ANALYSIS OF WALKING PATTERN OF NORMAL & SIMULATED PARALYTIC SUBJECT

Abstract

"Various disciplines have benefited from the advent of high-performance computing in achieving practical solutions to their problems and the area of health care is no exception to this. Non-linear signal processing tools have been developed to understand the hidden complexity of the time series and will help clinicians in diagnosis and treatment. Postural study helps the elderly and people with balancing problem due to various pathological conditions. In elderly subjects, falls are common and may result in injury. Correct postural balance is basic to wellbeing and it influences our daily life significantly. These postural signals are non-stationary: may appear to be random in the time scale and it is difficult to observe the subtle changes by the human observer. Hence, one can obtain more hidden information from the signal using non-linear parameters.

People usually develop different kinds of compensated gait in response to their local function deficits, such as, muscle weakness, spasticity in specific muscle groups, or joint stiffness, to overcome the falling-down risk factors. Compensated walking has been analyzed empirically in the impaired gait analysis area. However, the compensation could be identified spatially and temporally. In this work, I have analysed the normal and simulated paralytic subject walking using electromyogram (EMG) signals. Thus, this research can improve the understanding of the behaviour of compensated walking, thus bring insights not only for building useful walking assisting systems with high safety, but also for designing effective rehabilitation interventions.

Keywords: Hurst exponent, sensor, muscle, Correlation dimension, largest Lyapunov exponent, Capacity Dimension."