A smart mirror to promote a healthy lifestyle

ICT solutions to foster behavioural change have been shown to be effective in implementing primary prevention in terms of a healthy lifestyle. Primary prevention is the most viable approach to reduce the socio-economic burden of chronic and widespread diseases, such as cardiovascular and metabolic diseases. In this paper, we present a novel multisensory device, the Wize Mirror, which is under development in the EU FP7 Project SEMEOTICONS. The Wize Mirror detects and monitors over time semeiotic face signs related to cardio-metabolic risk, and encourages users to reduce their risk by improving their lifestyle.

[1]  E. Balas,et al.  Healthcare via cell phones: a systematic review. , 2009, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[2]  D. Karnofsky.  The clinical evaluation of chemotherapeutic agents in cancer , 1949 .

[3]  D. Feeny,et al.  Multiattribute utility function for a comprehensive health status classification system. Health Utilities Index Mark 2. , 1996, Medical care.

[4]  Audie A Atienza,et al.  Mobile health technology evaluation: the mHealth evidence workshop. , 2013, American journal of preventive medicine.

[5]  Eirini Christinaki,et al.  Facial Signs and Psycho-physical Status Estimation for Well-being Assessment , 2014, HEALTHINF.

[6]  Pradeep K. Atrey,et al.  Smart mirror for ambient home environment , 2007 .

[7]  Marcus Larsson,et al.  Estimating skin blood saturation by selecting a subset of hyperspectral imaging data , 2015, Photonics West - Biomedical Optics.

[8]  P Tugwell,et al.  The MACTAR Patient Preference Disability Questionnaire--an individualized functional priority approach for assessing improvement in physical disability in clinical trials in rheumatoid arthritis. , 1987, The Journal of rheumatology.

[9]  Mike Thelwall,et al.  Online Interventions for Social Marketing Health Behavior Change Campaigns: A Meta-Analysis of Psychological Architectures and Adherence Factors , 2011, Journal of medical Internet research.

[10]  N. Saranummi,et al.  Rethinking Health: ICT-Enabled Services to Empower People to Manage Their Health , 2011, IEEE Reviews in Biomedical Engineering.

[11]  A. Barak,et al.  A Comprehensive Review and a Meta-Analysis of the Effectiveness of Internet-Based Psychotherapeutic Interventions , 2008 .

[12]  Jacquelynne S Eccles,et al.  Rebranding exercise: closing the gap between values and behavior , 2011, The international journal of behavioral nutrition and physical activity.

[13]  Britt Klein,et al.  Positive psychology and the internet: A mental health opportunity , 2010 .

[14]  Harri Oinas-Kukkonen,et al.  Behavior Change Support Systems: A Research Model and Agenda , 2010, PERSUASIVE.

[15]  C. Heneghan,et al.  Sleep/wake measurement using a non‐contact biomotion sensor , 2011, Journal of sleep research.

[16]  S. Michie,et al.  Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis, Use of Behavior Change Techniques, and Mode of Delivery on Efficacy , 2010, Journal of medical Internet research.

[17]  J. Fries,et al.  The dimensions of health outcomes: the health assessment questionnaire, disability and pain scales. , 1982, The Journal of rheumatology.

[18]  Christoph U. Lehmann,et al.  Consumer health informatics: results of a systematic evidence review and evidence based recommendations , 2011, Translational behavioral medicine.

[19]  Michelle G. Craske,et al.  Computer Therapy for the Anxiety and Depressive Disorders Is Effective, Acceptable and Practical Health Care: A Meta-Analysis , 2010, PloS one.

[20]  Hermie Hermens,et al.  A framework for the comparison of mobile patient monitoring systems , 2012, J. Biomed. Informatics.

[21]  Sara Colantonio,et al.  Morphological Analysis of 3D Faces for Weight Gain Assessment , 2015, 3DOR@Eurographics.

[22]  Sara Colantonio,et al.  Moving Medical Semeiotics to the Digital Realm - SEMEOTICONS Approach to Face Signs of Cardiometabolic Risk , 2014, HEALTHINF.

[23]  D. Feeny,et al.  The Health Utilities Index (HUI®): concepts, measurement properties and applications , 2003, Health and quality of life outcomes.

[24]  Aly A. Farag,et al.  Multiresolution Approach for Non-Contact Measurements of Arterial Pulse using Thermal Imaging , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[25]  Hanna Karlsson,et al.  Microcirculation assessment using an individualized model for diffuse reflectance spectroscopy and conventional laser Doppler flowmetry , 2014, Journal of biomedical optics.

[26]  Ning Yang,et al.  A non-contact health monitoring model based on the Internet of things , 2012, 2012 8th International Conference on Natural Computation.

[27]  J. Lee,et al.  An Index to Measure Health Status , 2008 .

[28]  W. Nilsen,et al.  Health behavior models in the age of mobile interventions: are our theories up to the task? , 2011, Translational behavioral medicine.

[29]  J. Prochaska,et al.  A meta-analysis of computer-tailored interventions for health behavior change. , 2010, Preventive medicine.

[30]  Upkar Varshney,et al.  A model for improving quality of decisions in mobile health , 2014, Decis. Support Syst..

[31]  Pamela M. Kato,et al.  Video Games in Health Care: Closing the Gap , 2010 .

[32]  B. Fjeldsoe,et al.  Behavior change interventions delivered by mobile telephone short-message service. , 2009, American journal of preventive medicine.

[33]  Daniel McDuff,et al.  A medical mirror for non-contact health monitoring , 2011, SIGGRAPH '11.

[34]  George W. Torrance,et al.  Application of Multi-Attribute Utility Theory to Measure Social Preferences for Health States , 1982, Oper. Res..

[35]  O. Steinbrocker,et al.  Therapeutic criteria in rheumatoid arthritis. , 1949, Journal of the American Medical Association.

[36]  Giuseppe De Pietro,et al.  A pattern-based knowledge editing system for building clinical Decision Support Systems , 2012, Knowl. Based Syst..