Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection
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Diane J. Cook | Samaneh Aminikhanghahi | Cristian Culman | D. Cook | S. Aminikhanghahi | Cristian Culman
[1] Diane J. Cook,et al. A survey of methods for time series change point detection , 2017, Knowledge and Information Systems.
[2] Munoz-Organero Mario,et al. Human Activity Recognition Based on Single Sensor Square HV Acceleration Images and Convolutional Neural Networks , 2019, IEEE Sensors Journal.
[3] R. B. Deshmukh,et al. A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications , 2018, IEEE Access.
[4] R. Amutha,et al. A novel chaotic map based compressive classification scheme for human activity recognition using a tri-axial accelerometer , 2018, Multimedia Tools and Applications.
[5] Valentina Camomilla,et al. Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review , 2018, Sensors.
[6] Vigneshwaran Subbaraju,et al. Energy-Efficient Continuous Activity Recognition on Mobile Phones: An Activity-Adaptive Approach , 2012, 2012 16th International Symposium on Wearable Computers.
[7] C Leurent,et al. Digital technologies for cognitive assessment to accelerate drug development in Alzheimer's disease , 2015, Clinical pharmacology and therapeutics.
[8] Ari Yair Barrera-Animas,et al. Monitoring Student Activities with Smartwatches: On the Academic Performance Enhancement , 2019, Sensors.
[9] Yi Wang,et al. Identifying activity boundaries for activity recognition in smart environments , 2016, 2016 IEEE International Conference on Communications (ICC).
[10] Minoru Yoshizawa,et al. Parameter exploration for response time reduction in accelerometer-based activity recognition , 2013, UbiComp.
[11] Janardhan Rao Doppa,et al. Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications , 2017, IEEE Transactions on Knowledge and Data Engineering.
[12] L. Mathew,et al. Increasing trend of wearables and multimodal interface for human activity monitoring: A review. , 2017, Biosensors & bioelectronics.
[13] Mo Li,et al. Memento: An Emotion Driven Lifelogging System with Wearables , 2017, 2017 26th International Conference on Computer Communication and Networks (ICCCN).
[14] Christian Haubelt,et al. Towards energy efficient sensor nodes for online activity recognition , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.
[15] Pierre Vandergheynst,et al. Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes , 2011, IEEE Transactions on Biomedical Engineering.
[16] Eamonn J. Keogh,et al. MDL-based time series clustering , 2012, Knowledge and Information Systems.
[17] Gabrielle M. Turner-McGrievy,et al. Choosing between responsive-design websites versus mobile apps for your mobile behavioral intervention: presenting four case studies , 2017, Translational behavioral medicine.
[18] Hassan Ghasemzadeh,et al. Toward Ultra-Low-Power Remote Health Monitoring: An Optimal and Adaptive Compressed Sensing Framework for Activity Recognition , 2019, IEEE Transactions on Mobile Computing.
[19] Abbes Amira,et al. Compressive Sensing-Based IoT Applications: A Review , 2018, J. Sens. Actuator Networks.
[20] Mahbub Hassan,et al. CapSense: Capacitor-based Activity Sensing for Kinetic Energy Harvesting Powered Wearable Devices , 2017, MobiQuitous.
[21] Davide Anguita,et al. Transition-Aware Human Activity Recognition Using Smartphones , 2016, Neurocomputing.
[22] Diane J. Cook,et al. Automated Detection of Activity Transitions for Prompting , 2015, IEEE Transactions on Human-Machine Systems.
[23] Zoran A. Salcic,et al. Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer , 2017, Pervasive Mob. Comput..
[24] Carla L Graf,et al. The Lawton Instrumental Activities of Daily Living Scale , 2008, The American journal of nursing.
[25] Samaneh Aminikhanghahi,et al. Real-Time Change Point Detection with Application to Smart Home Time Series Data , 2019, IEEE Transactions on Knowledge and Data Engineering.
[26] Ian Cleland,et al. Dynamic detection of window starting positions and its implementation within an activity recognition framework , 2016, J. Biomed. Informatics.
[27] Kenji Yamanishi,et al. A unifying framework for detecting outliers and change points from non-stationary time series data , 2002, KDD.
[28] Paul J. M. Havinga,et al. SmokeSense: Online Activity Recognition Framework on Smartwatches , 2018, MobiCASE.
[29] Michèle Basseville,et al. Detection of abrupt changes: theory and application , 1993 .
[30] Ümit Y. Ogras,et al. Online Human Activity Recognition using Low-Power Wearable Devices , 2018, 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).
[31] Zhetao Li,et al. Energy-Efficient Dynamic Computation Offloading and Cooperative Task Scheduling in Mobile Cloud Computing , 2019, IEEE Transactions on Mobile Computing.
[32] Jeongho Kwak,et al. Proximity-Aware Location Based Collaborative Sensing for Energy-Efficient Mobile Devices , 2019, IEEE Transactions on Mobile Computing.
[33] Lawrence B. Holder,et al. Thyme: Improving Smartphone Prompt Timing Through Activity Awareness , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).
[34] Yoshihiro Kawahara,et al. Compressed sensing method for human activity sensing using mobile phone accelerometers , 2012, 2012 Ninth International Conference on Networked Sensing (INSS).
[35] David Atienza,et al. A Wireless Body Sensor Network for Activity Monitoring with Low Transmission Overhead , 2014, 2014 12th IEEE International Conference on Embedded and Ubiquitous Computing.
[36] Diane J. Cook,et al. Enhancing activity recognition using CPD-based activity segmentation , 2019, Pervasive Mob. Comput..
[37] Hassan Ghasemzadeh,et al. Trading Off Power Consumption and Prediction Performance in Wearable Motion Sensors , 2018, ACM Trans. Design Autom. Electr. Syst..
[38] Sunwoong Choi,et al. Classification of Various Daily Activities using Convolution Neural Network and Smartwatch , 2018, 2018 IEEE International Conference on Big Data (Big Data).
[39] Younghee Lee,et al. Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user Activities , 2016, M4IoT@Middleware.
[40] Bernt Schiele,et al. A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.
[41] Niall Twomey,et al. On-Board Feature Extraction from Acceleration Data for Activity Recognition , 2018, EWSN.
[42] Sara Khalifa,et al. Energy-efficient human activity recognition for self-powered wearable devices , 2017, ACSW.
[43] Jian Tang,et al. Energy-efficient collaborative sensing with mobile phones , 2012, 2012 Proceedings IEEE INFOCOM.
[44] Miguel A. Labrador,et al. A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.
[45] Jay Stewart,et al. The American Time Use Survey , 2005 .
[46] Takafumi Kanamori,et al. A Least-squares Approach to Direct Importance Estimation , 2009, J. Mach. Learn. Res..
[47] Nirmalya Roy,et al. A smart segmentation technique towards improved infrequent non-speech gestural activity recognition model , 2017, Pervasive Mob. Comput..
[48] Hao Ma,et al. Human Gait Modeling and Analysis Using a Semi-Markov Process With Ground Reaction Forces , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[49] Eamonn J. Keogh,et al. Clustering Time Series Using Unsupervised-Shapelets , 2012, 2012 IEEE 12th International Conference on Data Mining.
[50] N. Alshurafa,et al. Battery Optimization in Remote Health Monitoring Systems to Enhance User Adherence , 2014 .
[51] Maureen Schmitter-Edgecombe,et al. Technology-Enabled Assessment of Functional Health , 2019, IEEE Reviews in Biomedical Engineering.
[52] Gabriel Tamura,et al. Characterizing context-aware recommender systems: A systematic literature review , 2018, Knowl. Based Syst..
[53] Hassan Ghasemzadeh,et al. Improving Compliance in Remote Healthcare Systems Through Smartphone Battery Optimization , 2015, IEEE Journal of Biomedical and Health Informatics.
[54] Majid Sarrafzadeh,et al. Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data , 2018, JMIR mHealth and uHealth.
[55] Sajal K. Das,et al. HuMAn: Complex Activity Recognition with Multi-Modal Multi-Positional Body Sensing , 2019, IEEE Transactions on Mobile Computing.
[56] Ryan P. Adams,et al. Bayesian Online Changepoint Detection , 2007, 0710.3742.
[57] Carl E. Rasmussen,et al. Gaussian Process Change Point Models , 2010, ICML.
[58] Le Song,et al. M-Statistic for Kernel Change-Point Detection , 2015, NIPS.
[59] Nigel Collier,et al. Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation , 2012, Neural Networks.
[60] Daniel P. Siewiorek,et al. Selective Sampling Strategies to Conserve Power in Context Aware Devices , 2007, 2007 11th IEEE International Symposium on Wearable Computers.
[61] Sriram Chellappan,et al. Watch-Dog: Detecting Self-Harming Activities From Wrist Worn Accelerometers , 2018, IEEE Journal of Biomedical and Health Informatics.
[62] Michael Beigl,et al. Energy-Efficient Activity Recognition Using Prediction , 2012, 2012 16th International Symposium on Wearable Computers.
[63] M. Kawanabe,et al. Direct importance estimation for covariate shift adaptation , 2008 .
[64] Tommi Mikkonen,et al. Early analysis of resource consumption patterns in mobile applications , 2017, Pervasive Mob. Comput..
[65] Mo Li,et al. Memento: An Emotion Driven Lifelogging System with Wearables , 2017, 2017 26th International Conference on Computer Communication and Networks (ICCCN).
[66] Amr Mohamed,et al. Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach , 2018, Diagnostics.
[67] Yoshinobu Kawahara,et al. Change-Point Detection in Time-Series Data Based on Subspace Identification , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).