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 these will help clinicians in diagnosis and treatment. Postural study helps the elderly and people with a balancing problem due to various pathological conditions. In elderly subjects, falls are common and may result in injury. Correct postural balance is basic to well-being and it influences our daily life significantly. These postural signals are non-stationary; they may appear to be random in the time scale and it is difficult to observe the subtle changes for the human observer. Hence, more hidden information can be obtained from the signal using non-linear parameters. In this paper, ten young normal subjects are subjected to the balancing platform whose acceleration is gradually increased from 1 m/s2 to 5 m/s2 to study the postural response. The ankle front-back acceleration and ankle pitch angular velocity sensor data were studied using the largest Lyapunov exponent (LLE). The results show that for higher acceleration of the platform the ankle movement follows a particular rhythm, resulting in a lower Lyapunov exponent. During lower acceleration of the balancing platform, this value is higher because of the random movement of the ankle. In this work, the pattern of the body response was studied using LLE values for different accelerations using ankle data as the base signal for the normal subjects.

No abstract