Pathological Gait Detection of Parkinson's Disease Using Sparse Representation

Parkinson's disease is a progressively degenerative neurological disorder which impacts the control of body movements. While there is no known permanent cure for the disorder, it is possible to monitor the progression and establish management regime that could help the medical team, patients and their family cope with the condition. Gait analysis becomes an attractive quantitative and non-invasive mechanism that can aid early detection and monitoring of the response of patients to the management schedules. In this paper, we model cycles of human gait as a sparsely represented signal using over-complete dictionary. This representation forms the basis of a classification that allows the recognition of symptomatic subjects. Experiments have been conducted using signals of vertical ground reaction force (GRF) from subjects with Parkinson's disease from the publicly available gait database (physionet.org). Our method achieved a classification accuracy of 83% in recognising pathological cases and represents a significant improvement on previously published results that use a selection of the Fourier transform coefficients as features.

[1]  S H Holzreiter,et al.  Assessment of gait patterns using neural networks. , 1993, Journal of biomechanics.

[2]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[3]  Masayoshi Tomizuka,et al.  Gait phase analysis based on a Hidden Markov Model , 2011 .

[4]  Jeffrey M. Hausdorff,et al.  Rhythmic auditory stimulation modulates gait variability in Parkinson's disease , 2007, The European journal of neuroscience.

[5]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[6]  Larry S. Davis,et al.  Learning a discriminative dictionary for sparse coding via label consistent K-SVD , 2011, CVPR 2011.

[7]  T Chau,et al.  A review of analytical techniques for gait data. Part 1: Fuzzy, statistical and fractal methods. , 2001, Gait & posture.

[8]  J. L. Astephen,et al.  Biomechanical features of gait waveform data associated with knee osteoarthritis: an application of principal component analysis. , 2007, Gait & posture.

[9]  Joarder Kamruzzaman,et al.  Support Vector Machines and Other Pattern Recognition Approaches to the Diagnosis of Cerebral Palsy Gait , 2006, IEEE Transactions on Biomedical Engineering.

[10]  Marimuthu Palaniswami,et al.  Automatic Recognition of Gait Patterns Exhibiting Patellofemoral Pain Syndrome Using a Support Vector Machine Approach , 2009, IEEE Transactions on Information Technology in Biomedicine.

[11]  Ludmila I. Kuncheva,et al.  Switching between selection and fusion in combining classifiers: an experiment , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Dieter Merkl,et al.  Analyzing human gait patterns for malfunction detection , 2000, SAC '00.

[14]  S. M. N. Arosha Senanayake,et al.  Computational Intelligent Gait-Phase Detection System to Identify Pathological Gait , 2010, IEEE Transactions on Information Technology in Biomedicine.

[15]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[16]  Jeffrey M. Hausdorff,et al.  Dual tasking, gait rhythmicity, and Parkinson's disease: Which aspects of gait are attention demanding? , 2005, The European journal of neuroscience.

[17]  Baoxin Li,et al.  Discriminative K-SVD for dictionary learning in face recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Robert P. W. Duin,et al.  A multi-classifier for grading knee osteoarthritis using gait analysis , 2010, Pattern Recognit. Lett..

[19]  Marimuthu Palaniswami,et al.  Computational Intelligence in Gait Research: A Perspective on Current Applications and Future Challenges , 2009, IEEE Transactions on Information Technology in Biomedicine.

[20]  Michael W. Whittle,et al.  Gait Analysis: An Introduction , 1986 .

[21]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[22]  Marimuthu Palaniswami,et al.  Support vector machines for automated gait classification , 2005, IEEE Transactions on Biomedical Engineering.

[23]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Sally McClean,et al.  A machine learning approach to assessing gait patterns for Complex Regional Pain Syndrome. , 2012, Medical engineering & physics.

[25]  Sheldon R Simon,et al.  Quantification of human motion: gait analysis-benefits and limitations to its application to clinical problems. , 2004, Journal of biomechanics.

[26]  Jeffrey M. Hausdorff,et al.  Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson's disease , 2005, Movement disorders : official journal of the Movement Disorder Society.