Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer
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[1] Tim Dallas,et al. Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer , 2014, IEEE Transactions on Biomedical Engineering.
[2] Cagatay Catal,et al. On the use of ensemble of classifiers for accelerometer-based activity recognition , 2015, Appl. Soft Comput..
[3] Arkady B. Zaslavsky,et al. Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.
[4] K. I-K Wang,et al. Dynamic sliding window method for physical activity recognition using a single tri-axial accelerometer , 2015, 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA).
[5] Ifeyinwa E. Achumba,et al. Sensor Data Acquisition and Processing Parameters for Human Activity Classification , 2014, Sensors.
[6] H.J. Stam,et al. Automated estimation of initial and terminal contact timing using accelerometers; development and validation in transtibial amputees and controls , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[7] Ling Bao,et al. Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.
[8] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[9] Christopher J. James,et al. Wrist-Worn Accelerometer to Detect Postural Transitions and Walking Patterns , 2014 .
[10] David Howard,et al. A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data , 2009, IEEE Transactions on Biomedical Engineering.
[11] Wendong Xiao,et al. Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine , 2015 .
[12] M. Mancini,et al. Sit-stand and stand-sit transitions in older adults and patients with Parkinson’s disease: event detection based on motion sensors versus force plates , 2012, Journal of NeuroEngineering and Rehabilitation.
[13] R. Adaskevicius,et al. Method for Recognition of the Physical Activity of Human Being Using a Wearable Accelerometer , 2014 .
[14] Kenneth Meijer,et al. Activity identification using body-mounted sensors—a review of classification techniques , 2009, Physiological measurement.
[15] Jorge Dias,et al. Trajectory-based human action segmentation , 2015, Pattern Recognit..
[16] P H Peeters,et al. Design criteria for an automatic safety-alarm system for elderly. , 2000, Technology and health care : official journal of the European Society for Engineering and Medicine.
[17] Billur Barshan,et al. Detecting Falls with Wearable Sensors Using Machine Learning Techniques , 2014, Sensors.
[18] Shaojun Zhang,et al. An adaptive time window method for human activity recognition , 2015, CCECE.
[19] M. Gams,et al. Dynamic signal segmentation for activity recognition , 2011 .
[20] Iván Pau,et al. The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development , 2015, Sensors.
[21] Richard W. Bohannon,et al. Relationship of knee extension force to independence in sit-to-stand performance in patients receiving acute rehabilitation. , 2003, Physical therapy.
[22] J. Allum,et al. Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. , 2006, Gait & posture.
[23] M. N. Nyan,et al. Classification of gait patterns in the time-frequency domain. , 2006, Journal of biomechanics.
[24] Rémi Ronfard,et al. A survey of vision-based methods for action representation, segmentation and recognition , 2011, Comput. Vis. Image Underst..
[25] Ricard Gavaldà,et al. Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.
[26] Héctor Pomares,et al. Window Size Impact in Human Activity Recognition , 2014, Sensors.
[27] Héctor Pomares,et al. Evaluating the effects of signal segmentation on activity recognition , 2014, IWBBIO.
[28] Silvia Conforto,et al. Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer. , 2015, Medical engineering & physics.
[29] Eva Negri,et al. Risk factors for falls in older people in nursing homes and hospitals. A systematic review and meta-analysis. , 2013, Archives of gerontology and geriatrics.
[30] Hristijan Gjoreski,et al. Three-layer Activity Recognition Combining Domain Knowledge and Meta-classification , 2013 .
[31] Shuangquan Wang,et al. b-COELM: A fast, lightweight and accurate activity recognition model for mini-wearable devices , 2014, Pervasive Mob. Comput..
[32] 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.
[33] K. Aminian,et al. Temporal feature estimation during walking using miniature accelerometers: an analysis of gait improvement after hip arthroplasty , 1999, Medical & Biological Engineering & Computing.
[34] Lei Gao,et al. Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. , 2014, Medical engineering & physics.
[35] Rafael Morales Bueno,et al. Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners , 2007, J. Mach. Learn. Res..
[36] Paul Müller,et al. Ambient Intelligence in Assisted Living: Enable Elderly People to Handle Future Interfaces , 2007, HCI.
[37] Clemens Becker,et al. Epidemiology of falls in residential aged care: analysis of more than 70,000 falls from residents of bavarian nursing homes. , 2012, Journal of the American Medical Directors Association.
[38] Bin Liu,et al. Human daily activity recognition by fusing accelerometer and multi-lead ECG data , 2013, 2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013).
[39] Nicholas Wickström,et al. A Symbol-Based Approach to Gait Analysis From Acceleration Signals: Identification and Detection of Gait Events and a New Measure of Gait Symmetry , 2010, IEEE Transactions on Information Technology in Biomedicine.
[40] 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.
[41] L. Nyberg,et al. Patient falls in stroke rehabilitation. A challenge to rehabilitation strategies. , 1995, Stroke.
[42] Jung-Hsien Chiang,et al. Pattern analysis in daily physical activity data for personal health management , 2014, Pervasive Mob. Comput..
[43] Michelle McKinney. Ageing and Life Course , 2011 .
[44] Kamiar Aminian,et al. Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. , 2002, Journal of biomechanics.
[45] Jesse Hoey,et al. Sensor-Based Activity Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[46] Kamiar Aminian,et al. Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly , 2002, IEEE Transactions on Biomedical Engineering.
[47] Minoru Yoshizawa,et al. Parameter exploration for response time reduction in accelerometer-based activity recognition , 2013, UbiComp.
[48] Davide Anguita,et al. Transition-Aware Human Activity Recognition Using Smartphones , 2016, Neurocomputing.
[49] T Togawa,et al. Classification of waist-acceleration signals in a continuous walking record. , 2000, Medical engineering & physics.
[50] B. Unver,et al. ABility to rise independently from a chair during 6-month follow-up after unilateral and bilateral total knee replacement. , 2005, Journal of rehabilitation medicine.
[51] Yuting Zhang,et al. Continuous functional activity monitoring based on wearable tri-axial accelerometer and gyroscope , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.
[52] Zhenyu He,et al. Activity recognition from accelerometer signals based on Wavelet-AR model , 2010, 2010 IEEE International Conference on Progress in Informatics and Computing.
[53] Sajal K. Das,et al. Multimodal Wearable Sensing for Fine-Grained Activity Recognition in Healthcare , 2015, IEEE Internet Computing.
[54] Weng-Keen Wong,et al. Machine learning for activity recognition: hip versus wrist data , 2014, Physiological measurement.
[55] G. Pyka,et al. Effect of muscle strength and movement speed on the biomechanics of rising from a chair in healthy elderly and young women. , 1998, Gait & posture.
[56] A. C. Rencher. Methods of multivariate analysis , 1995 .
[57] LarsNyberg,et al. Patient Falls in Stroke Rehabilitation , 1995 .
[58] Luca Benini,et al. Wearable assistant for load monitoring: recognition of on—body load placement from gait alterations , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.
[59] Baozhi Chen,et al. Research challenges in computation, communication, and context awareness for ubiquitous healthcare , 2012, IEEE Communications Magazine.
[60] Miguel A. Labrador,et al. A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.
[61] Guang-Zhong Yang,et al. Sensor Positioning for Activity Recognition Using Wearable Accelerometers , 2011, IEEE Transactions on Biomedical Circuits and Systems.