Fractal dimensions of skin microcirculation flow in subjects with familial predisposition or newly diagnosed hypertension

Background: Fractal analysis has been shown to be capable of characterizing irregular time series generated in non-linear systems. Fluctuations in skin flow signals have a fractal nature, but to date there has been no analysis of subjects with hypertension. The aim of this study is to assess the fractal dimensions of skin microcirculation flows in subjects with a familial predisposition or newly diagnosed hypertension. Methods: A four-minutes rest flow (RF), minimal flow (BZ) during three-minutes ischemia and eight-minutes heat flow (HF) were recorded (using laser Doppler flowmetry) in patients with untreated hypertension, and in normotensives with no [NT(-)] or with a familial predisposition to hypertension [NT(+)]. Average one-minute surface areas under the curve of flow records and box dimensions (D) were calculated. Anova Kruskall-Wallis, c2 statistic and multivariate reverse regression analysis were used for calculation. Results: We studied 70 people (average age 36.1 ± 10.3 years, 39 men). Hypertensives (n = 31) had significantly higher values of both clinical blood pressure and 24-hour ambulatory blood pressure, body mass index, glucose, triglycerides and insulin than the NT(-), (n = 17) and NT(+), (n = 22) groups. Mean values of flows and surface area under the curve of RF, BZ, HF records, D RF and D HF were comparable in studied groups, but D BZ differed (1.13 ± ± 0.05 vs 1.15 ± 0.05 vs 1.11 ± 0.05, respectively; p = 0.04). A family history of hypertension, insulin level and variability of 24-hour diastolic blood pressure were significant predictors of D BZ lower values in the multiple regression model. Conclusions: Subjects with a familial predisposition to hypertension reveal altered homeodynamics of microvascular flow, with diminished chaotic ischemic flow. (Cardiol J 2011; 18, 1: 26-32

The application of topological data analysis to human motion recognition

Human motion analysis is a very important research topic in the field of computer vision, as evidenced by a wide range of applications such as video surveillance, medical assistance and virtual reality. Human motion analysis concerns the detection, tracking and recognition of human activities and behaviours. The development of low-cost range sensors enables the precise 3D tracking of body position. The aim of this paper is to present and evaluate a novel method based on topological data analysis (TDA) for motion capture (kinematic) processing and human action recognition. In contrast to existing methods of this type, we characterise human actions in terms of topological features. The recognition process is based on topological persistence which is stable to perturbations. The advantages of TDA are noise resistance and the ability to extract global structure from local information. The method we proposed in this paper deals very effectively with the task of human action recognition, even on the difficult classes of motion found in karate techniques. In order to evaluate our solution, we have performed three-fold cross-validation on a data set containing 360 recordings across twelve motion classes. The classification process does not require the use of machine learning and dynamical systems theory. The proposed classifier achieves a total recognition rate of 0.975 and outperforms the state-of-theart methods (Hachaj, 2019) that use support vector machines and principal component analysis-based feature generation