New directions in the use of a sensor output signal evaluation system for biofeedback training

Electromyograms (EMGs) are affected by the location of the measuring electrodes and the shape and size of the probes. Therefore, EMG evaluation is macroscopic and subjective, and no algorithm has yet been devised for quantifying the degree of muscular abnormality or recovery. We have developed measurement parameters for evaluating the average rectified surface EMG (sEMG) data obtained from perineal muscles during biofeedback training (BFT) of patients with dysuria and of patients who are prone to falling. This evaluation of new parameters is intended to serve as a statistical technique that uses EMG signals. We have already evaluated the effects of BFT using this novel sensor output signal evaluation (SOSE) system. In this study, a combination of SOSE systems is developed to make their useful application emerge.

[1]  Luigi Galvani,et al.  De viribus electricitatis in motu musculari , 1967 .

[2]  Carlo J. De Luca,et al.  The Use of Surface Electromyography in Biomechanics , 1997 .

[3]  G. Yoshimichi,et al.  Neuronal histamine regulates food intake, adiposity, and uncoupling protein expression in agouti yellow (A(y)/a) obese mice. , 2003, Endocrinology.

[4]  H. Yoshimatsu,et al.  Satiation and Masticatory Function Modulated by Brain Histamine in Rats , 1998, Proceedings of the Society for Experimental Biology and Medicine. Society for Experimental Biology and Medicine.

[5]  S. Czernichow,et al.  Impact of Morbid Obesity on Chewing Ability , 2008, Obesity Surgery.

[6]  Kosei Ishimura,et al.  The estimation of muscle fatigue using chaos analysis , 2004 .

[7]  P. Raven,et al.  Regulation of middle cerebral artery blood velocity during dynamic exercise in humans: influence of aging. , 2008, Journal of applied physiology.

[9]  H. Takada,et al.  Study of Surface Electromyography and Its Indices to Evaluate Muscular Control , 2014 .

[10]  H. Takada,et al.  Evaluating indices of age-related muscle performance by using surface electromyography , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  G V Cochran,et al.  Dynamic electromyography. II. Normal patterns during gait , 1990, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[12]  A. Kegel,et al.  Physiologic therapy for urinary stress incontinence. , 1951, Journal of the American Medical Association.

[13]  D. Imbeau,et al.  Physical exposure of sign language interpreters: baseline measures and reliability analysis , 2005, European Journal of Applied Physiology.

[14]  H. Yoshimatsu,et al.  Central infusion of histamine reduces fat accumulation and upregulates UCP family in leptin-resistant obese mice. , 2001, Diabetes.

[15]  George N. Saridis,et al.  EMG Pattern Analysis and Classification for a Prosthetic Arm , 1982, IEEE Transactions on Biomedical Engineering.

[16]  O B Paulson,et al.  Cerebral autoregulation. , 1990, Cerebrovascular and brain metabolism reviews.

[17]  Hiroki Takada,et al.  Effectiveness of New Technology to Compose Stereoscopic Movies , 2012 .

[18]  Hitoshi Tsunashima,et al.  Applications of Double-Wayland Algorithm to Detect Anomalous Signals , 2007 .

[19]  A. Kegel,et al.  Progressive resistance exercise in the functional restoration of the perineal muscles. , 1948, American journal of obstetrics and gynecology.

[20]  Jun Kimura,et al.  Electrodiagnosis in Diseases of Nerve and Muscle , 2013 .

[21]  H. Takada,et al.  Nonlinear Analysis for Evaluation of Age-Related Muscle Performance Using Surface Electromyography , 2012 .

[22]  Raghavaiah Kanakamedala,et al.  Electrodiagnosis in Diseases of Nerve and Muscle: Principles and Practice. 2nd Edition , 1989 .

[23]  J. Hruschak.  Urinary Incontinence in Adults: Acute and Chronic Management , 1996 .