Sensor-Based Activity Recognition Review

This chapter presents a comprehensive survey on the state of the art of various aspects of sensor-based activity recognition. It first examines the general rationale and distinctions of different sensor technologies for activity monitoring. Then we review the major approaches and methods associated with sensor-based activity modeling and recognition from which strengths and weaknesses of those approaches are analysed and highlighted. The survey makes a primary distinction between data-driven and knowledge-driven approaches, and uses this distinction to structure our survey.

[1]  Mubarak Shah,et al.  Ontology and taxonomy collaborated framework for meeting classification , 2004, ICPR 2004.

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

[3]  Friedrich Foerster,et al.  Motion pattern and posture: Correctly assessed by calibrated accelerometers , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

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

[5]  Kenji Mase,et al.  Activity and Location Recognition Using Wearable Sensors , 2002, IEEE Pervasive Comput..

[6]  Henry A. Kautz,et al.  An Overview of the Assisted Cognition Project , 2002 .

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

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

[9]  Monique Thonnat,et al.  A video interpretation platform applied to bank agency monitoring , 2004 .

[10]  Simon A. Dobson,et al.  A top-level ontology for smart environments , 2011, Pervasive Mob. Comput..

[11]  Albrecht Schmidt,et al.  There is more to context than location , 1999, Comput. Graph..

[12]  Eric Horvitz,et al.  Layered representations for learning and inferring office activity from multiple sensory channels , 2004, Comput. Vis. Image Underst..

[13]  Miodrag Potkonjak,et al.  Smart kindergarten: sensor-based wireless networks for smart developmental problem-solving environments , 2001, MobiCom '01.

[14]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[15]  Murray Shanahan.  Solving the frame problem - a mathematical investigation of the common sense law of inertia , 1997 .

[16]  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).

[17]  Wayne Wobcke,et al.  Two Logical Theories of Plan Recognition , 2002, J. Log. Comput..

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

[19]  Matthai Philipose,et al.  Mining models of human activities from the web , 2004, WWW '04.

[20]  Neal Lesh,et al.  Indoor navigation using a diverse set of cheap, wearable sensors , 1999, Digest of Papers. Third International Symposium on Wearable Computers.

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

[22]  Maxim A. Batalin,et al.  Demonstration of Active Guidance with SmartCane , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[23]  Jeff A. Bilmes,et al.  Structure Learning on Large Scale Common Sense Statistical Models of Human State , 2008, AAAI.

[24]  Paul Lukowicz,et al.  Wearable sensing to annotate meeting recordings , 2002, Proceedings. Sixth International Symposium on Wearable Computers,.

[25]  Jian Lu,et al.  epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[26]  Maryam Mahdaviani,et al.  Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition , 2007, NIPS.

[27]  Jesús Favela,et al.  Activity Recognition for the Smart Hospital , 2008, IEEE Intelligent Systems.

[28]  Ramakant Nevatia,et al.  VERL: An Ontology Framework for Representing and Annotating Video Events , 2005, IEEE Multim..

[29]  Koen De Bosschere,et al.  Towards an Extensible Context Ontology for Ambient Intelligence , 2004, EUSAI.

[30]  Ramakant Nevatia,et al.  An Ontology for Video Event Representation , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[31]  Thad Starner,et al.  Using GPS to learn significant locations and predict movement across multiple users , 2003, Personal and Ubiquitous Computing.

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

[33]  Hoi-Jun Yoo,et al.  Analysis of body sensor network using human body as the channel , 2008, BODYNETS.

[34]  Rama Chellappa,et al.  An ontology based approach for activity recognition from video , 2008, ACM Multimedia.

[35]  Claudio Bettini,et al.  OWL 2 modeling and reasoning with complex human activities , 2011, Pervasive Mob. Comput..

[36]  Paul Lukowicz,et al.  Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers , 2004, Pervasive.

[37]  Datong Chen,et al.  A Study of Detecting Social Interaction with Sensors in a Nursing Home Environment , 2005, ICCV-HCI.

[38]  Henry A. Kautz,et al.  Fine-grained activity recognition by aggregating abstract object usage , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

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

[40]  Ingrid Zukerman,et al.  Towards a Bayesian Model for Keyhole Plan Recognition in Large Domains , 1997 .

[41]  Emmanuel Munguia Tapia,et al.  Toward Scalable Activity Recognition for Sensor Networks , 2006, LoCA.

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

[43]  Tao Gu,et al.  Object relevance weight pattern mining for activity recognition and segmentation , 2010, Pervasive Mob. Comput..

[44]  Satoshi Tanaka,et al.  Applying Ontology and Probabilistic Model to Human Activity Recognition from Surrounding Things , 2007 .

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

[46]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[47]  Eric Horvitz,et al.  The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users , 1998, UAI.

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

[49]  Nikolaos G. Bourbakis,et al.  Wearable Obstacle Avoidance Electronic Travel Aids for Blind: A Survey , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[51]  Alex Pentland,et al.  Shiver motion and core body temperature classification for wearable soldier health monitoring systems , 2004, Eighth International Symposium on Wearable Computers.

[52]  Jeff Magee,et al.  Multi-sensor location tracking , 1998, MobiCom '98.

[53]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[54]  Bernard Lefebvre,et al.  Ontology-Based Management of the Telehealth Smart Home, Dedicated to Elderly in Loss of Cognitive Autonomy , 2007, OWLED.

[55]  Anthony Rowe,et al.  Location and Activity Recognition Using eWatch: A Wearable Sensor Platform , 2006, Ambient Intelligence in Everyday.

[56]  Albrecht Schmidt,et al.  Multi-Sensor Context-Awareness in Mobile Devices and Smart Artifacts , 2002, Mob. Networks Appl..

[57]  Henry A. Kautz,et al.  Hierarchical Conditional Random Fields for GPS-Based Activity Recognition , 2005, ISRR.

[58]  Henry A. Kautz,et al.  Integrating Sensing and Cueing for More Effective Activity Reminders , 2008, AAAI Fall Symposium: AI in Eldercare: New Solutions to Old Problems.

[59]  Gerhard Tröster,et al.  SMASH: a distributed sensing and processing garment for the classification of upper body postures , 2008, BODYNETS.

[60]  Qiang Yang,et al.  CIGAR: Concurrent and Interleaving Goal and Activity Recognition , 2008 .

[61]  Mario Cannataro,et al.  Protein-to-protein interactions: Technologies, databases, and algorithms , 2010, CSUR.

[62]  Matthai Philipose,et al.  Hands-on RFID: wireless wearables for detecting use of objects , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[63]  Kent Lyons,et al.  The Gesture Watch: A Wireless Contact-free Gesture based Wrist Interface , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[64]  John F. Canny,et al.  Modeling Human Behavior from Simple Sensors in the Home , 2006, Pervasive.

[65]  C. Randell,et al.  Context awareness by analysing accelerometer data , 2000, Digest of Papers. Fourth International Symposium on Wearable Computers.

[66]  Pascal Poupart,et al.  Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker , 2010, UAI.

[67]  Ingrid Zukerman,et al.  Bayesian Models for Keyhole Plan Recognition in an Adventure Game , 2004, User Modeling and User-Adapted Interaction.

[68]  Kristof Van Laerhoven,et al.  Real-time analysis of data from many sensors with neural networks , 2001, Proceedings Fifth International Symposium on Wearable Computers.

[69]  Karen Zita Haigh,et al.  Learning Models of Human Behaviour with Sequential Patterns , 2002 .

[70]  Martha E. Pollack,et al.  An 'Object-Use Fingerprint': The Use of Electronic Sensors for Human Identification , 2007, UbiComp.

[71]  James M. Rehg,et al.  A Scalable Approach to Activity Recognition based on Object Use , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[72]  Henry Kautz,et al.  Chapter 2 – A Formal Theory of Plan Recognition and its Implementation , 1991 .

[73]  Manuela M. Veloso,et al.  Conditional random fields for activity recognition , 2007, AAMAS '07.

[74]  Chris D. Nugent,et al.  Semantic Smart Homes: Towards Knowledge Rich Assisted Living Environments , 2009 .

[75]  John A. Quinn,et al.  Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[77]  Datong Chen,et al.  Towards automatic analysis of social interaction patterns in a nursing home environment from video , 2004, MIR '04.

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

[79]  C. Nugent,et al.  Experiences in the development of a Smart Lab , 2009 .

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

[81]  Dan Ding,et al.  Virtual Reality and Computer-Enhanced Training Applied to Wheeled Mobility: An Overview of Work in Pittsburgh , 2005, Assistive technology : the official journal of RESNA.