Smart Environments and Social Robots for Age-Friendly Integrated Care Services

The world is facing major societal challenges because of an aging population that is putting increasing pressure on the sustainability of care. While demand for care and social services is steadily increasing, the supply is constrained by the decreasing workforce. The development of smart, physical, social and age-friendly environments is identified by World Health Organization (WHO) as a key intervention point for enabling older adults, enabling them to remain as much possible in their residences, delay institutionalization, and ultimately, improve quality of life. In this study, we survey smart environments, machine learning and robot assistive technologies that can offer support for the independent living of older adults and provide age-friendly care services. We describe two examples of integrated care services that are using assistive technologies in innovative ways to assess and deliver of timely interventions for polypharmacy management and for social and cognitive activity support in older adults. We describe the architectural views of these services, focusing on details about technology usage, end-user interaction flows and data models that are developed or enhanced to achieve the envisioned objective of healthier, safer, more independent and socially connected older people.

[1]  Zahir Tari,et al.  CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living , 2014, Future Gener. Comput. Syst..

[2]  H. Sapci,et al.  Innovative Assisted Living Tools, Remote Monitoring Technologies, Artificial Intelligence-Driven Solutions, and Robotic Systems for Aging Societies: Systematic Review , 2019, JMIR aging.

[3]  Min Hong,et al.  Sleep Monitoring System Using Kinect Sensor , 2015, Int. J. Distributed Sens. Networks.

[4]  Manuel Esteve,et al.  Highly-efficient fog-based deep learning AAL fall detection system , 2020, Internet Things.

[5]  Peter Ford Dominey,et al.  Improving quality of life with a narrative companion , 2017, 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[6]  Michal Podpora,et al.  Multimodal sentiment analysis applied to interaction between patients and a humanoid robot Pepper , 2019 .

[7]  María del Carmen Miranda Duro,et al.  Mobile Self-Monitoring ECG Devices to Diagnose Arrhythmia that Coincide with Palpitations: A Scoping Review , 2019, Healthcare.

[8]  Matteo Matteucci,et al.  Sleep Staging Based on Signals Acquired Through Bed Sensor , 2010, IEEE Transactions on Information Technology in Biomedicine.

[9]  Tan-Hsu Tan,et al.  Unobtrusive Activity Recognition of Elderly People Living Alone Using Anonymous Binary Sensors and DCNN , 2019, IEEE Journal of Biomedical and Health Informatics.

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

[11]  Muhammad Salman Khan,et al.  An unsupervised acoustic fall detection system using source separation for sound interference suppression , 2015, Signal Process..

[12]  Luminita Dumitriu,et al.  Unobtrusive Monitoring the Daily Activity Routine of Elderly People Living Alone, with Low-Cost Binary Sensors , 2019, Sensors.

[13]  Diane J. Cook,et al.  One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes , 2016, IEEE Journal of Selected Topics in Signal Processing.

[14]  María Malfaz,et al.  A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User , 2018, Sensors.

[15]  Maartje M. A. de Graaf,et al.  Exploring influencing variables for the acceptance of social robots , 2013, Robotics Auton. Syst..

[16]  Jian-Ru Chen,et al.  Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis , 2019, Electronics.

[17]  Shehroz S. Khan,et al.  Agitation Detection in People Living with Dementia using Multimodal Sensors , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[18]  Ester Martínez-Martín,et al.  Socially Assistive Robots for Older Adults and People with Autism: An Overview , 2020 .

[19]  J. Broekens,et al.  Assistive social robots in elderly care: a review , 2009 .

[20]  Christian U. Krägeloh,et al.  Questionnaires to Measure Acceptability of Social Robots: A Critical Review , 2019, Robotics.

[21]  Imad H. Elhajj,et al.  Support Vector Machines to Define and Detect Agitation Transition , 2010, IEEE Transactions on Affective Computing.

[22]  D.H. Stefanov,et al.  The smart house for older persons and persons with physical disabilities: structure, technology arrangements, and perspectives , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Bessam Abdulrazak,et al.  Novel Unobtrusive Approach for Sleep Monitoring Using Fiber Optics in an Ambient Assisted Living Platform , 2017, ICOST.

[24]  Robert J Petrella,et al.  HealtheBrain: an innovative smartphone application to improve cognitive function in older adults. , 2017, mHealth.

[25]  H. Marston,et al.  A Review of Age Friendly Virtual Assistive Technologies and their Effect on Daily Living for Carers and Dependent Adults , 2019, Healthcare.

[27]  Dorin Moldovan,et al.  Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data , 2020, Applied Sciences.

[28]  Nora Mattek,et al.  Variability in medication taking is associated with cognitive performance in nondemented older adults , 2017, Alzheimer's & dementia.

[29]  M. Hebert,et al.  Usability of a Wearable Camera System for Dementia Family Caregivers. , 2015, Journal of healthcare engineering.

[30]  Dongkyoo Shin,et al.  Ubiquitous Health Management System with Watch-Type Monitoring Device for Dementia Patients , 2014, J. Appl. Math..

[31]  Diane Myung-kyung Woodbridge,et al.  A Scalable Smartwatch-Based Medication Intake Detection System Using Distributed Machine Learning , 2020, Journal of Medical Systems.

[32]  Filip De Turck,et al.  Pro-active positioning of a social robot intervening upon behavioral disturbances of persons with dementia in a smart nursing home , 2019, Cognitive Systems Research.

[33]  Somnath Chatterji,et al.  Health in an ageing world—what do we know? , 2015, The Lancet.

[34]  S. Kühn,et al.  Fighting Depression: Action Video Game Play May Reduce Rumination and Increase Subjective and Objective Cognition in Depressed Patients , 2018, Front. Psychol..

[35]  PfahringerBernhard,et al.  A survey on feature drift adaptation , 2017 .

[36]  Lieveke Ameye,et al.  Reliability of commercially available sleep and activity trackers with manual switch-to-sleep mode activation in free-living healthy individuals , 2017, Int. J. Medical Informatics.

[37]  Jinxin Ma,et al.  Medhere: A Smartwatch-based Medication Adherence Monitoring System using Machine Learning and Distributed Computing , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[38]  Carlos A. Cifuentes,et al.  Expectation vs. Reality: Attitudes Towards a Socially Assistive Robot in Cardiac Rehabilitation , 2019, Applied Sciences.

[39]  Marie Manthey,et al.  A GUIDE FOR , 1967 .

[40]  Aitor Almeida,et al.  Promotion of active ageing combining sensor and social network data , 2016, J. Biomed. Informatics.

[41]  Andreas Holzinger,et al.  Ambient Assisted Living Technologies from the Perspectives of Older People and Professionals , 2017, CD-MAKE.

[42]  Jeffrey Soar,et al.  Older people, assistive technologies, and the barriers to adoption: A systematic review , 2016, Int. J. Medical Informatics.

[43]  A. Goldstone,et al.  A validation study of Fitbit Charge 2™ compared with polysomnography in adults , 2018, Chronobiology international.

[44]  A. Chan,et al.  A review of technology acceptance by older adults , 2011 .

[45]  Xingshe Zhou,et al.  MHS: A Multimedia System for Improving Medication Adherence in Elderly Care , 2011, IEEE Systems Journal.

[46]  B. Stubbs,et al.  Accelerometer-assessed light physical activity is protective of future cognitive ability: A longitudinal study among community dwelling older adults , 2017, Experimental Gerontology.

[47]  David C. Atkins,et al.  The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial , 2016, Journal of medical Internet research.

[48]  Blanka Frydrychova Klimova,et al.  Older People and Technology Acceptance , 2018, HCI.

[49]  B. Sartorius,et al.  Technology acceptance of older persons living in residential care , 2020, Information Development.

[50]  Ibrahim A. Hameed,et al.  User Acceptance of Social Robots , 2016, ACHI 2016.

[51]  J. van Hoof,et al.  “Who Doesn’t Think about Technology When Designing Urban Environments for Older People?” A Case Study Approach to a Proposed Extension of the WHO’s Age-Friendly Cities Model , 2019, International journal of environmental research and public health.

[52]  Tudor Cioara,et al.  A Policy-Based Context Aware Self-Management Model , 2009, 2009 11th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

[53]  Hsiu-Ping Yueh,et al.  Development and Evaluation of a Cognitive Training Game for Older People: A Design-based Approach , 2017, Front. Psychol..

[54]  Emanuele Frontoni,et al.  A sequential deep learning application for recognising human activities in smart homes , 2020, Neurocomputing.

[55]  S. Peek,et al.  The Challenges of Urban Ageing: Making Cities Age-Friendly in Europe , 2018, International journal of environmental research and public health.

[56]  Min-Sup Shin,et al.  Effects of smartphone-based memory training for older adults with subjective memory complaints: a randomized controlled trial , 2018, Aging & mental health.

[57]  Fernando Seoane,et al.  Wearable Biomedical Measurement Systems for Assessment of Mental Stress of Combatants in Real Time , 2014, Sensors.

[58]  Guillermo Rodríguez-Ortiz,et al.  PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables , 2017, Sensors.

[59]  Elena Torta,et al.  Evaluation of a Small Socially-Assistive Humanoid Robot in Intelligent Homes for the Care of the Elderly , 2014, J. Intell. Robotic Syst..

[60]  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.

[61]  H. Chaudhury,et al.  The benefits of and barriers to using a social robot PARO in care settings: a scoping review , 2019, BMC Geriatrics.

[62]  Dinesh Kumar Vishwakarma,et al.  A review of state-of-the-art techniques for abnormal human activity recognition , 2019, Eng. Appl. Artif. Intell..

[63]  Shehroz S. Khan,et al.  Detecting agitation and aggression in people with dementia using sensors—A systematic review , 2018, Alzheimer's & Dementia.

[64]  Alexandru Vulpe,et al.  eWALL: An Open-Source Cloud-Based eHealth Platform for Creating Home Caring Environments for Older Adults Living with Chronic Diseases or Frailty , 2017, Wirel. Pers. Commun..

[65]  David McEneaney,et al.  Arm-ECG Wireless Sensor System for Wearable Long-Term Surveillance of Heart Arrhythmias , 2019 .

[66]  Ville Kyrki,et al.  Impacts of robot implementation on care personnel and clients in elderly-care institutions , 2019, Int. J. Medical Informatics.

[67]  Andrew Hua,et al.  Accelerometer-based predictive models of fall risk in older women: a pilot study , 2018, npj Digital Medicine.

[68]  Vanessa Evers,et al.  The influence of social presence on acceptance of a companion robot by older people , 2008 .

[69]  Yoshiro Tajitsu,et al.  Piezoelectret sensor made from an electro-spun fluoropolymer and its use in a wristband for detecting heart-beat signals , 2015, IEEE Transactions on Dielectrics and Electrical Insulation.

[70]  Beno Benhabib,et al.  Robot Imitation Learning of Social Gestures with Self-Collision Avoidance Using a 3D Sensor , 2018, Sensors.

[71]  Geoff Holmes,et al.  Evaluation methods and decision theory for classification of streaming data with temporal dependence , 2015, Machine Learning.

[72]  Lili Zhang,et al.  The IoT-based heart disease monitoring system for pervasive healthcare service , 2017, KES.

[73]  Albert Ali Salah,et al.  An autonomous robotic exercise tutor for elderly people , 2017, Auton. Robots.

[74]  Yuko Yasuhara,et al.  Rehabilitation care with Pepper humanoid robot: A qualitative case study of older patients with schizophrenia and/or dementia in Japan. , 2020, Enfermeria clinica.

[75]  S. Dong,et al.  Waist-wearable wireless respiration sensor based on triboelectric effect , 2019, Nano Energy.

[76]  Paolo Barsocchi,et al.  An unobtrusive sleep monitoring system for the human sleep behaviour understanding , 2016, 2016 7th IEEE International Conference on Cognitive Infocommunications (CogInfoCom).

[77]  Gregory M. P. O'Hare,et al.  Time-bounded Activity Recognition for Ambient Assisted Living , 2018 .

[78]  H. Lan,et al.  SWRL : A semantic Web rule language combining OWL and ruleML , 2004 .

[79]  Vicente Julián,et al.  PHAROS—PHysical Assistant RObot System , 2018, Sensors.

[80]  Sergei Gorlatch,et al.  Automatic Fall Detection System using Sensing Floors , 2016 .

[81]  Kai-Chun Liu,et al.  Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model , 2017, Sensors.

[82]  Francesco Piazza,et al.  Human Fall Detection by Using an Innovative Floor Acoustic Sensor , 2018, Multidisciplinary Approaches to Neural Computing.

[83]  Joost van Hoof,et al.  Factors influencing acceptance of technology for aging in place: A systematic review , 2014, Int. J. Medical Informatics.

[84]  Hassan Ghasemzadeh,et al.  A machine learning approach for medication adherence monitoring using body-worn sensors , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[85]  Alexandre Kalache,et al.  Towards Global Age-Friendly Cities: Determining Urban Features that Promote Active Aging , 2010, Journal of Urban Health.

[86]  Dorin Moldovan,et al.  M2O: A library for using ontologies in software engineering , 2015, 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP).

[87]  Rebecca M. C. Spencer,et al.  Reliability of Sleep Measures from Four Personal Health Monitoring Devices Compared to Research-Based Actigraphy and Polysomnography , 2016, Sensors.

[88]  Jeffrey M. Hausdorff,et al.  Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls , 2012, PloS one.

[89]  Jean Paul Barddal,et al.  A survey on feature drift adaptation: Definition, benchmark, challenges and future directions , 2017, J. Syst. Softw..

[90]  Kevin Kelly,et al.  Meet Stevie: a Socially Assistive Robot Developed Through Application of a ‘Design-Thinking’ Approach , 2019, Journal of Intelligent & Robotic Systems.

[91]  JongSuk Choi,et al.  A robot-assisted behavioral intervention system for children with autism spectrum disorders , 2016, Robotics Auton. Syst..

[92]  Steffen Leonhardt,et al.  A Novel 12-Lead ECG T-Shirt with Active Electrodes , 2016 .

[93]  Rajiv Khosla,et al.  Socially Assistive Robots in Elderly Care: A Mixed-Method Systematic Literature Review , 2014, Int. J. Hum. Comput. Interact..

[94]  Panos Markopoulos,et al.  Crowd of Oz: A Crowd-Powered Social Robotics System for Stress Management , 2020, Sensors.

[95]  Rahim Tafazolli,et al.  Adaptive Clustering for Dynamic IoT Data Streams , 2017, IEEE Internet of Things Journal.

[96]  Alessio Vecchio,et al.  A smartphone-based fall detection system , 2012, Pervasive Mob. Comput..

[97]  Rosa Maria Alsina-Pagès,et al.  Real-Time Distributed Architecture for Remote Acoustic Elderly Monitoring in Residential-Scale Ambient Assisted Living Scenarios , 2018, Sensors.

[98]  Ara Darzi,et al.  A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study , 2018, European Journal of Medical Research.

[99]  Jie Luo,et al.  Highly Portable, Sensor-Based System for Human Fall Monitoring , 2017, Sensors.

[100]  Javad Razjouyan,et al.  Improving Sleep Quality Assessment Using Wearable Sensors by Including Information From Postural/Sleep Position Changes and Body Acceleration: A Comparison of Chest-Worn Sensors, Wrist Actigraphy, and Polysomnography. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[101]  S. S. Man,et al.  Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. , 2019, Applied ergonomics.

[102]  Nuno M. Garcia,et al.  Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices , 2019 .

[103]  Azziza Bankole,et al.  Multiple-Instance Learning for Sparse Behavior Modeling from Wearables: Toward Dementia-Related Agitation Prediction , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[104]  Rodolphe Gelin,et al.  A Mass-Produced Sociable Humanoid Robot: Pepper: The First Machine of Its Kind , 2018, IEEE Robotics & Automation Magazine.

[105]  S. Teipel,et al.  Automated sensor-based detection of challenging behaviors in advanced stages of dementia in nursing homes , 2019, Alzheimer's & Dementia.

[106]  Marcos Faúndez-Zanuy,et al.  On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis , 2013, Sensors.

[107]  Alberto Trombetta,et al.  Semantic based events signaling for AAL systems , 2017, Journal of Ambient Intelligence and Humanized Computing.

[108]  Muhammad Awais,et al.  Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection , 2019, Sensors.

[109]  Dorin Moldovan,et al.  Identifying the Polypharmacy Side-Effects in Daily Life Activities of Elders with Dementia , 2018, IDC.

[110]  H. Kang,et al.  Review of outcome measures in PARO robot intervention studies for dementia care. , 2020, Geriatric nursing.

[111]  Yunyoung Nam,et al.  Sleep Monitoring Based on a Tri-Axial Accelerometer and a Pressure Sensor , 2016, Sensors.

[112]  Der-Jiunn Deng,et al.  Concept Drift Detection and Adaption in Big Imbalance Industrial IoT Data Using an Ensemble Learning Method of Offline Classifiers , 2019, IEEE Access.

[113]  Hannu Toivonen,et al.  Unobtrusive online monitoring of sleep at home , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[114]  Priyanka Kakria,et al.  A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors , 2015, International journal of telemedicine and applications.

[115]  Jacqueline Mogle,et al.  App‐based attention training: Incorporating older adults’ feedback to facilitate home‐based use , 2018, International journal of older people nursing.

[116]  Marjorie Skubic,et al.  Fall Detection in Homes of Older Adults Using the Microsoft Kinect , 2015, IEEE Journal of Biomedical and Health Informatics.

[117]  Jan Nedoma,et al.  Monitoring of the daily living activities in smart home care , 2017, Human-centric Computing and Information Sciences.

[118]  Yun Li,et al.  A Microphone Array System for Automatic Fall Detection , 2012, IEEE Transactions on Biomedical Engineering.

[119]  Binh Q. Tran,et al.  Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm , 2015, J. Sensors.

[120]  Juan A. Botía Blaya,et al.  Ambient Assisted Living system for in-home monitoring of healthy independent elders , 2012, Expert Syst. Appl..

[121]  Inês Sousa,et al.  Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders , 2019, Sensors.

[122]  Kyung-Sup Kwak,et al.  The Internet of Things for Health Care: A Comprehensive Survey , 2015, IEEE Access.

[123]  Aitor Almeida,et al.  A critical analysis of an IoT - aware AAL system for elderly monitoring , 2019, Future Gener. Comput. Syst..

[124]  Lisa A. Newland,et al.  Remote patient monitoring acceptance trends among older adults residing in a frontier state , 2015, Comput. Hum. Behav..

[125]  W C Mann,et al.  Elder Acceptance of Health Monitoring Devices in the Home , 2002, Care Management Journals.

[126]  Maria E. Niessen,et al.  Monitoring Activities of Daily Living in Smart Homes: Understanding human behavior , 2016, IEEE Signal Processing Magazine.

[127]  Mehedi Masud,et al.  Situation Awareness in Ambient Assisted Living for Smart Healthcare , 2017, IEEE Access.

[128]  Andrew F. Monk,et al.  Technological opportunities for supporting people with dementia who are living at home , 2008, Int. J. Hum. Comput. Stud..