A Smart Kitchen for Ambient Assisted Living

The kitchen environment is one of the scenarios in the home where users can benefit from Ambient Assisted Living (AAL) applications. Moreover, it is the place where old people suffer from most domestic injuries. This paper presents a novel design, implementation and assessment of a Smart Kitchen which provides Ambient Assisted Living services; a smart environment that increases elderly and disabled people's autonomy in their kitchen-related activities through context and user awareness, appropriate user interaction and artificial intelligence. It is based on a modular architecture which integrates a wide variety of home technology (household appliances, sensors, user interfaces, etc.) and associated communication standards and media (power line, radio frequency, infrared and cabled). Its software architecture is based on the Open Services Gateway initiative (OSGi), which allows building a complex system composed of small modules, each one providing the specific functionalities required, and can be easily scaled to meet our needs. The system has been evaluated by a large number of real users (63) and carers (31) in two living labs in Spain and UK. Results show a large potential of system functionalities combined with good usability and physical, sensory and cognitive accessibility.

[1]  Dieter Fox,et al.  Fine-grained kitchen activity recognition using RGB-D , 2012, UbiComp.

[2]  K. Shadan,et al.  Available online: , 2012 .

[3]  Alan Cooper,et al.  The Inmates are Running the Asylum , 1999, Software-Ergonomie.

[4]  Pei-Yu Chi,et al.  A Smart Kitchen for Nutrition-Aware Cooking , 2010, IEEE Pervasive Computing.

[5]  Gerhard Goos,et al.  Ambient Intelligence , 2015, Lecture Notes in Computer Science.

[6]  Jorge L. Falcó,et al.  AmI and Deployment Considerations in AAL Services Provision for Elderly Independent Living: The MonAMI Project , 2013, Sensors.

[7]  Álvaro Marco,et al.  Common OSGi Interface for Ambient Assisted Living Scenarios , 2009, BMI Book.

[8]  Leonardo Bonanni,et al.  CounterIntelligence: Augmented Reality Kitchen , 2005 .

[9]  James A Lenker,et al.  A Review of Conceptual Models for Assistive Technology Outcomes Research and Practice , 2003, Assistive technology : the official journal of RESNA.

[10]  Richard Tynan,et al.  Towards evolutionary ambient assisted living systems , 2010, J. Ambient Intell. Humaniz. Comput..

[11]  J. Jutai,et al.  Development of a scale to measure the psychosocial impact of assistive devices: lessons learned and the road ahead , 2002, Disability and rehabilitation.

[12]  M. Scherer,et al.  Matching Person & Technology (MPT) assessment process , 2002 .

[13]  Sahin Albayrak,et al.  Behavior-Sensitive User Interfaces for Smart Environments , 2009, HCI.

[14]  Wenjun Zhang,et al.  The design and implementation of home network system using OSGi compliant middleware , 2004, IEEE Trans. Consumer Electron..

[15]  T. Jick.  Mixing Qualitative and Quantitative Methods: Triangulation in Action. , 1979 .

[16]  Goldie Nejat,et al.  The Design of an Interactive Assistive Kitchen System , 2012 .

[17]  J. Combessie,et al.  A propos de méthodes : Effets d'optique, heuristique et objectivation , 1986 .

[18]  Boris Brandherm,et al.  Supporting Persons with Special Needs in their Daily Life in a Smart Home , 2011, 2011 Seventh International Conference on Intelligent Environments.

[19]  Álvaro Marco,et al.  Ethically Aware Design of a Location System for People with Dementia , 2006, ICCHP.

[20]  Joan A. Ballantine,et al.  Information systems/technology evaluation practices: evidence from UK organizations , 1996, J. Inf. Technol..

[21]  C. Wolfson,et al.  Reliability, validity, and applicability of the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST 2.0) for adults with multiple sclerosis , 2002, Disability and rehabilitation.

[22]  Rich Picking,et al.  Simplicity, consistency, universality and familiarity: applying ‘SCUF’ principles to technology for assisted living , 2009 .

[23]  Peter H. Rossi.  The war between the quals and the quants: Is a lasting peace possible? , 1994 .

[24]  P. J. Rigby,et al.  Toward a comprehensive evaluation of the impact of electronic aids to daily living: evaluation of consumer satisfaction , 2002, Disability and rehabilitation.

[25]  Jochen Frey.  Standardized Platform for Dual Reality Applications , 2011 .

[26]  S. C. Mukhopadhyay,et al.  Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly , 2012, IEEE Sensors Journal.

[27]  K. Howe,et al.  Getting over the Quantitative-Qualitative Debate , 1992, American Journal of Education.

[28]  Filippo Cavallo,et al.  AALIANCE Ambient Assisted Living Roadmap , 2010, Ambient Intelligence and Smart Environments.

[29]  Vic Grout,et al.  User Modelling in Ambient Intelligence for Elderly and Disabled People , 2008, ICCHP.

[30]  Albert M. Cook,et al.  Assistive Technologies: Principles and Practice , 1995 .

[31]  Drew Tiene,et al.  Adoption of Assistive Technology for computer access among college students with disabilities , 2002, Disability and rehabilitation.

[32]  M J Scherer,et al.  Measuring subjective quality of life following spinal cord injury: a validation study of the assistive technology device predisposition assessment. , 2001, Disability and rehabilitation.

[33]  Linpeng Huang,et al.  R-OSGi-based architecture of distributed smart home system , 2008, IEEE Transactions on Consumer Electronics.

[34]  Neil Gershenfeld,et al.  MIT-Media Lab , 1991, ICMC.

[35]  D. Morgan.  Focus groups for qualitative research. , 1988, Hospital guest relations report.

[36]  Juan A. Botía Blaya,et al.  An Approach for Representing Sensor Data to Validate Alerts in Ambient Assisted Living , 2012, Sensors.

[37]  Gülay Hasdoǧan,et al.  The role of user models in product design for assessment of user needs , 1996 .