The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development

Human activity detection within smart homes is one of the basis of unobtrusive wellness monitoring of a rapidly aging population in developed countries. Most works in this area use the concept of “activity” as the building block with which to construct applications such as healthcare monitoring or ambient assisted living. The process of identifying a specific activity encompasses the selection of the appropriate set of sensors, the correct preprocessing of their provided raw data and the learning/reasoning using this information. If the selection of the sensors and the data processing methods are wrongly performed, the whole activity detection process may fail, leading to the consequent failure of the whole application. Related to this, the main contributions of this review are the following: first, we propose a classification of the main activities considered in smart home scenarios which are targeted to older people’s independent living, as well as their characterization and formalized context representation; second, we perform a classification of sensors and data processing methods that are suitable for the detection of the aforementioned activities. Our aim is to help researchers and developers in these lower-level technical aspects that are nevertheless fundamental for the success of the complete application.

[1]  Claudio Bettini,et al.  Fine-grained recognition of abnormal behaviors for early detection of mild cognitive impairment , 2015, 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom).

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

[3]  Qing Zhang,et al.  Determination of Activities of Daily Living of independent living older people using environmentally placed sensors , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  Martin Becker,et al.  Software Architecture Trends and Promising Technology for Ambient Assisted Living Systems , 2007, Assisted Living Systems - Models, Architectures and Engineering Approaches.

[5]  Arkady B. Zaslavsky,et al.  Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.

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

[7]  Miguel A. Labrador,et al.  Centinela: A human activity recognition system based on acceleration and vital sign data , 2012, Pervasive Mob. Comput..

[8]  Michel Vacher,et al.  Making Context Aware Decision from Uncertain Information in a Smart Home: A Markov Logic Network Approach , 2013, AmI.

[9]  John Paul Varkey,et al.  Human motion recognition using a wireless sensor-based wearable system , 2012, Personal and Ubiquitous Computing.

[10]  Araceli Sanchis,et al.  Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors , 2013, Sensors.

[11]  Henry A. Kautz,et al.  Inferring activities from interactions with objects , 2004, IEEE Pervasive Computing.

[12]  Qing Zhang,et al.  Assisting an Elderly with Early Dementia Using Wireless Sensors Data in Smarter Safer Home , 2014, ICISO.

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

[14]  Mitsuru Ikeda,et al.  Activity Recognition Using Context-Aware Infrastructure Ontology in Smart Home Domain , 2012, 2012 Seventh International Conference on Knowledge, Information and Creativity Support Systems.

[15]  Mark Hasegawa-Johnson,et al.  Acoustic fall detection using Gaussian mixture models and GMM supervectors , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  Brigitte Meillon,et al.  The sweet-home project: Audio technology in smart homes to improve well-being and reliance , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Subhas Mukhopadhyay,et al.  Forecasting the behavior of an elderly using wireless sensors data in a smart home , 2013, Eng. Appl. Artif. Intell..

[18]  Andrei Tolstikov,et al.  2-layer Erroneous-Plan Recognition for dementia patients in smart homes , 2009, 2009 11th International Conference on e-Health Networking, Applications and Services (Healthcom).

[19]  Gwenn Englebienne,et al.  Accurate activity recognition in a home setting , 2008, UbiComp.

[20]  Chris D. Nugent,et al.  A Knowledge-Driven Approach to Activity Recognition in Smart Homes , 2012, IEEE Transactions on Knowledge and Data Engineering.

[21]  Chris D. Nugent,et al.  Segmenting sensor data for activity monitoring in smart environments , 2012, Personal and Ubiquitous Computing.

[22]  Young-Sik Jeong,et al.  RFID-based indoor location tracking to ensure the safety of the elderly in smart home environments , 2013, Personal and Ubiquitous Computing.

[23]  Deborah Estrin,et al.  Using mobile phones to determine transportation modes , 2010, TOSN.

[24]  Bernardo Gonçalves,et al.  ECGAWARE: AN ECG MARKUP LANGUAGE FOR AMBULATORY TELEMONITORING AND DECISION MAKING SUPPORT , 2008, HEALTHINF 2008.

[25]  Liming Chen,et al.  Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes , 2014, Future Gener. Comput. Syst..

[26]  Brigitte Meillon,et al.  Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects , 2011, Personal and Ubiquitous Computing.

[27]  U. Rajendra Acharya,et al.  Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform , 2013, Knowl. Based Syst..

[28]  Simon A. Dobson,et al.  KCAR: A knowledge-driven approach for concurrent activity recognition , 2015, Pervasive Mob. Comput..

[29]  Sungyoung Lee,et al.  KARE: a hybrid reasoning approach for promoting active lifestyle , 2015, IMCOM.

[30]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[31]  René Mayrhofer,et al.  An Analysis of Different Approaches to Gait Recognition Using Cell Phone Based Accelerometers , 2013, MoMM '13.

[32]  Stefan Klein,et al.  Feature Selection Based on SVM Significance Maps for Classification of Dementia , 2014, MLMI.

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

[34]  Marcello Ferro,et al.  Personal Health System architecture for stress monitoring and support to clinical decisions , 2012, Comput. Commun..

[35]  Jian Lu,et al.  Recognizing multi-user activities using wearable sensors in a smart home , 2011, Pervasive Mob. Comput..

[36]  Eamonn J. Keogh,et al.  An online algorithm for segmenting time series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[37]  H. B. Mitchell Sensor Value Normalization , 2007 .

[38]  Matthew S. Goodwin,et al.  Automated Detection of Stereotypical Motor Movements , 2011, Journal of autism and developmental disorders.

[39]  Araceli Sanchis,et al.  Online activity recognition using evolving classifiers , 2013, Expert Syst. Appl..

[40]  Chris D. Nugent,et al.  Ontology-based activity recognition in intelligent pervasive environments , 2009, Int. J. Web Inf. Syst..

[41]  Simon A. Dobson,et al.  USMART , 2014, ACM Trans. Interact. Intell. Syst..

[42]  Alex Mihailidis,et al.  A Survey on Ambient-Assisted Living Tools for Older Adults , 2013, IEEE Journal of Biomedical and Health Informatics.

[43]  Ifeyinwa E. Achumba,et al.  Monitoring: Taxonomy of Issues, Techniques, Applications, Challenges and Limitations , 2013 .

[44]  Lei Gao,et al.  Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. , 2014, Medical engineering & physics.

[45]  Lih-Jen Kau,et al.  A smart phone-based pocket fall accident detection system , 2014, 2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014).

[46]  Abdenour Bouzouane,et al.  A Smart Home Agent for Plan Recognition of Cognitively-impaired Patients , 2006, J. Comput..

[47]  Marcia J Scherer,et al.  From people-centered to person-centered services, and back again , 2014, Disability and rehabilitation. Assistive technology.

[48]  Elpiniki I. Papageorgiou,et al.  Towards a hierarchically-structured decision support tool for improving seniors' independent living: the USEFIL decision support system , 2013, PETRA '13.

[49]  Young-Koo Lee,et al.  A Framework for Supervising Lifestyle Diseases Using Long-Term Activity Monitoring , 2012, Sensors.

[50]  G. Klyne,et al.  Composite Capability/Preference Profiles (CC/PP) : Structure and Vocabularies , 2001 .

[51]  Norbert Noury,et al.  Computer simulation of the activity of the elderly person living independently in a Health Smart Home , 2012, Comput. Methods Programs Biomed..

[52]  Ivan Marsic,et al.  Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study , 2013, IEEE Transactions on Instrumentation and Measurement.

[53]  Paul Lukowicz,et al.  Collecting complex activity datasets in highly rich networked sensor environments , 2010, 2010 Seventh International Conference on Networked Sensing Systems (INSS).

[54]  Tim Dallas,et al.  Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer , 2014, IEEE Transactions on Biomedical Engineering.

[55]  Weng-Keen Wong,et al.  Physical Activity Recognition from Accelerometer Data Using a Multi-Scale Ensemble Method , 2013, IAAI.

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

[57]  Seok-Won Lee,et al.  Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones , 2013, Sensors.

[58]  B. Reimer,et al.  Older Adult Perceptions of Smart Home Technologies: Implications for Research, Policy & Market Innovations in Healthcare , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[59]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[60]  Hongnian Yu,et al.  Elderly activities recognition and classification for applications in assisted living , 2013, Expert Syst. Appl..

[61]  Claudio Bettini,et al.  Extended Report: Fine-grained Recognition of Abnormal Behaviors for Early Detection of Mild Cognitive Impairment , 2015, ArXiv.

[62]  Diane J. Cook,et al.  A Data Mining Framework for Activity Recognition in Smart Environments , 2010, 2010 Sixth International Conference on Intelligent Environments.

[63]  黄亚明,et al.  MedicineNet , 2012 .

[64]  Ifeyinwa E. Achumba,et al.  On time series sensor data segmentation for fall and activity classification , 2012, 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom).

[65]  Hongyi Li,et al.  A method to deal with installation errors of wearable accelerometers for human activity recognition , 2011, Physiological measurement.

[66]  Mauro Serpelloni,et al.  T-Shirt for Vital Parameter Monitoring , 2014 .

[67]  Shuwan Xue,et al.  Portable Preimpact Fall Detector With Inertial Sensors , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[68]  Carmen D Dirksen,et al.  Literature review on monitoring technologies and their outcomes in independently living elderly people , 2015, Disability and rehabilitation. Assistive technology.

[69]  Mikko Sallinen,et al.  Progressive monitoring and treatment planning of diabetes mellitus in smart home environment , 2013, 2013 IEEE International Conference on Consumer Electronics (ICCE).

[70]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[71]  Nagendra Sen,et al.  Development of a Novel ECG signal DenoisingSystem Using Extended Kalman Filter , 2014 .

[72]  Hassan Ghasemzadeh,et al.  A Body Sensor Network With Electromyogram and Inertial Sensors: Multimodal Interpretation of Muscular Activities , 2010, IEEE Transactions on Information Technology in Biomedicine.

[73]  Joël Vogt,et al.  Requirements Elicitation and System Specification of Assistive Systems for People with Mild Dementia , 2013 .

[74]  Paul Lukowicz,et al.  Active Capacitive Sensing: Exploring a New Wearable Sensing Modality for Activity Recognition , 2010, Pervasive.

[75]  Matti Linnavuo,et al.  Detection of falls among the elderly by a floor sensor using the electric near field , 2010, IEEE Transactions on Information Technology in Biomedicine.

[76]  Bernardo Gonçalves,et al.  ECGWARE: an ECG Markup Language for Ambulatory Telemonitoring and Decision Making Support , 2008, HEALTHINF.

[77]  Christopher G. Atkeson,et al.  Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors , 2005, Pervasive.

[78]  Bernt Schiele,et al.  A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.

[79]  Jennifer Healey,et al.  A Long-Term Evaluation of Sensing Modalities for Activity Recognition , 2007, UbiComp.

[80]  Gerhard Tröster,et al.  Eye Movement Analysis for Activity Recognition Using Electrooculography , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[81]  Giancarlo Fortino,et al.  A Java-Based Agent Platform for Programming Wireless Sensor Networks , 2011, Comput. J..

[82]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[83]  Daniel Borrajo,et al.  A dynamic sliding window approach for activity recognition , 2011, UMAP'11.

[84]  Gwenn Englebienne,et al.  UvA-DARE ( Digital Academic Repository ) Activity recognition using semi-Markov models on real world smart home datasets , 2010 .

[85]  Matthias Budde,et al.  SPAR Service-based Personal Activity Recognition for Mobile Phones , 2010 .

[86]  Abdelhamid Salih Mohamed Salih,et al.  A Review of Ambient Intelligence Assisted Healthcare Monitoring , 2014 .

[87]  Norbert Noury,et al.  Telemonitoring of patients at home: a software agent approach , 2003, Comput. Methods Programs Biomed..

[88]  Georgios Meditskos,et al.  MetaQ: A knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns , 2016, Pervasive Mob. Comput..

[89]  Mahdi Shabany,et al.  Efficient implementation of real-time ECG derived respiration system using cubic spline interpolation , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[90]  Weihua Sheng,et al.  Motion- and location-based online human daily activity recognition , 2011, Pervasive Mob. Comput..

[91]  Hsueh-Chun Lin,et al.  A Preliminary Activity Recognition of WSN Data on Ubiquitous Health Care for Physical Therapy , 2013 .

[92]  Heribert Baldus,et al.  A body-fixed-sensor-based analysis of power during sit-to-stand movements. , 2010, Gait & posture.

[93]  Sethuraman Panchanathan,et al.  Analysis of low resolution accelerometer data for continuous human activity recognition , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[94]  Motoki Miura,et al.  A Study of Long Term Tendencies in Residents' Activities of Daily Living at a Group Home for People with Dementia Using RFID Slippers , 2011, ICOST.

[95]  Paul J. M. Havinga,et al.  Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey , 2010, ARCS Workshops.

[96]  Chao Chen,et al.  CASASviz: Web-based visualization of behavior patterns in smart environments , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[97]  Gamini Dissanayake,et al.  Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals , 2012, Expert Syst. Appl..

[98]  Hongnian Yu,et al.  A practical multi-sensor activity recognition system for home-based care , 2014, Decis. Support Syst..

[99]  Diane J. Cook,et al.  Simple and Complex Activity Recognition through Smart Phones , 2012, 2012 Eighth International Conference on Intelligent Environments.

[100]  Kristof Van Laerhoven,et al.  A Feature Set Evaluation for Activity Recognition with Body-Worn Inertial Sensors , 2011, AmI Workshops.

[101]  M. N. Nyan,et al.  Classification of gait patterns in the time-frequency domain. , 2006, Journal of biomechanics.

[102]  Fernando Seoane,et al.  Adaptive Software Architecture Based on Confident HCI for the Deployment of Sensitive Services in Smart Homes , 2015, Sensors.

[103]  Wajahat Ali Khan,et al.  Recommendations service for chronic disease patient in multimodel sensors home environment. , 2015, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

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

[105]  Anijo Punnen Mathew,et al.  Technology to Aid Aging in Place- New Opportunities and Challenges , 2007 .

[106]  Jadwiga Indulska,et al.  A software engineering framework for context-aware pervasive computing , 2004, Second IEEE Annual Conference on Pervasive Computing and Communications, 2004. Proceedings of the.

[107]  Ahmad Lotfi,et al.  Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour , 2012, J. Ambient Intell. Humaniz. Comput..

[108]  Araceli Sanchis,et al.  Sensor-based Bayesian detection of anomalous living patterns in a home setting , 2014, Personal and Ubiquitous Computing.

[109]  Josef Hallberg,et al.  Assessing the Impact of the homeML Format and the homeML Suite within the Research Community , 2013, J. Univers. Comput. Sci..

[110]  Martina Ziefle,et al.  Smart Home Technologies: Insights into Generation-Specific Acceptance Motives , 2009, USAB.

[111]  Barry R. Greene,et al.  Quantitative Falls Risk Assessment Using the Timed Up and Go Test , 2010, IEEE Transactions on Biomedical Engineering.

[112]  Zhenyu He,et al.  Activity recognition from acceleration data based on discrete consine transform and SVM , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[113]  Juan Carlos Augusto,et al.  Distributed Vision-Based Accident Management for Assisted Living , 2007, ICOST.

[114]  Vincent Rialle,et al.  What Do Family Caregivers of Alzheimer’s Disease Patients Desire in Smart Home Technologies? , 2009, Methods of Information in Medicine.

[115]  Panagiotis D. Bamidis,et al.  Integrating the USEFIL Assisted Living Platform; Observation from the Field , 2015 .

[116]  Steve Brown,et al.  User experience design guidelines for telecare (e-health) services , 2007, INTR.

[117]  Nigel H. Lovell,et al.  Longitudinal Falls-Risk Estimation Using Triaxial Accelerometry , 2010, IEEE Transactions on Biomedical Engineering.

[118]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[119]  Diane J. Cook,et al.  MavHome: an agent-based smart home , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[120]  Matjaz Gams,et al.  Telehealth using ECG sensor and accelerometer , 2014, 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[121]  Héctor Pomares,et al.  A benchmark dataset to evaluate sensor displacement in activity recognition , 2012, UbiComp.

[122]  Michel Vacher,et al.  SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results , 2010, IEEE Transactions on Information Technology in Biomedicine.

[123]  Ta-Wen Kuan,et al.  SVM-based IADL score correlation and classification with EEG/ECG signals , 2013, 2013 1st International Conference on Orange Technologies (ICOT).

[124]  Héctor Pomares,et al.  Window Size Impact in Human Activity Recognition , 2014, Sensors.

[125]  Mamun Bin Ibne Reaz,et al.  A Review of Smart Homes—Past, Present, and Future , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[126]  R. Matthews,et al.  A Wearable Physiological Sensor Suite for Unobtrusive Monitoring of Physiological and Cognitive State , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[127]  Samir Chatterjee,et al.  Persuasive and pervasive sensing: A new frontier to monitor, track and assist older adults suffering from type-2 diabetes , 2013, 2013 46th Hawaii International Conference on System Sciences.

[128]  Lothar Litz,et al.  Data-driven generation of rule-based behavior models for an Ambient assisted living system , 2013, 2013 IEEE Third International Conference on Consumer Electronics ¿ Berlin (ICCE-Berlin).

[129]  Chris D. Nugent,et al.  Using Event Calculus for Behaviour Reasoning and Assistance in a Smart Home , 2008, ICOST.

[130]  R. Lutolf,et al.  Smart Home concept and the integration of energy meters into a home based system , 1992 .

[131]  S. Katz,et al.  A Measure of Primary Sociobiological Functions , 1976, International journal of health services : planning, administration, evaluation.

[132]  Kunal Pal,et al.  Development of EOG based human machine interface control system for motorized wheelchair , 2014, 2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD).

[133]  Harry Chen,et al.  SOUPA: standard ontology for ubiquitous and pervasive applications , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[134]  Daqing Zhang,et al.  Enabling Context-aware Smart Home with Semantic Web Technologies , 2006 .

[135]  Juha Röning,et al.  User-Independent Human Activity Recognition Using a Mobile Phone: Offline Recognition vs. Real-Time on Device Recognition , 2012, DCAI.

[136]  Mi Zhang,et al.  A feature selection-based framework for human activity recognition using wearable multimodal sensors , 2011, BODYNETS.

[137]  Diogo R. Ferreira,et al.  Preprocessing techniques for context recognition from accelerometer data , 2010, Personal and Ubiquitous Computing.

[138]  Lei Wang,et al.  Analysis of filtering methods for 3D acceleration signals in body sensor network , 2011, International Symposium on Bioelectronics and Bioinformations 2011.

[139]  Diane J. Cook,et al.  CASAS: A Smart Home in a Box , 2013, Computer.

[140]  Gérard Chollet,et al.  Thermal signal analysis in smart home environment for detecting a human presence , 2014, 2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

[141]  Diane J. Cook,et al.  Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset , 2014 .

[142]  C. Sweetlin Hemalatha,et al.  Conference on Ambient Systems , Networks and Technologies ( ANT 2013 ) Frequent Bit Pattern Mining Over Triaxial Accelerometer Data Streams For Recognizing Human Activities And Detecting Fall , 2013 .

[143]  V. Klyuev,et al.  A smart reminder system for complex human activities , 2012, 2012 14th International Conference on Advanced Communication Technology (ICACT).

[144]  Minoru Yoshizawa,et al.  Parameter exploration for response time reduction in accelerometer-based activity recognition , 2013, UbiComp.

[145]  William C. Mann,et al.  The Gator Tech Smart House: a programmable pervasive space , 2005, Computer.