Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review
暂无分享,去创建一个
Po Yang | Jun Qi | Youbing Zhao | Zhikun Deng | Atif Waraich | Yun Yang | Po Yang | J. Qi | Yun Yang | Atif Waraich | Zhikun Deng | Youbing Zhao
[1] M. -. H. A. U. Ajdsp,et al. Accelerometer , 2020, Definitions.
[2] Andrej Zgank,et al. Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service , 2019, Sensors.
[3] Julián Colorado,et al. Wearable-Based Human Activity Recognition Using an IoT Approach , 2017, J. Sens. Actuator Networks.
[4] Julián Colorado,et al. IoT system for Human Activity Recognition using BioHarness 3 and Smartphone , 2017, ICFNDS.
[5] Mehdi Ammi,et al. Recognition of human activity using Internet of Things in a non-controlled environment , 2016, 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV).
[6] Ilkka Korhonen,et al. Wearable Monitoring of Physical Functioning and Disability Changes, Circadian Rhythms and Sleep Patterns in Nursing Home Residents , 2016, IEEE Journal of Biomedical and Health Informatics.
[7] He Jian,et al. A portable fall detection and alerting system based on k-NN algorithm and remote medicine , 2015, China Communications.
[8] Héctor Pomares,et al. mDurance: A Novel Mobile Health System to Support Trunk Endurance Assessment , 2015, Sensors.
[9] Shyamnath Gollakota,et al. Contactless Sleep Apnea Detection on Smartphones , 2015 .
[10] Pierluigi Casale,et al. Identifying Physical Activity Profiles in COPD Patients Using Topic Models , 2015, IEEE Journal of Biomedical and Health Informatics.
[11] Simon A. Dobson,et al. KCAR: A knowledge-driven approach for concurrent activity recognition , 2015, Pervasive Mob. Comput..
[12] Rob Miller,et al. Smart Homes that Monitor Breathing and Heart Rate , 2015, CHI.
[13] Alanson P. Sample,et al. IDSense: A Human Object Interaction Detection System Based on Passive UHF RFID , 2015, CHI.
[14] Shwetak N. Patel,et al. WiBreathe: Estimating respiration rate using wireless signals in natural settings in the home , 2015, 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[15] Héctor Pomares,et al. mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications , 2014, IWAAL.
[16] Lars Thomas Boye,et al. NFC based provisioning of instructional videos to assist with instrumental activities of daily living , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[17] Changseok Bae,et al. Unsupervised learning for human activity recognition using smartphone sensors , 2014, Expert Syst. Appl..
[18] Xi Zhao,et al. Continuous fine‐grained arm action recognition using motion spectrum mixture models , 2014, Electronics Letters.
[19] Wenyao Xu,et al. Designing a Robust Activity Recognition Framework for Health and Exergaming Using Wearable Sensors , 2014, IEEE Journal of Biomedical and Health Informatics.
[20] Chris D. Nugent,et al. A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors , 2014, Sensors.
[21] Yujie Dong,et al. Detecting Periods of Eating During Free-Living by Tracking Wrist Motion , 2014, IEEE Journal of Biomedical and Health Informatics.
[22] Norbert Noury,et al. Characterization of Physical Activity in COPD Patients: Validation of a Robust Algorithm for Actigraphic Measurements in Living Situations , 2014, IEEE Journal of Biomedical and Health Informatics.
[23] Paul J. M. Havinga,et al. Fusion of Smartphone Motion Sensors for Physical Activity Recognition , 2014, Sensors.
[24] A Moncada-Torres,et al. Activity classification based on inertial and barometric pressure sensors at different anatomical locations , 2014, Physiological measurement.
[25] Shane A Lowe,et al. Monitoring human health behaviour in one's living environment: a technological review. , 2014, Medical engineering & physics.
[26] Diane J. Cook,et al. Activity recognition on streaming sensor data , 2014, Pervasive Mob. Comput..
[27] Liming Chen,et al. Dynamic sensor data segmentation for real-time knowledge-driven activity recognition , 2014, Pervasive Mob. Comput..
[28] 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.
[29] Oliver Amft,et al. COPDTrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback , 2013, UbiComp.
[30] Angelo Cappello,et al. Quantitative Description of the Lie-to-Sit-to-Stand-to-Walk Transfer by a Single Body-Fixed Sensor , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[31] Faicel Chamroukhi,et al. An Unsupervised Approach for Automatic Activity Recognition Based on Hidden Markov Model Regression , 2013, IEEE Transactions on Automation Science and Engineering.
[32] Hongnian Yu,et al. Elderly activities recognition and classification for applications in assisted living , 2013, Expert Syst. Appl..
[33] A. Bernardos,et al. Activity logging using lightweight classification techniques in mobile devices , 2013, Personal and Ubiquitous Computing.
[34] Seungmin Rho,et al. Physical activity recognition using multiple sensors embedded in a wearable device , 2013, TECS.
[35] Davide Anguita,et al. Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine , 2012, IWAAL.
[36] M. Buranarach,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.
[37] James McNames,et al. Shoulder and Elbow Joint Angle Tracking With Inertial Sensors , 2012, IEEE Transactions on Biomedical Engineering.
[38] Javier Reina-Tosina,et al. SoM: A Smart Sensor for Human Activity Monitoring and Assisted Healthy Ageing , 2012, IEEE Transactions on Biomedical Engineering.
[39] Vigneshwaran Subbaraju,et al. Energy-Efficient Continuous Activity Recognition on Mobile Phones: An Activity-Adaptive Approach , 2012, 2012 16th International Symposium on Wearable Computers.
[40] C. Nugent,et al. A Knowledge-Driven Approach to Activity Recognition in Smart Homes , 2012, IEEE Transactions on Knowledge and Data Engineering.
[41] Hans-Werner Gellersen,et al. Multimodal recognition of reading activity in transit using body-worn sensors , 2012, TAP.
[42] Robert X. Gao,et al. Multisensor Data Fusion for Physical Activity Assessment , 2012, IEEE Transactions on Biomedical Engineering.
[43] Jung-Keun Lee,et al. Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions , 2012, IEEE Transactions on Instrumentation and Measurement.
[44] A K Bourke,et al. Activity classification using a single chest mounted tri-axial accelerometer. , 2011, Medical engineering & physics.
[45] L. Kilmartin,et al. Accurate monitoring of human physical activity levels for medical diagnosis and monitoring using off-the-shelf cellular handsets , 2011, Personal and Ubiquitous Computing.
[46] Gerhard Tröster,et al. The adARC pattern analysis architecture for adaptive human activity recognition systems , 2011, Journal of Ambient Intelligence and Humanized Computing.
[47] Alberto G. Bonomi,et al. Identifying Types of Physical Activity With a Single Accelerometer: Evaluating Laboratory-trained Algorithms in Daily Life , 2011, IEEE Transactions on Biomedical Engineering.
[48] Takuya Maekawa,et al. Unsupervised Activity Recognition with User's Physical Characteristics Data , 2011, 2011 15th Annual International Symposium on Wearable Computers.
[49] P. Pasquina,et al. Sensor technology for smart homes. , 2011, Maturitas.
[50] Sung-Bae Cho,et al. Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer , 2011, HAIS.
[51] Gary M. Weiss,et al. Activity recognition using cell phone accelerometers , 2011, SKDD.
[52] Tae-Seong Kim,et al. Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly , 2010, Medical & Biological Engineering & Computing.
[53] Sung-Bae Cho,et al. A Mobile Context Sharing System Using Activity and Emotion Recognition with Bayesian Networks , 2010, 2010 7th International Conference on Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing.
[54] Tae-Seong Kim,et al. A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer , 2010, IEEE Transactions on Information Technology in Biomedicine.
[55] S. Cerutti,et al. Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[56] Rong Jin,et al. Understanding bag-of-words model: a statistical framework , 2010, Int. J. Mach. Learn. Cybern..
[57] Simon A. Dobson,et al. Activity recognition using temporal evidence theory , 2010, J. Ambient Intell. Smart Environ..
[58] Urbashi Mitra,et al. Multimodal Physical Activity Recognition by Fusing Temporal and Cepstral Information , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[59] Sethuraman Panchanathan,et al. Activity gesture spotting using a threshold model based on Adaptive Boosting , 2010, 2010 IEEE International Conference on Multimedia and Expo.
[60] Simon A. Dobson,et al. Using Ontologies in Case-Based Activity Recognition , 2010, FLAIRS.
[61] Ig-Jae Kim,et al. Mobile health monitoring system based on activity recognition using accelerometer , 2010, Simul. Model. Pract. Theory.
[62] Ganesh R. Naik,et al. Twin SVM for Gesture Classification Using the Surface Electromyogram , 2010, IEEE Transactions on Information Technology in Biomedicine.
[63] Huosheng Hu,et al. Reducing Drifts in the Inertial Measurements of Wrist and Elbow Positions , 2010, IEEE Transactions on Instrumentation and Measurement.
[64] Tao Gu,et al. Object relevance weight pattern mining for activity recognition and segmentation , 2010, Pervasive Mob. Comput..
[65] Deborah Estrin,et al. Using mobile phones to determine transportation modes , 2010, TOSN.
[66] Bernt Schiele,et al. Enabling Efficient Time Series Analysis for Wearable Activity Data , 2009, 2009 International Conference on Machine Learning and Applications.
[67] 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.
[68] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[69] John Staudenmayer,et al. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. , 2009, Journal of applied physiology.
[70] Kristof Van Laerhoven,et al. When Else Did This Happen? Efficient Subsequence Representation and Matching for Wearable Activity Data , 2009, 2009 International Symposium on Wearable Computers.
[71] Li-Chen Fu,et al. Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home , 2009, IEEE Transactions on Automation Science and Engineering.
[72] Daqing Zhang,et al. Gesture Recognition with a 3-D Accelerometer , 2009, UIC.
[73] Bernt Schiele,et al. Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning , 2009, LoCA.
[74] Sasiwan Paiyarom,et al. Activity monitoring system using Dynamic Time Warping for the elderly and disabled people , 2009, 2009 2nd International Conference on Computer, Control and Communication.
[75] Jeen-Shing Wang,et al. Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers , 2008, Pattern Recognit. Lett..
[76] Stelios C. A. Thomopoulos,et al. An indoor localization platform for ambient assisted living using UWB , 2008, MoMM.
[77] Bernt Schiele,et al. Exploring semi-supervised and active learning for activity recognition , 2008, 2008 12th IEEE International Symposium on Wearable Computers.
[78] Gwenn Englebienne,et al. Accurate activity recognition in a home setting , 2008, UbiComp.
[79] Lian-Wen Jin,et al. Activity recognition from acceleration data using AR model representation and SVM , 2008, 2008 International Conference on Machine Learning and Cybernetics.
[80] Paul Lukowicz,et al. Gesture spotting with body-worn inertial sensors to detect user activities , 2008, Pattern Recognit..
[81] Ehud Sharlin,et al. Exploring the use of tangible user interfaces for human-robot interaction: a comparative study , 2008, CHI.
[82] Shuwan Xue,et al. Portable Preimpact Fall Detector With Inertial Sensors , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[83] Paul Lukowicz,et al. Rapid Prototyping of Activity Recognition Applications , 2008, IEEE Pervasive Computing.
[84] James A. Landay,et al. The Mobile Sensing Platform: An Embedded Activity Recognition System , 2008, IEEE Pervasive Computing.
[85] J. Giuffrida,et al. Upper-Extremity Stroke Therapy Task Discrimination Using Motion Sensors and Electromyography , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[86] Wan Young Chung,et al. Activity monitoring from real-time triaxial accelerometer data using sensor network , 2007, 2007 International Conference on Control, Automation and Systems.
[87] James M. Rehg,et al. A Scalable Approach to Activity Recognition based on Object Use , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[88] Maryam Mahdaviani,et al. Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition , 2007, NIPS.
[89] Jing Liu,et al. Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[90] P. Caselli,et al. Classification of Motor Activities through Derivative Dynamic Time Warping applied on Accelerometer Data , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[91] Kent Larson,et al. Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor , 2007, 2007 11th IEEE International Symposium on Wearable Computers.
[92] Jennifer Healey,et al. A Long-Term Evaluation of Sensing Modalities for Activity Recognition , 2007, UbiComp.
[93] Donghai Guan,et al. Activity Recognition Based on Semi-supervised Learning , 2007, 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2007).
[94] Gerhard Tröster,et al. On-body activity recognition in a dynamic sensor network , 2007, BODYNETS.
[95] Federica Paganelli,et al. An Ontology-Based Context Model for Home Health Monitoring and Alerting in Chronic Patient Care Networks , 2007, 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07).
[96] Ramakant Nevatia,et al. Coupled Hidden Semi Markov Models for Activity Recognition , 2007, 2007 IEEE Workshop on Motion and Video Computing (WMVC'07).
[97] Kamiar Aminian,et al. Quantification of Tremor and Bradykinesia in Parkinson's Disease Using a Novel Ambulatory Monitoring System , 2007, IEEE Transactions on Biomedical Engineering.
[98] Paul Lukowicz,et al. Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[99] 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).
[100] Anthony Rowe,et al. eWatch: a wearable sensor and notification platform , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).
[101] Kamiar Aminian,et al. Stair climbing detection during daily physical activity using a miniature gyroscope. , 2005, Gait & posture.
[102] Blake Hannaford,et al. A Hybrid Discriminative/Generative Approach for Modeling Human Activities , 2005, IJCAI.
[103] Kamiar Aminian,et al. A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes , 2005, IEEE Transactions on Biomedical Engineering.
[104] Matthai Philipose,et al. Unsupervised Activity Recognition Using Automatically Mined Common Sense , 2005, AAAI.
[105] Michael L. Littman,et al. Activity Recognition from Accelerometer Data , 2005, AAAI.
[106] Paul Lukowicz,et al. Recognizing and Discovering Human Actions from On-Body Sensor Data , 2005, 2005 IEEE International Conference on Multimedia and Expo.
[107] G M Lyons,et al. A description of an accelerometer-based mobility monitoring technique. , 2005, Medical engineering & physics.
[108] Svetha Venkatesh,et al. Activity recognition and abnormality detection with the switching hidden semi-Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[109] Henry A. Kautz,et al. Inferring activities from interactions with objects , 2004, IEEE Pervasive Computing.
[110] 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..
[111] Ling Bao,et al. Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.
[112] Kent Larson,et al. Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.
[113] P.H. Veltink,et al. Inclination measurement of human movement using a 3-D accelerometer with autocalibration , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[114] Albrecht Schmidt,et al. Multi-sensor Activity Context Detection for Wearable Computing , 2003, EUSAI.
[115] Kamiar Aminian,et al. Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly , 2003, IEEE Transactions on Biomedical Engineering.
[116] M. Akay,et al. Discrimination of walking patterns using wavelet-based fractal analysis , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[117] Kevin J Deluzio,et al. Knee and hip kinetics during normal stair climbing. , 2002, Gait & posture.
[118] Tapio Seppänen,et al. Recognizing human motion with multiple acceleration sensors , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).
[119] M.R. Popovic,et al. A reliable gait phase detection system , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[120] Andrew McCallum,et al. Maximum Entropy Markov Models for Information Extraction and Segmentation , 2000, ICML.
[121] T Togawa,et al. Classification of waist-acceleration signals in a continuous walking record. , 2000, Medical engineering & physics.
[122] B. Scholkopf,et al. Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).
[123] Johan A. K. Suykens,et al. Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.
[124] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.
[125] J. Fahrenberg,et al. Assessment of posture and motion by multichannel piezoresistive accelerometer recordings. , 1997, Psychophysiology.
[126] P H Veltink,et al. Detection of static and dynamic activities using uniaxial accelerometers. , 1996, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[127] O. Rioul,et al. Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.
[128] David A. Landgrebe,et al. A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..
[129] J E Hewett,et al. Efficacy of physical conditioning exercise in patients with rheumatoid arthritis and osteoarthritis. , 1989, Arthritis and rheumatism.
[130] G. Guyatt,et al. The 6-minute walk: a new measure of exercise capacity in patients with chronic heart failure. , 1985, Canadian Medical Association journal.
[131] C. Caspersen,et al. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. , 1985, Public health reports.
[132] P. Jeyanthi,et al. A Smart Phone-Base Pocket Fall Accident Detection, Positioning, and Rescue System. , 2016 .
[133] 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..
[134] Davide Anguita,et al. Transition-Aware Human Activity Recognition Using Smartphones , 2016, Neurocomputing.
[135] M. Altini,et al. Estimating Energy Expenditure Using Body-Worn Accelerometers: A Comparison of Methods, Sensors Number and Positioning , 2015, IEEE Journal of Biomedical and Health Informatics.
[136] M. Gams,et al. Dynamic signal segmentation for activity recognition , 2011 .
[137] Diane J. Cook,et al. Human Activity Recognition and Pattern Discovery , 2010, IEEE Pervasive Computing.
[138] Gwenn Englebienne,et al. UvA-DARE ( Digital Academic Repository ) Activity recognition using semi-Markov models on real world smart home datasets , 2010 .
[139] E. G. Rajan,et al. Rajan Transform and its uses in Pattern Recognition , 2009, Informatica.
[140] Mitja Lustrek,et al. Fall Detection and Activity Recognition with Machine Learning , 2009, Informatica.
[141] Montse Pardàs,et al. Activity Classification , 2009, Computers in the Human Interaction Loop.
[142] Ilkka Korhonen,et al. Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions , 2008, IEEE Transactions on Information Technology in Biomedicine.
[143] Johannes Peltola,et al. Activity classification using realistic data from wearable sensors , 2006, IEEE Transactions on Information Technology in Biomedicine.
[144] Nigel H. Lovell,et al. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring , 2006, IEEE Transactions on Information Technology in Biomedicine.
[145] Bernt Schiele,et al. Towards Less Supervision in Activity Recognition from Wearable Sensors , 2006, 2006 10th IEEE International Symposium on Wearable Computers.
[146] S. Venkatesh,et al. Online Context Recognition in Multisensor Systems using Dynamic Time Warping , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.
[147] Eamonn J. Keogh,et al. Segmenting Time Series: A Survey and Novel Approach , 2002 .
[148] Karen Zita Haigh,et al. Learning Models of Human Behaviour with Sequential Patterns , 2002 .
[149] J. T. Lassiter. Ambulatory cardiac monitoring. , 1982, Medical electronics.
[150] Filip De Turck,et al. Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .
[151] Nils Y. Hammerla,et al. Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing the Mobile Fitness Coach: towards Individualized Skill Assessment Using Personalized Mobile Devices , 2022 .