The symmetry of feet loading and adequate postural control are crucial aspects of proper squat performance. The study aimed to evaluate the effect of various stance widths during squat on postural control and symmetry of feet loading. Methods: Thirty healthy individuals participated in this study. Each participant performed one type of squat with a narrow stance (NS), hip stance (HS) and wide stance (WS). Results: A significantly higher value of CoP path length, the velocity of sways and Area95 were obtained for the WS squat compared to NS and HS. In addition, the wide feet setting significantly affected not only the feet loading symmetry but also the strategy (high LyE), the amount of irregularities (high SampEn) and the CoP time-series roughness complexity (high FD). It has been shown that as the base of support grew, the asymmetry index grew. Conclusions: The wide squat is less stable, requiring more complex postural control behavior and more flexibility. Performing this squat significantly shifts the pressure to the dominant limb.
It is well known that postural stability is influenced by visual stimuli. The influence of saccadic eye movement on postural control has been described, however, a specific response of different body segments has not been studied yet. Therefore, the aim of this study was to assess the effect of horizontal and vertical saccadic eye movements on postural stability with specific focus on upper trunk, lower trunk and lower limbs movement variability and complexity. Methods: Eighteen elderly participants (aged 70.3 ± 7.7 years) stood in bipedal stance in three visual conditions – horizontal saccades, vertical saccades and fixation. Accelerometers were attached to their lower back, sternum and shanks. Movement variability of each body segment was described by root-mean-square and sample entropy of acceleration. Results: The results of the present study revealed significant influence of saccadic eye movements on anterior-posterior and vertical shanks, and vertical lower trunk movement variability described by root-mean-square. Conclusions: The correlations between results of the observed segments showed segment-specific variability patterns but generalised complexity pattern.
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W referacie przedstawiono problematykę określania złożoności danych pomiarowych oraz przegląd pewnej klasy algorytmów wyznaczających złożoność. Omówiono złożoność Kołmogorowa, entropię Shannona oraz entropię próbek. Celem przeprowadzonych prac była analiza wybranych algorytmów zastosowanych do określania złożoności sygnałów generowanych przez coraz bardziej złożony system. Badania miały charakter eksperymentów symulacyjnych. Ich wyniki pokazują, że miara złożoności danych generowanych przez badany system może być powiązana ze złożonością jego struktury i procesów zachodzących w samym systemie. Jest to krok wstępny do opracowania metody monitorowania zmian zachodzących w systemach złożonych poprzez oceny złożoności generowanych przez nie sygnałów. Do systemów takich należy m.in. układ oddechowy monitorowany podczas bezdechu sennego.
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In this paper the problem of assessment of measurement data complexity is presented together with a review of a certain class of algorithms for complexity calculation. The Kolmogorov complexity, Shannon entropy and sample entropy are discussed. The aim of this work was to analyse the chosen algorithms used in the assessment of complexity of signals generated by a more and more complex system. The investigations were performed as simulation experiments. Their results show that the measure of complexity of data generated by the system under investigation can be related to the complexity of both the system’s structure and internal processes. This is a preliminary step towards the elaboration of a method for monitoring changes in complex systems by assessment of the complexity of generated by them signals. Such systems include e.g. the respiratory system monitored during sleep apnoea.
We investigate the variability of one of the most often used complexity measures in the analysis of the time series of RR intervals, i.e. Sample Entropy. The analysis is carried out for a dense matrix of possible r thresholds in 79 24h recordings, for segments consisting of 5000 consecutive beats, randomly selected from the whole recording. It is repeated for the same recordings in random order. This study is made possible by the novel NCM algorithm which is many orders of magnitude faster than the alternative approaches. We find that the bootstrapped standard errors for Sample entropy are large for RR intervals in physiological order compared to the standard errors for shuffled data which correspond to the maximum available entropy. This result indicates that Sample Entropy varies widely over the circadian period. This paper is purely methodological and no physiological interpretations are attempted.
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A physiological system encompasses numerous components that function at various time scales. To characterize the scale-dependent feature, the multiscale entropy (MSE) analysis has been proposed to describe the complex processes on multiple time scales. However, MSE analysis uses the relative scale factors to reveal the time-related dynamics, which may cause in-comparability of results from diverse studies with inconsistent sampling rates. In this study, in addition to the conventional MSE with relative scale factors, we also expressed MSE with absolute time scales (MaSE). We compared the effects of sampling rates on MSE and MaSE of simulated and real EEG time series. The results show that the previously found phenomenon (down-sampling can increase sample entropy) is just the projection of the compressing effect of down-sampling on MSE. And we have also shown the compressing effect of down-sampling on MSE does not change MaSE’s profile, despite some minor right-sliding. In addition, by analyzing a public EEG dataset of emotional states, we have demonstrated improved classification rate after choosing appropriate sampling rate. We have finally proposed a working strategy to choose an appropriate sampling rate, and suggested using MaSE to avoid confusion caused by sampling rate inconsistency. This novel study may apply to a broad range of studies that would traditionally utilize sample entropy and MSE to analyze the complexity of an underlying dynamic process.
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The hydrological regime in both the Godavari and Krishna River has been altered due to both human-induced and environmental changes. The present study utilizes the sample entropy and its more generalized approach known as multiscale entropy to investigate the temporal and spatial distribution of complexity and quantify them using SampEn values. Daily streamflow for five stations, three from Godavari River (Dhalegaon, Nowrangpur, and Polavaram), and two from Krishna River (Yadgir and K. Agraharam), was analysed for the complexity analyses. Trends in the streamflow for the selected gauging stations and their annual entropy values have also been evaluated using the Mann–Kendall test. The trend results revealed that three (Dhalegaon and Nowrangpur in Godavari basin and Yadgir in Krishna basin) out of five stations showed significant decreasing trends for both monthly and annual streamflow series. The declining trend in streamflow could be attributed to both anthropogenic (reservoir operation, increased water abstraction, etc.) and climatic (change in monsoon rainfall, temperature, etc.) factors. The most significant reduction in annual streamflow during the post-impact period was observed at Dhalegaon station in Godavari Basin (from 53,573 to 19,555 m3/s) signifying maximum alteration in annual flow regime. The entropy analysis results of streamflow showed that there was obvious spatial and temporal variation in the complexity, as indicated by the annual SampEn values. Although not profound, a negative correlation exists between the annual runoff and SampEn values (highest −0.42 at K. Agraharam) and hence a reverse correspondence exists between them. In MSE analysis, the original streamflow series increased with time scale (up to 30 days was chosen for this study), whereas entropy decreased with an increased time scale. Due to the fully operational state of the dams upstream of the gauging stations, the entropy values during the post-impact period were less the pre-impact period. The present study can be used as a scientific reference to use information science to detect hydrologic alterations in the river basins. Future studies should focus on considering both climatic and land-use changes in conjunction with the human-induced changes for more comprehensive river system disorder analysis.
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