Multivariate Multiscale Symbolic Entropy Analysis of Human Gait Signals

Yu, Jinxin; Cao, Junyi; Liao, Wei-Hsin; Chen, YangQuan; Lin, Jing; Liu, Rong

doi:10.3390/e19100557

Cited by 18 publications

(11 citation statements)

References 29 publications

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“…For computing MNCSE, mean of data points was used as a criterion for constructing coarse-grained time series at various temporal scales. A nearly identical study appeared, form the methods point of view [ 24 ] during the review process, in which multivariate multiscale symbolic entropy is proposed. Three main differences including coarse graining procedure followed, data symbolization techniques used and application area exist between the two studies.…”

Section: Introductionmentioning

confidence: 99%

“…Three main differences including coarse graining procedure followed, data symbolization techniques used and application area exist between the two studies. The main focus of the study [ 24 ] is to address the multiscale and multichannel dependence inherent in the time series data, whereas our methodology accounts for the multiple time scale inherent on a single channel data. The coarse grained time series is generated using mean of multichannel time series data in [ 24 ], and in our proposed method coarse grained time series is generated by taking average of single channel time series data at a specific temporal scale.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, the two methodologies using different data symbolization techniques. Thirdly, research reported in [ 24 ] is applied for discriminating control and neurodegenerative disease subjects, whereas, application area of our study is cardiac interbeat interval time series data.…”

Section: Introductionmentioning

confidence: 99%

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Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

et al. 2018

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Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

et al. 2018

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“…In this paper, stride intervals extracted from the original voltage data of the NG were examined by complexity analysis to identify the status of the human gait. Previously, various evaluation indexes have been used for complexity analysis and have been proposed including multiscale entropy [26], composite multiscale entropy [27], and multivariate multiscale symbolic entropy [28]. In the analysis of entropy, larger values indicate the data is with higher complexity To demonstrate the potential of the smart insole for human gait signal monitoring, two young, healthy subjects, A and B, were instructed to walk continuously on a treadmill at their self-determined rate of slow, normal and fast speed.…”

Section: Resultsmentioning

confidence: 99%

Self-Powered Smart Insole for Monitoring Human Gait Signals

Wang

Cao

et al. 2019

Sensors

Self Cite

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With the rapid development of low-power consumption wireless sensors and wearable electronics, harvesting energy from human motion to enable self-powered sensing is becoming desirable. Herein, a pair of smart insoles integrated with piezoelectric poly(vinylidene fluoride) (PVDF) nanogenerators (NGs) are fabricated to simultaneously harvest energy from human motion and monitor human gait signals. Multi-target magnetron sputtering technology is applied to form the aluminum electrode layers on the surface of the PVDF film and the self-powered insoles are fabricated through advanced 3D seamless flat-bed knitting technology. Output responses of the NGs are measured at different motion speeds and a maximum value of 41 V is obtained, corresponding to an output power of 168.1 μW. By connecting one NG with an external circuit, the influence of external resistance, capacitor, and motion speed on the charging characteristics of the system is systematically investigated. To demonstrate the potential of the smart insoles for monitoring human gait signals, two subjects were asked to walk on a treadmill at different speeds or with a limp. The results show that one can clearly distinguish walking with a limp from regular slow, normal, and fast walking states by using multiscale entropy analysis of the stride intervals.

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“…As a result, the rigid ground assumption is no longer valid, and the robot-ground impact model is no longer independent of the ground compliance as well [14][15][16][17][18]. In documents, the effect of ground compliance on the bipedal locomotion and the control strategies to cope with it have been studied [19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Stabilization Method Based on an Adaptive Feedforward Controller for the Underactuated Bipedal Walking with Variable Step-Length on Compliant Discontinuous Ground

Wang

Yao

et al. 2020

Complexity

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Both compliance and discontinuity are the common characteristics of the real ground surface. This paper proposes a stabilization method for the underactuated bipedal locomotion on the discontinuous compliant ground. Unlike a totally new control method, the method is actually a high-level control strategy developed based on an existing low-level controller meant for the continuous compliant ground. As a result, although the ground environment is more complex, the calculation cost for the robot walking control system is not increased. With the high-level control strategy, the robot is able to adjust its step-length and velocity simultaneously to stride over the discontinuous areas on the compliant ground surface. The effectiveness of the developed method is validated with a numerical simulation and a physical experiment.

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Multivariate Multiscale Symbolic Entropy Analysis of Human Gait Signals

Cited by 18 publications

References 29 publications

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

Self-Powered Smart Insole for Monitoring Human Gait Signals

A Stabilization Method Based on an Adaptive Feedforward Controller for the Underactuated Bipedal Walking with Variable Step-Length on Compliant Discontinuous Ground

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