The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development
暂无分享,去创建一个
Iván Pau | Qin Ni | Ana-Belén García-Hernando | I. Pau | Qin Ni | Ana-Belén García-Hernando | Iván Pau
[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.