The classification for “equilibrium triad” sensory loss based on sEMG signals of calf muscles

Surface Electromyography (sEMG) has been commonly applied for analysing the electrical activities of skeletal muscles. The sensory system of maintaining posture balance includes vision, proprioception and vestibular senses. In this work, an attempt is made to classify whether the body is missing one of the sense during balance control by using sEMG signals. A trial of combination with different features and muscles is also developed. The results demonstrate that the classification accuracy between vision loss and the normal condition is higher than the one between vestibular sense loss and normal condition. When using different features and muscles, the impact on classification results is also different. The outcomes of this study could aid the development of sEMG based classification for the function of sensory systems during human balance movement.

[1]  T. Wredmark,et al.  Calf muscle atrophy and muscle function after non-operative vs operative treatment of achilles tendon ruptures. , 1986, Orthopedics.

[2]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

[3]  Mau-Roung Lin,et al.  Psychometric Comparisons of the Timed Up and Go, One‐Leg Stand, Functional Reach, and Tinetti Balance Measures in Community‐Dwelling Older People , 2004, Journal of the American Geriatrics Society.

[4]  Zhizhong Wang,et al.  Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification , 2007 .

[5]  M. Patel,et al.  The effect of foam surface properties on postural stability assessment while standing. , 2008, Gait & posture.

[6]  Marie-Françoise Lucas,et al.  Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization , 2008, Biomed. Signal Process. Control..

[7]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[8]  J. Allum,et al.  Identifying deficits in balance control following vestibular or proprioceptive loss using posturographic analysis of stance tasks , 2008, Clinical Neurophysiology.

[9]  E. Hansson,et al.  Effect of vision, proprioception, and the position of the vestibular organ on postural sway , 2010, Acta oto-laryngologica.

[10]  Carlo Menon,et al.  Surface EMG pattern recognition for real-time control of a wrist exoskeleton , 2010, Biomedical engineering online.

[11]  T. Kuiken,et al.  Quantifying Pattern Recognition—Based Myoelectric Control of Multifunctional Transradial Prostheses , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Ahmet Alkan,et al.  Identification of EMG signals using discriminant analysis and SVM classifier , 2012, Expert Syst. Appl..

[13]  Abdulhamit Subasi,et al.  Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders , 2013, Comput. Biol. Medicine.

[14]  Anil Kumar,et al.  Features based on intrinsic mode functions for classification of EMG signals , 2015 .

[15]  J. Cabri,et al.  Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification. , 2015, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[16]  Ganesh R. Naik,et al.  Single-Channel EMG Classification With Ensemble-Empirical-Mode-Decomposition-Based ICA for Diagnosing Neuromuscular Disorders , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.