Ambient Intelligence in Assisted Living: Enable Elderly People to Handle Future Interfaces

By T. Kleinberger, Martin Becker, E. Ras, Andreas Holzinger, Paul Müller. Interacción, 2007

388 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Demography

View Paper: https://www.semanticscholar.org/paper/5289644c10a65cc5a1683b1932e0d4feb707417f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Demography

Francisco Flórez-Revuelta

Citations in the corpus

Ambient Assisted Living Healthcare Frameworks, Platforms, Standards, and Quality Attributes (2014, background): “They emphasize that the disabled and elderly should be trained to use future technology interfaces, so that they might be capable of using personal healthcare devices without technical support [74].”
Elderly activities recognition and classification for applications in assisted living (2013, background): “Usually they are closed, stand-alone systems with limited ability to describe actual situations, often too difficult for elderly people to use and ineffective in emergency situations (Kleinberger et al., 2007).”
Seven HCI Grand Challenges (2019, background): “…and modelling ● Broad scope of the concepts extending beyond the individual to community well-being and planet goodness, as well as other concepts, such as human rights and fair labor population and the consequent rise of chronic diseases (Kleinberger, Becker, Ras, Holzinger, & Müller, 2007).”
From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices (2016, background): “The recognition of the ADLs [81,110,131,141,142] is very important in systems developed to support and monitor elderly people or people with some disease or impairment [143,144].”
A systematic review of the smart home literature: A user perspective (2019, background): “Smart homes acceptance and adoption 9 Smart home technology acceptance factors [1, 5, 7, 9, 13, 14, 27, 46, 47, 52, 54, 61, 68, 73, 83, 85, 87, 103, 118, 119, 130, 133, 138, 143] 24”