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

Lee, Chia-Hsuan; Sun, Tien‐Lung

doi:10.3390/e17117608

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…As shown in Table 5, we found by categorizing fallers and non-fallers by critical values from clinical tests using MSE. MSE has previously been used to detect the complexity of physiological signals, such as heart beat [30,51,52] brain waves [53], acceleration [32], and postural stability [52,54]. The results all showed that MSE can effectively identify objective data.…”

Section: Resultsmentioning

confidence: 96%

Using Wearable Accelerometers in a Community Service Context to Categorize Falling Behavior

Lee

Sun

Jiang

et al. 2016

Entropy

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Abstract:In this paper, the Multiscale Entropy (MSE) analysis of acceleration data collected from a wearable inertial sensor was compared with other features reported in the literature to observe falling behavior from the acceleration data, and traditional clinical scales to evaluate falling behavior. We use a fall risk assessment over a four-month period to examine >65 year old participants in a community service context using simple clinical tests, including the Short Form Berg Balance Scale (SFBBS), Timed Up and Go test (TUG), and the Short Portable Mental Status Questionnaire (SPMSQ), with wearable accelerometers for the TUG test. We classified participants into fallers and non-fallers to (1) compare the features extracted from the accelerometers and (2) categorize fall risk using statistics from TUG test results. Combined, TUG and SFBBS results revealed defining features were test time, Slope(A) and slope(B) in Sit(A)-to-stand(B), and range(A) and slope(B) in Stand(B)-to-sit(A). Of (1) SPMSQ; (2) TUG and SPMSQ; and (3) BBS and SPMSQ results, only range(A) in Stand(B)-to-sit(A) was a defining feature. From MSE indicators, we found that whether in the X, Y or Z direction, TUG, BBS, and the combined TUG and SFBBS are all distinguishable, showing that MSE can effectively classify participants in these clinical tests using behavioral actions. This study highlights the advantages of body-worn sensors as ordinary and low cost tools available outside the laboratory. The results indicated that MSE analysis of acceleration data can be used as an effective metric to categorize falling behavior of community-dwelling elderly. In addition to clinical application, (1) our approach requires no expert physical therapist, nurse, or doctor for evaluations and (2) fallers can be categorized irrespective of the critical value from clinical tests.

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Section: Resultsmentioning

confidence: 96%

Using Wearable Accelerometers in a Community Service Context to Categorize Falling Behavior

Lee

Sun

Jiang

et al. 2016

Entropy

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“…Our study is different from previous studies, in that previous studies have used MSE to determine the physiological and pathological events of aging [ 11 , 12 , 18 – 21 , 51 , 52 ], whereas we used MSE to determine the equipment that measures the performance of postural stability. We attempted to generalize the applications of complexity index to tasks [ 53 ] and training characteristics and to explore different tools to obtain different results.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of postural stability based on a force plate and inertial sensor during static balance measurements

Lee

Sun

2018

J Physiol Anthropol

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BackgroundPrevious research on balance mostly focused on the assessment, training, and improvements of balance through interventions. We investigated tools commonly used to study static balance. Differences in postural stability were analyzed using multiscale entropy (MSE) and feature analysis.MethodsA force plate and inertial sensor were used to collect acceleration and center-of-pressure (COP) nonlinear signals. MSE was also used to detect fractal correlations and assess the complexity of univariate data complexity. Fifteen healthy subjects participated in the experiments. Each stood on a force plate and wore a sensor while attempting to maintain postural stability for 30 s in four randomized experiments to evaluate their static balance via a copositive experiment with eyes open/closed and with standing on one foot or both feet. A Wilcoxon-signed rank test was used to confirm that the conditions were significant. Considering the effect of the assessment tools, the influence of the visual and lower limb systems on postural stability was assessed and the results from the inertial sensor and force plate experiments were compared.ResultsForce plate usage provided more accurate readings when completing static balance tasks based on the visual system, whereas an inertial sensor was preferred for lower-limb tasks. Further, the eyes-open-standing-on-one-foot case involved the highest complexity at the X, Y, and Z axes for acceleration and at the ML axis for COP compared with other conditions, from which the axial directions can be identified.ConclusionsThe findings suggested investigation of different evaluation tool choices that can be easily adapted to suit different needs. The results for the complexity index and traditional balance indicators were comparable in their implications on different conditions. We used MSE to determine the equipment that measures the postural stability performance. We attempted to generalize the applications of complexity index to tasks and training characteristics and explore different tools to obtain different results.Trial registrationThis study was approved by the Research Ethics Committee of National Taiwan University and classified as expedited on August 24, 2017. The committee is organized under and operates in accordance with Social and Behavioral Research Ethical Principles and Regulations of National Taiwan University and government laws and regulations.

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“…Even though both use all features or only important features, eye-movement complexity features are consistently superior in detecting computer activities. The basic nature of complexity-based features that are containing “implicit” information about body responses to the human activity and states [ 46 , 47 ] has benefited AI models to help them to overcome the prediction confusedness (compare Table 4 with Table 5 ). As was expected, the findings in this study prove that complexity analysis is also suitable for eye-movement-based data, as useful as its usage in human heart rate [ 16 ], cerebral hemodynamics [ 17 ], blood pressure [ 18 ], and body movements [ 19 , 48 ] data.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Effectiveness of Complexity Features of Eye Movement on Computer Activities Detection

Destyanto

Lin

2022

Healthcare

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Recently, tools developed for detecting human activities have been quite prominent in contributing to health issue prevention and long-term healthcare. For this occasion, the current study aimed to evaluate the performance of eye-movement complexity features (from multi-scale entropy analysis) compared to eye-movement conventional features (from basic statistical measurements) on detecting daily computer activities, comprising reading an English scientific paper, watching an English movie-trailer video, and typing English sentences. A total of 150 students participated in these computer activities. The participants’ eye movements were captured using a desktop eye-tracker (GP3 HD Gazepoint™ Canada) while performing the experimental tasks. The collected eye-movement data were then processed to obtain 56 conventional and 550 complexity features of eye movement. A statistic test, analysis of variance (ANOVA), was performed to screen these features, which resulted in 45 conventional and 379 complexity features. These eye-movement features with four combinations were used to build 12 AI models using Support Vector Machine, Decision Tree, and Random Forest (RF). The comparisons of the models showed the superiority of complexity features (85.34% of accuracy) compared to conventional features (66.98% of accuracy). Furthermore, screening eye-movement features using ANOVA enhances 2.29% of recognition accuracy. This study proves the superiority of eye-movement complexity features.

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

Cited by 4 publications

References 24 publications

Using Wearable Accelerometers in a Community Service Context to Categorize Falling Behavior

Using Wearable Accelerometers in a Community Service Context to Categorize Falling Behavior

Evaluation of postural stability based on a force plate and inertial sensor during static balance measurements

Evaluating the Effectiveness of Complexity Features of Eye Movement on Computer Activities Detection

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