User-Centered Rating of Well-Being in Older Adults

Predictions estimate that the future will entail several devices related to IoT (Internet of Things). Most of these will be present in our homes, collecting useful information and triggering informed actions. Much work has been done on collecting data and triggering such devices. However, there is not much work on how to make use of such information to measure the well-being of a person. In the context of older adults, it would be useful to define a means to estimate their well-being, provide them some feedback, and eventually share it with a family member or caregiver. This article emphasizes how to measure well-being through a user-centered Personal Well-being Rating (PWR). Although the proposed rating is idealized as a general equation, our case study is mainly centered on older adults. These are undeniably a group of society that can enrich their lives, by integrating possible solutions implemented considering the PWR. This interpretation opens the door to the development of future interfaces, which can be supported by an explicit way of measuring the well-being of someone inside a home.

[1]  Jan Treur,et al.  A computational model based on Gross’ emotion regulation theory , 2010, Cognitive Systems Research.

[2]  Venu Govindaraju,et al.  Real-time Automatic Deceit Detection from Involuntary Facial Expressions , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Debotosh Bhattacharjee,et al.  A Novel Approach for Human Action Recognition from Silhouette Images , 2015, ArXiv.

[4]  Abdulsalam Yassine,et al.  Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications , 2017, IEEE Access.

[5]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Martial Hebert,et al.  Spatio-temporal Shape and Flow Correlation for Action Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Edith Maier,et al.  Ambient Lighting Assistance for an Ageing Population , 2009 .

[8]  Myounghoon Jeon,et al.  Emotions and Affect in Human Factors and Human–Computer Interaction: Taxonomy, Theories, Approaches, and Methods , 2017 .

[9]  Jan Treur,et al.  Displaying and Regulating Different Social Response Patterns: A Computational Agent Model , 2014, Cognitive Computation.

[10]  Gwen Littlewort,et al.  Recognizing facial expression: machine learning and application to spontaneous behavior , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  David R Bassett,et al.  2011 Compendium of Physical Activities: a second update of codes and MET values. , 2011, Medicine and science in sports and exercise.

[12]  Caifeng Shan,et al.  Smile detection by boosting pixel differences , 2012, IEEE Transactions on Image Processing.

[13]  E. Diener,et al.  Review of the Satisfaction with Life Scale , 1993 .

[14]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[15]  L. Fleischer Telling Lies Clues To Deceit In The Marketplace Politics And Marriage , 2016 .

[16]  Marius Prelipceanu,et al.  An Intelligent Assistive Tool Using Exergaming and Response Surface Methodology for Patients With Brain Disorders , 2019, IEEE Access.

[17]  Tibor Bosse,et al.  Learning Emotion Regulation Strategies: A Cognitive Agent Model , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[18]  Alan Chalmers,et al.  Levels of realism: from virtual reality to real virtuality , 2008, SCCG.

[19]  C. Darwin The Expression of the Emotions in Man and Animals , .

[20]  Cristina Urdiales,et al.  Supported human autonomy for recovery and enhancement of cognitive and motor abilities using agent technologies , 2007 .

[21]  Bappaditya Mandal,et al.  Spontaneous Versus Posed Smiles - Can We Tell the Difference? , 2016, CVIP.

[22]  Eric Campo,et al.  A review of smart homes - Present state and future challenges , 2008, Comput. Methods Programs Biomed..

[23]  Javier Bajo,et al.  Ambient Agents: Embedded Agents for Remote Control and Monitoring Using the PANGEA Platform , 2014, Sensors.

[24]  A. Jette,et al.  The Physical Activity Scale for the Elderly (PASE): development and evaluation. , 1993, Journal of clinical epidemiology.

[25]  Arsénio Reis,et al.  Autonomous systems to support social activity of elderly people a prospective approach to a system design , 2016, 2016 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW).

[26]  Julien Bidot,et al.  Artificial Intelligence Planning for Ambient Environments , 2011 .

[27]  Zhibo Pang,et al.  Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges , 2017, Sensors.

[28]  José Manuel Pastor,et al.  Software Architecture for Smart Emotion Recognition and Regulation of the Ageing Adult , 2016, Cognitive Computation.

[29]  Yuchae Jung Hybrid-Aware Model for Senior Wellness Service in Smart Home , 2017, Sensors.

[30]  Jer-Vui Lee,et al.  Smart Elderly Home Monitoring System with an Android Phone , 2013 .

[31]  Albert Ali Salah,et al.  Recognition of Genuine Smiles , 2015, IEEE Transactions on Multimedia.

[32]  A. King,et al.  Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association. , 2007, Medicine and science in sports and exercise.

[33]  M. Jetté,et al.  Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity , 1990, Clinical cardiology.

[34]  G. Dewsbury The Social and Psychological Aspects of Smart Home Technolgy within the Care Sector , 2001 .

[35]  Maja Pantic,et al.  Spontaneous vs. posed facial behavior: automatic analysis of brow actions , 2006, ICMI '06.

[36]  Nicu Sebe,et al.  Emotion Recognition Based on Joint Visual and Audio Cues , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[37]  M. Kruger,et al.  Smile Intensity in Photographs Predicts Longevity , 2010, Psychological science.

[38]  António Pereira,et al.  Real-Time Low-Cost Active and Assisted Living for the Elderly , 2019, ISAmI.

[39]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  KeeHyun Park,et al.  An IoT System for Remote Monitoring of Patients at Home , 2017 .

[41]  Florentino Fernández Riverola,et al.  A Mobile Virtual Butler to Bridge the Gap between Users and Ambient Assisted Living: A Smart Home Case Study , 2014, Sensors.

[42]  Tapas Mondal,et al.  Wearable Sensors for Remote Health Monitoring , 2017, Sensors.

[43]  Antonio Fernández-Caballero,et al.  A survey of video datasets for human action and activity recognition , 2013, Comput. Vis. Image Underst..

[44]  Hatice Gunes,et al.  How to distinguish posed from spontaneous smiles using geometric features , 2007, ICMI '07.

[45]  Jeffrey F. Cohn,et al.  The Timing of Facial Motion in posed and Spontaneous Smiles , 2003, Int. J. Wavelets Multiresolution Inf. Process..

[46]  Pinar Duygulu Sahin,et al.  Human Action Recognition Using Distribution of Oriented Rectangular Patches , 2007, Workshop on Human Motion.

[47]  V. Froelicher,et al.  Nomogram based on metabolic equivalents and age for assessing aerobic exercise capacity in men. , 1993, Journal of the American College of Cardiology.

[48]  Iván Pau,et al.  The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development , 2015, Sensors.

[49]  Philippe Verduyn,et al.  Which emotions last longest and why: The role of event importance and rumination , 2015 .

[50]  J. A. Herrick Aristotle on Rhetoric , 2017 .

[51]  António Pereira,et al.  Fall Detection on Ambient Assisted Living using a Wireless Sensor Network , 2013 .

[52]  Debotosh Bhattacharjee,et al.  Robust Human Action Recognition Using AREI Features and Trajectory Analysis from Silhouette Image Sequence , 2019 .

[53]  R. K. Rayudu,et al.  Wellness determination of inhabitant based on daily activity behaviour in real-time monitoring using Sensor Networks , 2011, 2011 Fifth International Conference on Sensing Technology.

[54]  Hong Liu,et al.  Comparison of methods for smile deceit detection by training AU6 and AU12 simultaneously , 2012, 2012 19th IEEE International Conference on Image Processing.

[55]  Zhining Liao,et al.  A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare , 2017 .

[56]  Hong Liu,et al.  Spontaneous versus posed smile recognition using discriminative local spatial-temporal descriptors , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[57]  P. Ekman,et al.  Felt, false, and miserable smiles , 1982 .

[58]  Wanqing Li,et al.  Action recognition based on a bag of 3D points , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[59]  José Manuel Pastor,et al.  Smart environment architecture for emotion detection and regulation , 2016, J. Biomed. Informatics.

[60]  Rémi Ronfard,et al.  A survey of vision-based methods for action representation, segmentation and recognition , 2011, Comput. Vis. Image Underst..

[61]  Gourab Sen Gupta,et al.  Elder Care Based on Cognitive Sensor Network , 2011, IEEE Sensors Journal.

[62]  Mohammad Mehedi Hassan,et al.  Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care , 2017, Sensors.

[63]  E. Diener,et al.  Subjective well-being. The science of happiness and a proposal for a national index. , 2000, The American psychologist.

[64]  P. Petta,et al.  Computational models of emotion , 2010 .

[65]  Agus Harjoko,et al.  Fake smile detection using linear support vector machine , 2015, 2015 International Conference on Data and Software Engineering (ICoDSE).

[66]  David A. van Leeuwen,et al.  Automatic discrimination between laughter and speech , 2007, Speech Commun..

[67]  David H. Barlow,et al.  Incorporating Emotion Regulation into Conceptualizations and Treatments of Anxiety and Mood Disorders. , 2007 .

[68]  L. Varga,et al.  K4Care: Knowledge-Based Homecare e-Services for an Ageing Europe , 2007 .

[69]  Carlos Carrascosa,et al.  Emotions Detection on an Ambient Intelligent System Using Wearable Devices , 2019, AfCAI.

[70]  Ruijiao Li,et al.  Cognitive assisted living ambient system: a survey , 2015, Digit. Commun. Networks.

[71]  Zhihong Zeng,et al.  A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[72]  Subhas Chandra Mukhopadhyay,et al.  Wearable Sensors for Human Activity Monitoring: A Review , 2015, IEEE Sensors Journal.

[73]  H. Buchanan,et al.  Development of a computerised dental anxiety scale for children: validation and reliability , 2005, British Dental Journal.

[74]  Jing Zhang,et al.  Action Recognition From Depth Maps Using Deep Convolutional Neural Networks , 2016, IEEE Transactions on Human-Machine Systems.

[75]  G. Finley How much does it hurt? Pediatric pain measurement for doctors, nurses, and parents , 2001, Canadian journal of anaesthesia = Journal canadien d'anesthesie.

[76]  P. Ekman,et al.  Facial action coding system: a technique for the measurement of facial movement , 1978 .

[77]  Matthew J. Hertenstein,et al.  Smile intensity in photographs predicts divorce later in life , 2009 .

[78]  P. Ekman,et al.  Pan-Cultural Elements in Facial Displays of Emotion , 1969, Science.

[79]  Debajyoti Pal,et al.  Analyzing the Elderly Users’ Adoption of Smart-Home Services , 2018, IEEE Access.

[80]  José Manuel Pastor,et al.  Electrodermal Activity Sensor for Classification of Calm/Distress Condition , 2017, Sensors.

[81]  Subhas Mukhopadhyay,et al.  Intelligent Sensing Systems for Measuring Wellness Indices of the Daily Activities for the Elderly , 2012, 2012 Eighth International Conference on Intelligent Environments.

[82]  Antonio Fernández-Caballero,et al.  Facial expression recognition in ageing adults: from lab to ambient assisted living , 2017, J. Ambient Intell. Humaniz. Comput..

[83]  António Pereira,et al.  eServices - Service Platform for Pervasive Elderly Care , 2015, ISAmI.

[84]  Jing Xiao,et al.  Automatic analysis and recognition of brow actions and head motion in spontaneous facial behavior , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[85]  Gwen Littlewort,et al.  Automatic Recognition of Facial Actions in Spontaneous Expressions , 2006, J. Multim..

[86]  Jaime Lloret Mauri,et al.  A smart communication architecture for ambient assisted living , 2015, IEEE Communications Magazine.

[87]  Javier Bajo,et al.  Monitoring and Detection Platform to Prevent Anomalous Situations in Home Care , 2014, Sensors.

[88]  Yu Qiao,et al.  Action Recognition with Stacked Fisher Vectors , 2014, ECCV.

[89]  Robert Biswas-Diener,et al.  New Measures of Well-Being , 2009 .

[90]  Itziar G. Alonso-González,et al.  A Low Cost Wireless Acoustic Sensor for Ambient Assisted Living Systems , 2017 .

[91]  Shuang Wang,et al.  A Review on Human Activity Recognition Using Vision-Based Method , 2017, Journal of healthcare engineering.

[92]  Albert Ali Salah,et al.  Eyes do not lie: spontaneous versus posed smiles , 2010, ACM Multimedia.

[93]  Suman K. Mitra,et al.  Human Action Recognition Using DFT , 2011, 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics.

[94]  Sydney Katz Assessing Self‐maintenance: Activities of Daily Living, Mobility, and Instrumental Activities of Daily Living , 1983, Journal of the American Geriatrics Society.

[95]  Miguel Hernando,et al.  Home Camera-Based Fall Detection System for the Elderly , 2017, Sensors.

[96]  Simon Lucey,et al.  Investigating Spontaneous Facial Action Recognition through AAM Representations of the Face , 2007 .

[97]  Bhiksha Raj,et al.  Beyond Gaussian Pyramid: Multi-skip Feature Stacking for action recognition , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[98]  Xiaodong Yang,et al.  Super Normal Vector for Human Activity Recognition with Depth Cameras , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[99]  Diego López-de-Ipiña,et al.  Integration of Multisensor Hybrid Reasoners to Support Personal Autonomy in the Smart Home , 2014, Sensors.

[100]  A. Mehrabian Framework for a comprehensive description and measurement of emotional states. , 1995, Genetic, social, and general psychology monographs.

[102]  Ciprian Dobre,et al.  Ambient Assisted Living and Enhanced Living Environments: Principles, Technologies and Control , 2016 .

[103]  V. Murthy,et al.  Smile Detection for User Interfaces , 2014 .

[104]  Gwen Littlewort,et al.  A Prototype for Automatic Recognition of Spontaneous Facial Actions , 2002, NIPS.

[105]  Fernando De la Torre,et al.  Estimating smile intensity: A better way , 2015, Pattern Recognit. Lett..

[106]  Charalampos Konstantopoulos,et al.  Hands-On Experiences in Deploying Cost-Effective Ambient-Assisted Living Systems , 2015, Sensors.

[107]  C. Ellison Spiritual Well-Being: Conceptualization and Measurement , 1983 .

[108]  B E Ainsworth,et al.  Compendium of physical activities: an update of activity codes and MET intensities. , 2000, Medicine and science in sports and exercise.

[109]  Gwen Littlewort,et al.  Toward Practical Smile Detection , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[110]  Samih Eisa,et al.  A Behaviour Monitoring System (BMS) for Ambient Assisted Living , 2017, Sensors.

[111]  Eva Hudlicka,et al.  From Habits to Standards: Towards Systematic Design of Emotion Models and Affective Architectures , 2014, Emotion Modeling.

[112]  Maja Pantic,et al.  Audiovisual discrimination between laughter and speech , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[113]  Rama Chellappa,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Matching Shape Sequences in Video with Applications in Human Movement Analysis. Ieee Transactions on Pattern Analysis and Machine Intelligence 2 , 2022 .

[114]  Emanuele Frontoni,et al.  HDOMO: Smart Sensor Integration for an Active and Independent Longevity of the Elderly , 2017, Sensors.

[115]  M. Bartlett,et al.  Machine Analysis of Facial Expressions , 2007 .

[116]  Abdesselam Bouzerdoum,et al.  Automatic Recognition of Smiling and Neutral Facial Expressions , 2010, 2010 International Conference on Digital Image Computing: Techniques and Applications.

[117]  Ashish Kapoor,et al.  Automatic prediction of frustration , 2007, Int. J. Hum. Comput. Stud..

[118]  Olac Fuentes,et al.  Color Analysis of Facial Skin: Detection of Emotional State , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[119]  Nuno M. M. Rodrigues,et al.  A User-Centred Well-Being Home for the Elderly , 2018 .

[120]  Ronald Poppe,et al.  A survey on vision-based human action recognition , 2010, Image Vis. Comput..

[121]  Kostas Karpouzis,et al.  Emotion recognition through facial expression analysis based on a neurofuzzy network , 2005, Neural Networks.

[122]  Yuxiao Hu,et al.  Spontaneous Emotional Facial Expression Detection , 2006, J. Multim..

[123]  Tibor Bosse,et al.  Incorporating Emotion Regulation into Virtual Stories , 2007, IVA.

[124]  Frank M. Andrews,et al.  Social Indicators of Well-Being , 1976 .

[125]  Yves Schutz,et al.  Metabolic equivalent: one size does not fit all. , 2005, Journal of applied physiology.

[126]  Roozbeh Jafari,et al.  A Survey on Smart Homes for Aging in Place: Toward Solutions to the Specific Needs of the Elderly , 2018, IEEE Signal Processing Magazine.

[127]  Fernando De la Torre,et al.  Facial Expression Analysis , 2011, Visual Analysis of Humans.

[128]  Debajyoti Pal,et al.  Smart Homes and Quality of Life for the Elderly: Perspective of Competing Models , 2018, IEEE Access.

[129]  Eftim Zdravevski,et al.  A survey of Ambient Assisted Living systems: Challenges and opportunities , 2016, 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP).

[130]  Tsuhan Chen,et al.  The painful face - Pain expression recognition using active appearance models , 2009, Image Vis. Comput..

[131]  M. Lawton,et al.  Assessment of older people: self-maintaining and instrumental activities of daily living. , 1969, The Gerontologist.

[132]  Yuri L. Hanin,et al.  Emotions in Sport: Current Issues and Perspectives , 2012 .

[133]  T. Shaw Darwin on the Expression of the Emotions in Man and Animals , 1873 .

[134]  Svetha Venkatesh,et al.  Activity recognition and abnormality detection with the switching hidden semi-Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[135]  Arsénio Reis,et al.  Using Emotion Recognition in Intelligent Interface Design for Elderly Care , 2018, WorldCIST.

[136]  R. Plutchik A GENERAL PSYCHOEVOLUTIONARY THEORY OF EMOTION , 1980 .

[137]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[138]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.

[139]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[140]  Shigehiro Oishi,et al.  Intensity of Smiling in Facebook Photos Predicts Future Life Satisfaction , 2012 .

[141]  Jan Treur,et al.  An agent-based model for integrated emotion regulation and contagion in socially affected decision making , 2015, BICA 2015.

[142]  Mubarak Shah,et al.  Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[143]  B. Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[144]  João Barroso,et al.  Low Cost Smart Homes for Elders , 2017, HCI.