Ambient Assisted Living [Guest editors' introduction]

The guest editors of this special issue on ambient assisted living discuss the field in general and the articles they have selected to represent it in particular.

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[2]  Albrecht Schmidt,et al.  Ubiquitous computing - computing in context , 2003 .

[3]  Alois Ferscha,et al.  Real-Time Transfer and Evaluation of Activity Recognition Capabilities in an Opportunistic System , 2011 .

[4]  Dorothy Ndedi Monekosso,et al.  Behavior Analysis for Assisted Living , 2010, IEEE Transactions on Automation Science and Engineering.

[5]  Diane J. Cook,et al.  Infrastructure-assisted smartphone-based ADL recognition in multi-inhabitant smart environments , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[6]  Amy Loutfi,et al.  Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges , 2013, Sensors.

[7]  Shuai Tao,et al.  Multiperson Locating and Their Soft Tracking in a Binary Infrared Sensor Network , 2015, IEEE Transactions on Human-Machine Systems.

[8]  Maureen Schmitter-Edgecombe,et al.  Prompting technologies: A comparison of time-based and context-aware transition-based prompting. , 2015, Technology and health care : official journal of the European Society for Engineering and Medicine.

[9]  Shyamal Patel,et al.  A review of wearable sensors and systems with application in rehabilitation , 2012, Journal of NeuroEngineering and Rehabilitation.

[10]  Bruno Ando,et al.  A Haptic Solution to Assist Visually Impaired in Mobility Tasks , 2015, IEEE Transactions on Human-Machine Systems.

[11]  Gwenn Englebienne,et al.  An activity monitoring system for elderly care using generative and discriminative models , 2010, Personal and Ubiquitous Computing.

[12]  Maureen Schmitter-Edgecombe,et al.  Automated Cognitive Health Assessment From Smart Home-Based Behavior Data , 2016, IEEE Journal of Biomedical and Health Informatics.

[13]  Jennifer G. Dy,et al.  Home monitoring of patients with Parkinson's disease via wearable technology and a web-based application , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[14]  M. Pavel,et al.  Intelligent Systems For Assessing Aging Changes: home-based, unobtrusive, and continuous assessment of aging. , 2011, The journals of gerontology. Series B, Psychological sciences and social sciences.

[15]  Marjorie Skubic,et al.  Passive in-home measurement of stride-to-stride gait variability comparing vision and Kinect sensing , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Athanasios V. Vasilakos,et al.  A Survey on Ambient Intelligence in Healthcare , 2013, Proceedings of the IEEE.

[17]  Diane J. Cook,et al.  Data-Driven Activity Prediction: Algorithms, Evaluation Methodology, and Applications , 2015, KDD.

[18]  Wei Tu,et al.  Activity Sequence-Based Indoor Pedestrian Localization Using Smartphones , 2015, IEEE Transactions on Human-Machine Systems.

[19]  Emmanuel,et al.  Activity recognition in the home setting using simple and ubiquitous sensors , 2003 .

[20]  Andrea Mannini,et al.  Activity recognition using a single accelerometer placed at the wrist or ankle. , 2013, Medicine and science in sports and exercise.

[21]  Jesse Hoey,et al.  Sensor-Based Activity Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[22]  Jay Lundell,et al.  Deploying wide-scale in-home assessment technology , 2008 .

[23]  Ramona Rednic,et al.  Wireless sensor networks for activity monitoring in safety c ritical applications , 2009 .

[24]  Francisco Javier Ferrández Pastor,et al.  A Vision-Based System for Intelligent Monitoring: Human Behaviour Analysis and Privacy by Context , 2014, Sensors.

[25]  Martin Klepal,et al.  Mobile Phone-Based Displacement Estimation for Opportunistic Localisation Systems , 2009, 2009 Third International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies.

[26]  Diane J. Cook,et al.  Human Activity Recognition and Pattern Discovery , 2010, IEEE Pervasive Computing.

[27]  Alex Mihailidis,et al.  The use of computer vision in an intelligent environment to support aging-in-place, safety, and independence in the home , 2004, IEEE Transactions on Information Technology in Biomedicine.

[28]  Simon Hay,et al.  Bluetooth Tracking without Discoverability , 2009, LoCA.

[29]  Alexandros André Chaaraoui,et al.  A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living , 2012, Expert Syst. Appl..

[30]  Martin Klepal,et al.  Adaptive Motion Model for a Smart Phone Based Opportunistic Localization System , 2009, MELT.

[31]  Misha Pavel,et al.  Unobtrusive in-home assessment by means of everyday computer mouse usage , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Dimitrios Makris,et al.  Fall detection system using Kinect’s infrared sensor , 2014, Journal of Real-Time Image Processing.

[33]  T. Hayes,et al.  One walk a year to 1000 within a year: continuous in-home unobtrusive gait assessment of older adults. , 2012, Gait & posture.

[34]  Diane J. Cook,et al.  Activity recognition on streaming sensor data , 2014, Pervasive Mob. Comput..

[35]  James M. Rehg,et al.  A Scalable Approach to Activity Recognition based on Object Use , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[36]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[37]  Fabien Cardinaux,et al.  Video based technology for ambient assisted living: A review of the literature , 2011, J. Ambient Intell. Smart Environ..