Multiscale entropy approach to physiological fatigue during long‐term Web browsing

Hung, Chai-Ting; Jiang, Bernard C.

doi:10.1002/hfm.20146

Cited by 13 publications

(16 citation statements)

References 38 publications

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“…The results suggest that this type of exergame lends itself to the use of the complexity index to design training tasks. Our study is different from previous studies that have used multi-scale entropy to discuss physiological and pathological events of aging [10,16,[19][20][21]24,25,27,32,35,36] in that we used multi-scale entropy to determine the parameters that affect the performance of movement, as a method of data analysis, and as an assessment of activity performance. In the future, the applications of the complexity index could be generalized to other population groups, ideally covering a range of tasks and training characteristics such as difficulty levels.…”

Section: Discussionmentioning

confidence: 88%

“…Complexity is related to our ability to provide a short description of a phenomenon [32]. Costa et al [16] used the MSE method for quantifying the complexity expressed by physiological dynamics over multiple scales.…”

Section: Resultsmentioning

confidence: 99%

“…A previous study published by Hung et.al. [32] investigated the complexity of psychological and physiological changes in undergraduates and post-graduate research students after long-term internet surfing. They showed that there was higher complexity amongst undergraduate students.…”

Section: Resultsmentioning

confidence: 99%

“…They showed that there was higher complexity amongst undergraduate students. Hung et al [32] suggested that this may have been because the undergraduate students were more entertained by internet surfing than the research students. This may be because the undergraduate students were still in the beginning of their university studies, where they had just begun to realize that a wide range of information exists online and were therefore more interested in surfing.…”

Section: Resultsmentioning

confidence: 99%

“…Currently, clinical investigations and ergonomics-related research regularly apply MSE to perform quantitative analysis on recorded physiological signals [16,[19][20][21][22]24,25,32].…”

Section: Multi-scale Entropymentioning

confidence: 99%

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Multi-Scale Entropy Analysis of Body Sway for Investigating Balance Ability During Exergame Play Under Different Parameter Settings

Lee

Sun

2015

Entropy

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Abstract:The goal of this study was to investigate the parameters affecting exergame performance using multi-scale entropy analysis, with the aim of informing the design of exergames for personalized balance training. Test subjects' center of pressure (COP) displacement data were recorded during exergame play to examine their balance ability at varying difficulty levels of a balance-based exergame; the results of a multi-scale entropy-based analysis were then compared to traditional COP indicators. For games involving static posture frames, variation in posture frame travel time was found to significantly affect the complexity of both the anterior-posterior (MSE-AP) and medio-lateral (MSE-ML) components of balancing movements. However, in games involving dynamic posture frames, only MSE-AP was found to be sensitive to the variation of parameters, namely foot-lifting speed. Findings were comparable to the COP data published by Sun et al., indicating that the use of complexity data is a feasible means of distinguishing between different parameter sets and of understanding how human design considerations must be taken into account in exergame development. Not only can this method be used as another assessment index in the future, it can also be used in the optimization of parameters within the virtual environments of exergames.

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Section: Discussionmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Currently, clinical investigations and ergonomics-related research regularly apply MSE to perform quantitative analysis on recorded physiological signals [16,[19][20][21][22]24,25,32].…”

Section: Multi-scale Entropymentioning

confidence: 99%

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Multi-Scale Entropy Analysis of Body Sway for Investigating Balance Ability During Exergame Play Under Different Parameter Settings

Lee

Sun

2015

Entropy

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Evaluating Presence for Customer Experience in a Virtual Environment: Using a Nuclear Power Plant as an Example

Lee

Chou

Sun

2014

Hum Ftrs & Erg Mfg Svc

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In contrast to experiences offered by traditional media, this study used a three‐dimensional virtual environment to evaluate customer experiences. The aims of this study were twofold: first, to investigate whether virtual interaction can impress customers in a visual or auditory sense and thereby further induce “presence”; and second, to understand how to provide a better sense of presence by using virtual interactions. This study used a nuclear power plant control room to construct virtual customer experience patterns. The results of the questionnaire showed no statistical significance, except for one question: “How responsive was the environment to actions that you initiated (or performed)?” During the experience, interactively receiving information in the active model offered a more significant sense of presence. For the physiological measurement, we used the multiscale entropy of complexity to analyze the heart rate. The complexity index for the active model proved significantly larger than for the passive, indicating better adaptability to the active model.

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Singular Value Decomposition Based Feature Extraction Technique for Physiological Signal Analysis

2010

Self Cite

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Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.

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Multiscale entropy approach to physiological fatigue during long‐term Web browsing

Cited by 13 publications

References 38 publications

Multi-Scale Entropy Analysis of Body Sway for Investigating Balance Ability During Exergame Play Under Different Parameter Settings

Multi-Scale Entropy Analysis of Body Sway for Investigating Balance Ability During Exergame Play Under Different Parameter Settings

Evaluating Presence for Customer Experience in a Virtual Environment: Using a Nuclear Power Plant as an Example

Singular Value Decomposition Based Feature Extraction Technique for Physiological Signal Analysis

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