (ed) Agents Ambient Intell, Ambient Intell Smart Environ, vol 12. Ios PressInc, Amsterdam, pp 137–158

  Google Scholar 

• European Commission (2015) European innovation partnership on active and healthy ageing. https://webgate.ec.europa.eu. Accessed 1 Feb 2018

• European Network on IndependentLiving (ENIL) (1998) Shaping our futures. In: A conference on Independent Living, London

• Eurostat (2016) Population projections 2015–2081. http://epp.eurostat.ec.europa.eu

• Hagler S, Austin D, Hayes TL, Kaye J, Pavel M (2010) Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders. IEEE Trans Biomed Eng 57(4):813–820

  Article  Google Scholar 

• Han J, Kamber M, Pei J (2006) Data mining, second edition: concepts and techniques. The Morgan Kaufmann series in data management systems, Elsevier Science. http://books.google.es/books?id=AfL0t-YzOrEC

• He J, Li H, Tan J (2007) Real-time daily activity classification with wireless sensor networks using hidden markov model. In: 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society. EMBS, IEEE, pp 3192–3195

• Honeywell (2015) Honeywell hommed. http://www.hommed.com. Accessed 1 Feb 2018

• Hong YJ, Kim IJ, Ahn SC, Kim HG (2010) Mobile health monitoring system based on activity recognition using accelerometer. Simul Model Pract Theory 18(4):446–455

  Article  Google Scholar 

• Independent Living Committee (1992) Tools for power: resource kit for independent living. In: Disabled People’s International

• Isoda Y, Kurakake S, Nakano H (2004) Ubiquitous sensors based human behavior modeling and recognition using a spatio-temporal representation of user states. In: 18th International Conference on Advanced Information Networking and Applications, 2004, IEEE, vol 1, pp 512–517

• Kaye JA, Austin J, Dodge HH, Mattek N, Riley T, Seelye A, Seelye A, Sharma N, Wild K (2016) Ecologically valid assessment of life activites: unobtrusive continuos monitoring with sensors. Alzheimer’s Dementia: J Alzheimer’s Assoc 12(7):374

  Article  Google Scholar 

• Krishnan NC, Cook DJ (2014) Activity recognition on streaming sensor data. Pervasive Mob Comput 10(B):138–154

  Article  Google Scholar 

• Krishnan NC, Colbry D, Juillard C, Panchanathan S (2008) Real time human activity recognition using triaxial accelerometers. In: Sensors, signals and information processing workshop

• Kyriazakos S, Mihaylov M, Anggorojati B, Mihovska A, Craciunescu R, Fratu O, Prasad R (2016) ewall: An intelligent caring home environment offering personalized context-aware applications based on advanced sensing. Wirel Pers Commun 87(3):1093–1111

  Article  Google Scholar 

• Martín M (2015) Siafu an open source context simulator. http://siafusimulator.org/. Accessed 1 Feb 2018

• Marufuzzaman M, Raez M, Ali M, Rahman LF (2013) Classification and detection of intelligent house resident activities using multiagent. In: Proceedings of the 4th international conference on computing and informatics, Sarawak, Malaysia, pp 195–200

• Modayil J, Bai T, Kautz H (2008) Improving the recognition of interleaved activities. In: Proceedings of the 10th international conference on Ubiquitous computing, ACM, pp 40–43

• Mynatt ED, Rowan J, Craighill S, Jacobs A (2001) Digital family portraits: supporting peace of mind for extended family members. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, New York, USA, pp 333–340

• Newland P, Wagner JM, Salter A, Thomas FP, Skubic M, Rantz M (2016) Exploring the feasibility and acceptability of sensor monitoring of gait and falls in the homes of persons with multiple sclerosis. Gait Posture 49:277–282

  Article  Google Scholar 

• Park S, Kautz H (2008) Hierarchical recognition of activities of daily living using multi-scale, multi-perspective vision and rfid. In: 2008 IET 4th International Conference on Intelligent Environments, IET, pp 1–4

• Patterson DJ, Fox D, Kautz H, Philipose M (2005) Fine-grained activity recognition by aggregating abstract object usage. In: Proceedings of the 2005 9th IEEE International Symposium on Wearable Computers, IEEE, pp 44–51

• Petersen J, Austin D, Mattek N, Kaye J (2015) Time out-of-home and cognitive, physical, and emotional wellbeing of older adults: a longitudinal mixed effects model. PLoS One 10(10):1–16

  Article  Google Scholar 

• Poncela A, Urdiales C, Pérez EJ, Sandoval F (2009) A new efficiency-weighted strategy for continuous human/robot cooperation in navigation. IEEE Trans Syst, Man, Cybern-Part A: Syst Hum 39(3):386–500

  Article  Google Scholar 

• Ramos C, Autusto J, Shapiro D (2008) Ambient intelligence—the next step for artificial intelligence. IEEE Intell Syst 23(2):15–18

  Article  Google Scholar 

• Rapid-I (2015) Rapidminer. http://rapid-i.com. Accessed 1 Feb 2018

• Rashidi P, Cook DJ, Holder LB, Schmitter-Edgecombe M (2011) Discovering activities to recognize and track in a smart environment. IEEE Trans Knowl Data Eng 23(4):527–539

  Article  Google Scholar 

• Remagnino P, Foresti G, Ellis T (2005) Ambient intelligence: a novel paradigm. Springer, Berlin

  Book  Google Scholar 

• Robben S, Pol M, Ben Krose B (2014) Longitudinal ambient sensor monitoring for functional health assessments: a case study. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing: adjunct publication, UbiComp ’14 Adjunct, pp 1209–1216

• Robben S, Aicha AN, Ben Krose B (2016) Measuring regularity in daily behavior for the purpose of detecting alzheimer. In: Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth ’16, pp 97–100

• Rowan J, Mynatt ED (2005) Digital family portrait field trial: Support for aging in place. In: van der Veer GC, Gale C (eds) Proceedings of the SIGCHI conference on human factors in computing systems, ACM, New York, USA, pp 521–530

• Tapia EM, Intille SS, Haskell W, Larson K, Wright J, King A, Friedman R (2007) Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: 2007 11th IEEE International Symposium on Wearable Computers, IEEE, pp 37–40

• Tunstall (2015) Tunstall healthcare. http://www.tunstall.es/en/index.htm. Accessed 1 Feb 2018

• Vail DL, Veloso MM, Lafferty JD (2007) Conditional random fields for activity recognition. In: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, ACM, p 235

• van Kasteren T, Noulas A, Englebienne G, Krose B (2008) Accurate activity recognition in a home setting. In: Proceedings of the 10th international conference on Ubiquitous computing, ACM, pp 1–9

• Virone G, Alwan M, Dalal S, Kell SW, Turner B, Stankovic JA, Felder R (2008) Behavioral patterns of older adults in assisted living. IEEE Trans Inf Technol Biomed 12(3):387–398

  Article  Google Scholar 

• Viterion (2015) Viterion telehealthcare. http://www.viterion.com/. Accessed 1 Feb 2018

• Wang L, Gu T, Tao X, Chen H, Lu J (2011) Recognizing multi-user activities using wearable sensors in a smart home. Pervasive Mob Comput 7(3):287–298

  Article  Google Scholar 

• Wang L, Gu T, Tao X, Lu J (2012) A hierarchical approach to real-time activity recognition in body sensor networks. Pervasive Mob Comput 8(1):115–130

  Article  Google Scholar 

• Washington State Univ (2015) Casas smart home project. http://ailab.wsu.edu/casas/. Accessed 1 Feb 2018

• Woznowski P, Burrows A, Diethe T, Fafoutis X, Hall J, Hannuna S, Camplani M, Twomey N, Kozlowski M, Tan B, Zhu N, Elsts A, Vafeas A, Paiement A, Tao L, Mirmehdi M, Burghardt T, Damen D, Flach P, Piechocki R, Craddock I, Oikonomou G (2017) SPHERE: a sensor platform for healthcare in a residential environment. Springer International Publishing, Berlin, pp 315–333

  Google Scholar 

• Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou ZH, Steinbach M, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14:1–37

  Article  Google Scholar 

• Zheng H, Wang H, Black N (2008) Human activity detection in smart home environment with self-adaptive neural networks. In: Networking, sensing and control, 2008. ICNSC 2008. IEEE International Conference on, IEEE, pp 1505–1510

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