Design of a neuromuscular disorders diagnostic system using human movement analysis

This communication summarizes the outcome of our research program on the design of a diagnostic system for neuromuscular disorders based on the analysis of human movement using the Kinematic Theory of Rapid Human Movements. Herein, this design problem is split in sub-problems which are then described. The solutions adopted at each design step are explained. As an example of application, typical results obtained so far for the assessment of the most important modifiable risk factors of brain stroke (diabetes, hypertension, hypercholesterolemia, obesity, cardiac problems, and cigarette smoking) are reported by the means of the area under the receiver operating characteristic curve (AUC).

[1]  Boualem Boashash,et al.  Calibration of time features and frequency features in the time-frequency domain for improved detection and classification of seizure in newborn EEG signals , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[2]  G E Stelmach,et al.  Parkinson’s disease patients undershoot target size in handwriting and similar tasks , 2003, Journal of neurology, neurosurgery, and psychiatry.

[3]  Mohamed Cheriet,et al.  A new framework for online sketch-based image retrieval in web environment , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[4]  René Wamkeue,et al.  Performance of SVM algorithm implemented on fixed point ADSP-BF527 , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[5]  Réjean Plamondon,et al.  A New Algorithm and System for the Characterization of Handwriting Strokes with Delta-Lognormal Parameters , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Cornel Ioana,et al.  Using wide band signals for obstacle path correction in acoustic scintillation flow meters , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[7]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[8]  Khalid Jamil,et al.  On modeling and hardware implementation of Space-Time Adaptive Processing (STAP) for target detection in passive BI-static radar , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[9]  G. Stelmach,et al.  Parkinsonism Reduces Coordination of Fingers, Wrist, and Arm in Fine Motor Control , 1997, Experimental Neurology.

[10]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  R. Plamondon,et al.  Impact of the principal stroke risk factors on human movements. , 2011, Human movement science.

[12]  Madalina Boureanu.  Canada (Canada) , 2011 .

[13]  Réjean Plamondon,et al.  A kinematic theory of rapid human movements , 1995, Biological Cybernetics.

[14]  Réjean Plamondon,et al.  A kinematic theory of rapid human movements , 1995, Biological Cybernetics.

[15]  Christian O'Reilly,et al.  Prototype-Based Methodology for the Statistical Analysis of Local Features in Stereotypical Handwriting Tasks , 2010, 2010 20th International Conference on Pattern Recognition.

[16]  R. Duncan Luce,et al.  Response Times: Their Role in Inferring Elementary Mental Organization , 1986 .

[17]  A. Hanks.  Canada , 2002 .

[18]  Neville Hogan,et al.  Avoiding spurious submovement decompositions: a globally optimal algorithm , 2003, Biological Cybernetics.

[19]  Hadi Veisi,et al.  The effect of phase information in speech enhancement and speech recognition , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[20]  B. Tabachnick,et al.  Using multivariate statistics, 5th ed. , 2007 .

[21]  Hans-Leo Teulings,et al.  Handwriting movement kinematics for quantifying extrapyramidal side effects in patients treated with atypical antipsychotics , 2010, Psychiatry Research.

[22]  Réjean Plamondon,et al.  Development of a Sigma-Lognormal representation for on-line signatures , 2009, Pattern Recognit..

[23]  N. Hogan,et al.  Submovements grow larger, fewer, and more blended during stroke recovery. , 2003, Motor control.

[24]  N. Hogan,et al.  Movement Smoothness Changes during Stroke Recovery , 2002, The Journal of Neuroscience.

[25]  Réjean Plamondon,et al.  A kinematic theory of rapid human movement. Part IV: a formal mathematical proof and new insights , 2003, Biological Cybernetics.

[26]  P. Fitts,et al.  INFORMATION CAPACITY OF DISCRETE MOTOR RESPONSES. , 1964, Journal of experimental psychology.

[27]  Réjean Plamondon,et al.  Can computer mice be used as low-cost devices for the acquisition of planar human movement velocity signals? , 2011, Behavior research methods.

[28]  Abderrahmane Amrouche,et al.  Automatic speaker recognition for mobile communications using AMR-WB speech coding , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[29]  H. Möller,et al.  Kinematic Analysis of Handwriting Movements in Patients with Alzheimer’s Disease, Mild Cognitive Impairment, Depression and Healthy Subjects , 2003, Dementia and Geriatric Cognitive Disorders.

[30]  Réjean Plamondon,et al.  A Globally Optimal Estimator for the Delta-Lognormal Modeling of Fast Reaching Movements , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  Mohamed Cheriet,et al.  Automated intrusion attack with permanent control: Analysis and countermeasures , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[32]  Jin H. Yan,et al.  Alzheimer's disease and mild cognitive impairment deteriorate fine movement control. , 2008, Journal of psychiatric research.

[33]  Fadi Fayad,et al.  Object-to-track association in a multisensor fusion system under the TBM framework , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[34]  Chakib Tadj,et al.  Newborn's pathological cry identification system , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[35]  Neville Hogan,et al.  Avoiding Spurious Submovement Decompositions II: A Scattershot Algorithm , 2006, Biological Cybernetics.

[36]  B. Bhakta,et al.  Measuring movement irregularity in the upper motor neurone syndrome using normalised average rectified jerk. , 2003, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[37]  R Plamondon,et al.  Studying the variability of handwriting patterns using the Kinematic Theory. , 2009, Human movement science.

[38]  C. O'Reilly.  Développement d'outils d’analyse de la motricité fine pour l’investigation de troubles neuromusculaires : théorie, prototype et mise en application dans le contexte des accidents vasculaires cérébraux , 2012 .

[39]  R A Heath,et al.  Spiral drawing performance as an indicator of fine motor function in people with multiple sclerosis. , 2006, Human movement science.

[40]  Erik Blasch,et al.  Orbital satellite pursuit-evasion game-theoretical control , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).