Nonlinear analysis of human physical activity patterns in health and disease

Abstract

The reliable and objective assessment of chronic disease state has been and still is a very significant challenge in clinical medicine. An essential feature of human behavior related to the health status, the functional capacity, and the quality of life is the physical activity during daily life. A common way to assess physical activity is to measure the quantity of body movement. Since human activity is controlled by various factors both extrinsic and intrinsic to the body, quantitative parameters only provide a partial assessment and do not allow for a clear distinction between normal and abnormal activity. In this paper, we propose a methodology for the analysis of human activity pattern based on the definition of different physical activity time series with the appropriate analysis methods. The temporal pattern of postures, movements, and transitions between postures was quantified using fractal analysis and symbolic dynamics statistics. The derived nonlinear metrics were able to discriminate patterns of daily activity generated from healthy and chronic pain states.

Classification of daily-life postural transitions using trunk-worn wearable barometric pressure sensor

“…For validation purpose, two more inertial sensors were placed on thigh and shank, which with a validated algorithm provide actual postural transition [2]. Data were acquired at 200Hz for the inertial and 25Hz for pressure sensors.…”

“…The type of transition (SiSt/StSi) was classified on the sign of difference in pressure before and after each transition. For validation, actual transitions were obtained from the algorithm in [2]. Sensitivity (SEN), and specificity (SPE) were computed using a 10-fold cross-validation.…”

The Contribution of Nonlinear Methods in the Understanding of Atrial Fibrillation

“…Fractal analysis is an appropriate method to characterize complex time series by focusing on the time-evolutionary properties on the data series and on their correlation properties. In this context, the detrended fluctuation analysis (DFA) method was developed specifically to distinguish between intrinsic fluctuations generated by complex systems and those caused by external or environmental stimuli acting on the system [20]. The DFA method can quantify the temporal organization of the fluctuations in a given non-stationary time series by a single scaling exponent α, a self-similarity parameter that represents the long-range power-law correlation properties of the signal.…”

“…Symbolic time series analysis involves the transformation of the original time series into a series of discrete symbols that are processed to extract useful information about the state of the system generating the process [20]. The first step of symbolic time series analysis is, hence, the transformation of the time series into a symbolic/binary sequence using a context-dependent symbolization procedure.…”

“…Thus, a complexity measures widely used is the proposed by Lempel and Ziv [20], which will be referred to as Lempel-Ziv complexity (LZC). This metric provides a measure of complexity related to the number of distinct substrings and the rate of their occurrence along a given sequence, larger values of LZC corresponding to more complex series.…”