Mining the home environment

Individuals spend a majority of their time in their home or workplace and for many, these places are our sanctuaries. As society and technology advance there is a growing interest in improving the intelligence of the environments in which we live and work. By filling home environments with sensors and collecting data during daily routines, researchers can gain insights on human daily behavior and the impact of behavior on the residents and their environments. In this article we provide an overview of the data mining opportunities and challenges that smart environments provide for researchers and offer some suggestions for future work in this area.

[1]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words , 2008, International Journal of Computer Vision.

[2]  Desney S. Tan,et al.  An ultra-low-power human body motion sensor using static electric field sensing , 2012, UbiComp.

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

[4]  James M. Keller,et al.  Resident identification using kinect depth image data and fuzzy clustering techniques , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  S. Heinze,et al.  Spontaneous atomic-scale magnetic skyrmion lattice in two dimensions , 2011 .

[6]  LukowiczPaul,et al.  Gesture spotting with body-worn inertial sensors to detect user activities , 2008 .

[7]  A. Mihailidis,et al.  The development of an adaptive upper-limb stroke rehabilitation robotic system , 2011, Journal of NeuroEngineering and Rehabilitation.

[8]  P. Johri,et al.  Survey on Privacy Preserving Data Mining , 2014 .

[9]  Jian Lu,et al.  A hierarchical approach to real-time activity recognition in body sensor networks , 2012, Pervasive Mob. Comput..

[10]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  E. Waltz,et al.  How i quantified myself , 2012, IEEE Spectrum.

[12]  Stefan Goetze,et al.  Voice activity detection driven acoustic event classification for monitoring in smart homes , 2010, 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010).

[13]  Stan Sclaroff,et al.  A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[15]  Miguel A. Labrador,et al.  On the Interactions between Privacy-Preserving, Incentive, and Inference Mechanisms in Participatory Sensing Systems , 2013, NSS.

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

[17]  Diane J. Cook,et al.  Discovering frequent user--environment interactions in intelligent environments , 2011, Personal and Ubiquitous Computing.

[18]  Matthai Philipose,et al.  Common Sense Based Joint Training of Human Activity Recognizers , 2007, IJCAI.

[19]  David Wetherall,et al.  Recognizing daily activities with RFID-based sensors , 2009, UbiComp.

[20]  Yaser Sheikh,et al.  Matching Trajectories of Anatomical Landmarks Under Viewpoint, Anthropometric and Temporal Transforms , 2009, International Journal of Computer Vision.

[21]  Diane J. Cook,et al.  Activity Discovery and Activity Recognition: A New Partnership , 2013, IEEE Transactions on Cybernetics.

[22]  H. V. Dijck.  Energy efficiency in buildings , 2009 .

[23]  Jian Pei,et al.  Constraint-based sequential pattern mining: the pattern-growth methods , 2007, Journal of Intelligent Information Systems.

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

[25]  Koji Yatani,et al.  BodyScope: a wearable acoustic sensor for activity recognition , 2012, UbiComp.

[26]  Les E. Atlas,et al.  Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments , 2012, Pervasive Mob. Comput..

[27]  David C Klonoff,et al.  The Increasing Incidence of Diabetes in the 21st Century , 2009, Journal of diabetes science and technology.

[28]  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).

[29]  Eric C. Larson,et al.  Disaggregated End-Use Energy Sensing for the Smart Grid , 2011, IEEE Pervasive Computing.

[30]  Karen L. Courtney,et al.  Defining Obtrusiveness in Home Telehealth Technologies , 2006 .

[31]  Gregory D. Abowd,et al.  Physical, Social, and Experiential Knowledge in Pervasive Computing Environments , 2007, IEEE Pervasive Computing.

[32]  Sarvapali D. Ramchurn,et al.  Understanding domestic energy consumption through interactive visualisation: a field study , 2012, UbiComp.

[33]  B. Kröse,et al.  Bayesian Activity Recognition in Residence for Elders , 2007 .

[34]  Diane J. Cook,et al.  Data Mining for Hierarchical Model Creation , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[35]  Shuang Wang,et al.  Using passive sensing to estimate relative energy expenditure for eldercare monitoring , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[36]  Diane J. Cook,et al.  Defining the Complexity of an Activity , 2011, Activity Context Representation.

[37]  Hani Hagras,et al.  An Incremental Adaptive Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments , 2007, IEEE Transactions on Fuzzy Systems.

[38]  Kun Li,et al.  MAQS: a personalized mobile sensing system for indoor air quality monitoring , 2011, UbiComp '11.

[39]  Lawrence B. Holder,et al.  Discovering Activities to Recognize and Track in a Smart Environment , 2011, IEEE Transactions on Knowledge and Data Engineering.

[40]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[41]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[42]  Paul Lukowicz,et al.  Gesture spotting with body-worn inertial sensors to detect user activities , 2008, Pattern Recognit..

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

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

[45]  Miguel A. Labrador,et al.  MaPIR: Mapping-based private information retrieval for location privacy in LBISs , 2013, 38th Annual IEEE Conference on Local Computer Networks - Workshops.

[46]  Vikramaditya R. Jakkula,et al.  Anomaly Detection Using Temporal Data Mining in a Smart Home Environment , 2008, Methods of Information in Medicine.

[47]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories , 2006 .

[48]  M. Schmitter-Edgecombe,et al.  Characterizing multiple memory deficits and their relation to everyday functioning in individuals with mild cognitive impairment. , 2009, Neuropsychology.

[49]  Qiang Yang,et al.  Cross-domain activity recognition via transfer learning , 2011, Pervasive Mob. Comput..

[50]  B. Winblad,et al.  Influence of social network on occurrence of dementia: a community-based longitudinal study , 2000, The Lancet.

[51]  Ari Visa,et al.  GAIS: A Method for Detecting Interleaved Sequential Patterns from Imperfect Data , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[52]  Albert-László Barabási,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[53]  Koji Tsukada,et al.  IteMinder: finding items in a room using passive RFID tags and an autonomous robot (poster) , 2011, UbiComp '11.

[54]  Alex Pentland,et al.  Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[55]  Mikkel Baun Kjærgaard,et al.  Mobile sensing of pedestrian flocks in indoor environments using WiFi signals , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[56]  Donald E. Brown,et al.  Health-status monitoring through analysis of behavioral patterns , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[57]  Anind K. Dey,et al.  Embedded assessment of aging adults: A concept validation with stakeholders , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[58]  E. Cornwell,et al.  Social Disconnectedness, Perceived Isolation, and Health among Older Adults∗ , 2009, Journal of health and social behavior.

[59]  Michael C. Mozer,et al.  The Neural Network House: An Environment that Adapts to its Inhabitants , 1998 .

[60]  Kun Li,et al.  MAQS: a mobile sensing system for indoor air quality , 2011, UbiComp '11.

[61]  Philippe Mabilleau,et al.  Location Estimation in a Smart Home: System Implementation and Evaluation Using Experimental Data , 2008, International journal of telemedicine and applications.

[62]  Ramakant Nevatia,et al.  Video-based event recognition: activity representation and probabilistic recognition methods , 2004, Comput. Vis. Image Underst..

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

[64]  Matthai Philipose,et al.  Unsupervised Activity Recognition Using Automatically Mined Common Sense , 2005, AAAI.

[65]  Norbert Gyorbíró,et al.  An Activity Recognition System For Mobile Phones , 2009, Mob. Networks Appl..

[66]  D. Harvey,et al.  MCI is Associated With Deficits in Everyday Functioning , 2006, Alzheimer disease and associated disorders.

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

[68]  AthitsosVassilis,et al.  A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation , 2009 .

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

[70]  Bernt Schiele,et al.  Discovery of activity patterns using topic models , 2008 .

[71]  Sunny Consolvo,et al.  Living in a glass house: a survey of private moments in the home , 2011, UbiComp '11.

[72]  Qiang Yang,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Transfer Learning for Activity Recognition via Sensor Mapping , 2022 .

[73]  G. Mcnicoll.  World Population Ageing 1950-2050. , 2002 .

[74]  John Krumm,et al.  PreHeat: controlling home heating using occupancy prediction , 2011, UbiComp '11.

[75]  D. Cook,et al.  Smart Home-Based Health Platform for Behavioral Monitoring and Alteration of Diabetes Patients , 2009, Journal of diabetes science and technology.

[76]  Stephen Lindsay,et al.  Making family care work: dependence, privacy and remote home monitoring telecare systems , 2013, UbiComp.

[77]  H. Jimison,et al.  Mobility Assessment Using Event-Related Responses , 2006, 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2..

[78]  Diane J. Cook,et al.  Smart environments - technology, protocols and applications , 2004 .

[79]  M. Trivedi,et al.  Articulated Human Body Pose Inference from Voxel Data Using a Kinematically Constrained Gaussian Mixture Model , 2007 .

[80]  Ramakant Nevatia,et al.  Coupled Hidden Semi Markov Models for Activity Recognition , 2007, 2007 IEEE Workshop on Motion and Video Computing (WMVC'07).

[81]  Daniel Gatica-Perez,et al.  StressSense: detecting stress in unconstrained acoustic environments using smartphones , 2012, UbiComp.

[82]  N. Ambady,et al.  Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis. , 1992 .

[83]  Matthai Philipose,et al.  Building Reliable Activity Models Using Hierarchical Shrinkage and Mined Ontology , 2006, Pervasive.

[84]  Prafulla N. Dawadi,et al.  An approach to cognitive assessment in smart home , 2011, DMMH '11.

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

[86]  Mark W. Newman,et al.  Automatic Assessment of Cognitive Impairment through Electronic Observation of Object Usage , 2010, Pervasive.

[87]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[88]  M. Skubic,et al.  Findings from a participatory evaluation of a smart home application for older adults. , 2008, Technology and health care : official journal of the European Society for Engineering and Medicine.

[89]  Qiang Yang,et al.  Sensor-Based Abnormal Human-Activity Detection , 2008, IEEE Transactions on Knowledge and Data Engineering.

[90]  Jian Lu,et al.  An unsupervised approach to activity recognition and segmentation based on object-use fingerprints , 2010, Data Knowl. Eng..

[91]  B. Prabhakaran,et al.  Video Human Motion Recognition Using a Knowledge-Based Hybrid Method Based on a Hidden Markov Model , 2012, TIST.

[92]  Andreas Savvides,et al.  Recognizing activities from context and arm pose using finite state machines , 2009, 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC).

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

[94]  Marjorie Skubic,et al.  Pulse rate estimation using hydraulic bed sensor , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[95]  Alex Pentland,et al.  Real-time American Sign Language recognition from video using hidden Markov models , 1995 .

[96]  Sajal K. Das,et al.  Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing) , 2004 .

[97]  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.

[98]  Diane J. Cook,et al.  Learning Setting-Generalized Activity Models for Smart Spaces , 2012, IEEE Intelligent Systems.

[99]  Prashant J. Shenoy,et al.  SmartCap: Flattening peak electricity demand in smart homes , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[100]  Karen L. Courtney,et al.  Brief Review: Defining Obtrusiveness in Home Telehealth Technologies: A Conceptual Framework , 2006, J. Am. Medical Informatics Assoc..

[101]  Jake K. Aggarwal,et al.  Recognition of Composite Human Activities through Context-Free Grammar Based Representation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[102]  Context-Aware Computing,et al.  Inferring Activities from Interactions with Objects , 2004 .

[103]  Diane J. Cook,et al.  Predicting air quality in smart environments , 2010, J. Ambient Intell. Smart Environ..

[104]  Allen Y. Yang,et al.  Distributed recognition of human actions using wearable motion sensor networks , 2009, J. Ambient Intell. Smart Environ..

[105]  Hans W. Guesgen,et al.  Use Cases for Abnormal Behaviour Detection in Smart Homes , 2010, ICOST.

[106]  Timo Grimmer,et al.  Impairment of activities of daily living requiring memory or complex reasoning as part of the MCI syndrome , 2006, International journal of geriatric psychiatry.

[107]  Michael J. Laszlo,et al.  Minimum spanning tree partitioning algorithm for microaggregation , 2005, IEEE Transactions on Knowledge and Data Engineering.

[108]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[109]  Diane J. Cook,et al.  The user side of sustainability: Modeling behavior and energy usage in the home , 2013, Pervasive Mob. Comput..

[110]  Diane J. Cook,et al.  Improving home automation by discovering regularly occurring device usage patterns , 2003, Third IEEE International Conference on Data Mining.

[111]  Gregory D. Abowd,et al.  Designing for the Human Experience in Smart Environments , 2005 .

[112]  Wayne E. Baker,et al.  Achieving Success Through Social Capital , 2003 .

[113]  Blake Hannaford,et al.  A Hybrid Discriminative/Generative Approach for Modeling Human Activities , 2005, IJCAI.

[114]  Friedrich Foerster,et al.  Detection of posture and motion by accelerometry : a validation study in ambulatory monitoring , 1999 .

[115]  Oliver Brdiczka,et al.  Learning Situation Models in a Smart Home , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[116]  Prashant J. Shenoy,et al.  Combined heat and privacy: Preventing occupancy detection from smart meters , 2014, 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[117]  Miguel A. Labrador,et al.  Privacy, quality of information, and energy consumption in Participatory Sensing systems , 2014, 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[118]  Maureen Schmitter-Edgecombe,et al.  Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[119]  Michael Marsiske,et al.  The Revised Observed Tasks of Daily Living: A Performance-Based Assessment of Everyday Problem Solving in Older Adults , 2005, Journal of applied gerontology : the official journal of the Southern Gerontological Society.

[120]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.