Ambient Assisted Living: Systems Characterization

  • Alexandra QueirósEmail author
  • Milton Santos
  • Ana Dias
  • Nelson Pacheco da Rocha
Part of the Human–Computer Interaction Series book series (HCIS)


The AAL solutions, embedded in the older adults’ natural environment, should improve their functioning. For that, the developers should know and understand the needs of final users and their care providers (i.e. formal and informal care providers). It is necessary to go beyond the technology possibilities to reach the activities and participation for which final users need support, since the focus must be the person and not the technology.

Considering the diversity of AAL solutions, there is the need for a common language to facilitate the communication between different stakeholders (e.g. technology developers, care providers and end users) and the interoperability between different solutions and components.

The fundamental concepts of the International Classification of Functioning, Disability and Health (ICF) are related to the human functioning and performance in activities and participation. The existence of an international classification implies the existence of concepts able to provide a standardized language to all relevant stakeholders in the development and application of AAL solutions.

The next sections will present the type of AAL solutions developed in the past years, retrieved in the systematic review of the second chapter of this book, and will identify what type of activities and participation are supported by these systems. Afterwards, a classification of the AAL systems according to ICF concepts will be presented.


  1. Alemán JJ, Sanchez-Pi N, Bicharra Garcia AC (2015) Saferoute: an example of multi-sensoring tracking for the elderly using mobiles on ambient intelligence, pp 201–212Google Scholar
  2. Austin J et al (2016) A smart-home system to unobtrusively and continuously assess loneliness in older adults. IEEE J Transl Eng Health Med 4:2800311CrossRefGoogle Scholar
  3. Barham P, Carmien S, Garzo A (2015) The assistant project: creating a smartphone app to assist older people when travelling by public transportGoogle Scholar
  4. Boll S et al (2010) Development of a multimodal reminder system for older persons in their residential home. Inform Health Soc Care 35(3–4):104–124CrossRefGoogle Scholar
  5. Chang KC, Liu PK, Yu CS (2016) Design of real-time video streaming and object tracking system for home care servicesGoogle Scholar
  6. Chiang TC, Liang WH (2015) A context-aware interactive health care system based on ontology and fuzzy inference. J Med Syst 39(9):105CrossRefGoogle Scholar
  7. Ciampolini P et al (2016) The HELICOPTER project: continuous monitoring for early detection of age-related diseases. Gerontechnology 15:148sGoogle Scholar
  8. Coronato A, De Pietro G, Sannino G (2010) Middleware services for pervasive monitoring elderly and ill people in smart environmentsGoogle Scholar
  9. Costa R, Calçada L, Jesus D, Lima L, Lima LC (2014) AmI: monitoring physical activity. In: Ambient intelligence-software and applications. Springer, Cham, pp 233–239CrossRefGoogle Scholar
  10. Damaševičius R et al (2016) Human activity recognition in AAL environments using random projections. Comput Math Methods Med 2016:1–17MathSciNetCrossRefGoogle Scholar
  11. Deen MJ (2015) Information and communications technologies for elderly ubiquitous healthcare in a smart home. Pers Ubiquit Comput 19(3):573–599CrossRefGoogle Scholar
  12. Eichelberg M et al (2014) A technical platform for environments for ageing--lessons learned from three field studies. Inform Health Soc Care 39(3-4):272–293CrossRefGoogle Scholar
  13. Fahim M et al (2012) Daily life activity tracking application for smart homes using android smartphoneGoogle Scholar
  14. Foroughi H, Aski BS, Pourreza H (2008). Intelligent video surveillance for monitoring fall detection of elderly in home environments. In: 2008 11th international conference on computer and information technologyGoogle Scholar
  15. Jin A et al (2009) Performance evaluation of a tri-axial accelerometry-based respiration monitoring for ambient assisted living. In: Engineering in medicine and biology society, 2009. EMBC 2009. Annual international conference of the IEEE. IEEEGoogle Scholar
  16. Keegan S, O’Hare GM, O’Grady MJ (2008) Easishop: ambient intelligence assists everyday shopping. Inf Sci 178(3):588–611CrossRefGoogle Scholar
  17. Kieffer S, Lawson JYL, Macq B (2009) User-centered design and fast prototyping of an ambient assisted living system for elderly peopleGoogle Scholar
  18. Kuhn N et al (2009) Document management for elderly peopleGoogle Scholar
  19. Lopes IC, Vaidya B, Rodrigues JJ (2013) Towards an autonomous fall detection and alerting system on a mobile and pervasive environment. Telecommun Syst 52(4):2299–2310CrossRefGoogle Scholar
  20. Losardo A et al (2014) Getting out of the lab: a real-world AAL experience. Geron 13(2):256Google Scholar
  21. Maglogiannis I et al (2014) Fall detection using commodity smart watch and smart phone. In: IFIP advances in information and communication technology, pp 70–78CrossRefGoogle Scholar
  22. McCrindle RJ et al (2011) Wearable device to assist independent living. Int J Disabil Human Dev 10(4):349–354CrossRefGoogle Scholar
  23. Miori V, Russo D (2017) Improving life quality for the elderly through the Social Internet of Things (SIoT)Google Scholar
  24. Mlinac ME, Feng MC (2016) Assessment of activities of daily living, self-care, and independence. Arch Clin Neuropsychol 31(6):506–516CrossRefGoogle Scholar
  25. Nakagawa E et al (2016) Investigating recognition accuracy improvement by adding user’s acceleration data to location and power consumption-based in-home activity recognition systemGoogle Scholar
  26. Niemela M et al (2007) Supporting independent living of the elderly with mobile-centric ambient intelligence: user evaluation of three scenarios. In: Schiele B et al (eds) Ambient intelligence, proceedings, pp 91–107Google Scholar
  27. Passas N, Fried M, Manolakos ES (2012) PeerAssist: a P2P platform supporting virtual communities to assist independent living of senior citizens, pp 25–32Google Scholar
  28. Pensas H et al (2013) Building a client-server social network application for elders and safety netGoogle Scholar
  29. Queirós A et al (2012) Ambient assisted living technologies, systems and services: a systematic literature review. In: 2nd international living usability lab workshop on AAL latest solutions, trends and applications-AALGoogle Scholar
  30. Queirós A et al (2013a) A conceptual framework for the design and development of AAL services. In: Handbook of research on ICTs for human-centered healthcare and social care services. IGI Global, pp 568–586Google Scholar
  31. Queirós A, Alvarelhão J, Rocha N (2013b) Um Modelo Conceptual para o Ambient Assisted Living, in Laboratório Vivo de Usabilidade. In: Teixeira A, Queirós A, Rocha N (eds) Arc Publishing, pp 89–99Google Scholar
  32. Queirós A, Silva AG, Alvarelhão J, Teixeira A, Rocha NP (2015) Characterization and classification of existing ambient assisted living systems: a systematic literature review. In: Garcia NM, Rodrigues JJP (eds) Ambient assisted living. CRC Press, Boca Raton, pp 24–57CrossRefGoogle Scholar
  33. Research M (2018) Modelling ambient intelligence research lab [Cited 2018 June 2018]; Available from:
  34. Stucki RA, Urwyler P (2014) A web-based non-intrusive ambient system to measure and classify activities of daily living. J Med Internet Res 16(7):e175CrossRefGoogle Scholar
  35. Sun H et al (2009a) Promises and challenges of ambient assisted living systems. In: Information Technology: New Generations, 2009. ITNG’09. Sixth International Conference on IeeeGoogle Scholar
  36. Sun H et al (2009b) Promises and challenges of ambient assisted living systemsGoogle Scholar
  37. Suryadevara NK, Mukhopadhyay SC (2014) An intelligent system for continuous monitoring of wellness of an inhabitant for sustainable future. In: 2014 IEEE region 10 humanitarian technology conference (R10 HTC)Google Scholar
  38. Torkestani SS et al (2012) Infrared communication technology applied to indoor mobile healthcare monitoring system. Int J E-Health Med Commun (IJEHMC) 3(3):1–11CrossRefGoogle Scholar
  39. Ueda K et al (2015) Exploring accuracy-cost tradeoff in in-home living activity recognition based on power consumptions and user positions. In: 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computingGoogle Scholar
  40. World Health Organization (2001) International classification of functioning, disability and health: ICF. World Health Organization, GenevaGoogle Scholar
  41. Wolfgang Inninger FH, Nicole Wagner FH (2012) The iMo project individualised location-Based transportation services for elderly people in rural areasGoogle Scholar
  42. Zhou F et al (2010) Mobile personal health care system for patients with diabetes, pp 94–101Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Alexandra Queirós
    • 1
    • 2
    Email author
  • Milton Santos
    • 1
    • 2
  • Ana Dias
    • 3
    • 4
  • Nelson Pacheco da Rocha
    • 5
    • 2
  1. 1.Health Sciences SchoolUniversity of AveiroAveiroPortugal
  2. 2.Institute of Electronics and Telematics Engineering of Aveiro (IEETA)University of AveiroAveiroPortugal
  3. 3.Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT)University of AveiroAveiroPortugal
  4. 4.Governance, Competitiveness and Public Policies (GOVCOPP)University of AveiroAveiroPortugal
  5. 5.Medical Sciences DepartmentUniversity of AveiroAveiroPortugal

Personalised recommendations