Improving Human Activity Recognition and its Application in Early Stroke Diagnosis

The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices.

[1]  Yutaka Hata,et al.  Wearable Human Activity Recognition by Electrocardiograph and Accelerometer , 2013, 2013 IEEE 43rd International Symposium on Multiple-Valued Logic.

[2]  Nancy E Mayo,et al.  The Stroke Rehabilitation Assessment of Movement (STREAM): a comparison with other measures used to evaluate effects of stroke and rehabilitation. , 2003, Physical therapy.

[3]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[4]  Yen-Ping Chen,et al.  Online classifier construction algorithm for human activity detection using a tri-axial accelerometer , 2008, Appl. Math. Comput..

[5]  Francisco Herrera,et al.  Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..

[6]  Hojjat Adeli,et al.  Machine Learning: Neural Networks , 1994 .

[7]  Kristen Hollands,et al.  Whole body coordination during turning while walking in stroke survivors , 2010 .

[8]  Peter C. Fishburn,et al.  Going from theory to practice: the mixed success of approval voting , 2005, Soc. Choice Welf..

[9]  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).

[10]  M. Kaste,et al.  Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke. , 2008, The New England journal of medicine.

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

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

[13]  Shuangquan Wang,et al.  Human activity recognition with user-free accelerometers in the sensor networks , 2005, 2005 International Conference on Neural Networks and Brain.

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

[15]  María José del Jesús,et al.  Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems , 2001, Inf. Sci..

[16]  Debbie Rand,et al.  How Active Are People With Stroke?: Use of Accelerometers to Assess Physical Activity , 2009, Stroke.

[17]  Kurt Hornik,et al.  A Robust Subspace Algorithm for Principal Component Analysis , 2003, Int. J. Neural Syst..

[18]  Camelia Chira,et al.  Human Activity Recognition and Feature Selection for Stroke Early Diagnosis , 2013, HAIS.

[19]  Emilio Corchado,et al.  Meta-heuristic improvements applied for steel sheet incremental cold shaping , 2012, Memetic Computing.

[20]  Hojjat Adeli,et al.  Cost Optimization of Structures: Fuzzy Logic, Genetic Algorithms, and Parallel Computing , 2006 .

[21]  V. L. Nickel,et al.  Gait parameters following stroke: a practical assessment. , 1995, Journal of rehabilitation research and development.

[22]  G. Moneta,et al.  Thrombolysis with Alteplase 3 to 4.5 Hours after Acute Ischemic Stroke , 2009 .

[23]  Luciano Sánchez,et al.  Boosting fuzzy rules in classification problems under single‐winner inference , 2007, Int. J. Intell. Syst..

[24]  Kamal C. Sarma,et al.  FUZZY GENETIC ALGORITHM FOR OPTIMIZATION OF STEEL STRUCTURES , 2000 .

[25]  Faicel Chamroukhi,et al.  Joint segmentation of multivariate time series with hidden process regression for human activity recognition , 2013, Neurocomputing.

[26]  Mary G. George,et al.  An Updated Definition of Stroke for the 21st Century: A Statement for Healthcare Professionals From the American Heart Association/American Stroke Association , 2013, Stroke.

[27]  C.J. De Luca,et al.  A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Eliathamby Ambikairajah,et al.  Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. , 2006, Physiological measurement.

[29]  Sundaram Suresh,et al.  Human action recognition using Meta-Cognitive Neuro-Fuzzy Inference System , 2012, IJCNN.

[30]  Edward Sazonov,et al.  Using Sensors to Measure Activity in People with Stroke , 2011, Topics in stroke rehabilitation.

[31]  Ahmed Bouridane,et al.  2D and 3D palmprint information, PCA and HMM for an improved person recognition performance , 2013, Integr. Comput. Aided Eng..

[32]  B. G. Celler,et al.  Classification of basic daily movements using a triaxial accelerometer , 2004, Medical and Biological Engineering and Computing.

[33]  M Ahmed,et al.  Kinetics and Kinematics of Loading Response in Stroke Patients (A Review Article) , 2008 .

[34]  Camelia Chira,et al.  A Preliminary Study on Early Diagnosis of Illnesses Based on Activity Disturbances , 2013, DCAI.

[35]  T. Olsen,et al.  Arm and leg paresis as outcome predictors in stroke rehabilitation. , 1990, Stroke.

[36]  Adnan I. Qureshi,et al.  Guidelines for the Early Management of Adults With Ischemic Stroke , 2007 .

[37]  J. Saver.  Time Is Brain—Quantified , 2006, Stroke.

[38]  Jeen-Shing Wang,et al.  Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers , 2008, Pattern Recognit. Lett..

[39]  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.

[40]  V. Feigin,et al.  Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010 , 2014, The Lancet.

[41]  Jenq-Neng Hwang,et al.  A Review on Video-Based Human Activity Recognition , 2013, Comput..

[42]  Sundaram Suresh,et al.  Human action recognition using Meta-Cognitive Neuro-Fuzzy Inference System , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[43]  Randall T Higashida,et al.  Forecasting the Future of Stroke in the United States: A Policy Statement From the American Heart Association and American Stroke Association , 2013, Stroke.

[44]  Suraj Raghuraman,et al.  Exploring unconstrained mobile sensor based human activity recognition , 2013 .

[45]  Oscar Cordón,et al.  Body posture recognition by means of a genetic fuzzy finite state machine , 2011, 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS).

[46]  M. Akay,et al.  Quantitative Measures of Functional Upper Limb Movement in Persons after Stroke , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[47]  Mi Zhang,et al.  Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors , 2013, IEEE Journal of Biomedical and Health Informatics.

[48]  Oscar Cordón,et al.  Human Gait Modeling Using a Genetic Fuzzy Finite State Machine , 2012, IEEE Transactions on Fuzzy Systems.

[49]  A. B. Drought,et al.  WALKING PATTERNS OF NORMAL MEN. , 1964, The Journal of bone and joint surgery. American volume.

[50]  Jake K. Aggarwal,et al.  Human Activity Recognition , 2005, PReMI.

[51]  Nazmul Siddique,et al.  Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing , 2013 .

[52]  Birgitta Langhammer,et al.  Physical Therapy Tests in Stroke Rehabilitation , 2009 .

[53]  Plamen P. Angelov,et al.  Human Activity Recognition Based on Evolving Fuzzy Systems , 2010, Int. J. Neural Syst..

[54]  S. Simon,et al.  Gait Pattern in the Early Recovery Period after Stroke* , 1996, The Journal of bone and joint surgery. American volume.

[55]  W. Marsden.  I and J , 2012 .

[56]  Carlos Cotta,et al.  Memetic algorithms and memetic computing optimization: A literature review , 2012, Swarm Evol. Comput..