Multiscale Entropy for Physical Activity Recognition

Nurwulan, Nurul Retno; Jiang, Bernard C.

doi:10.1145/3379310.3379318

Cited by 5 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Entropy-based analysis has been widely applied to different fields, like medicine and biology because of its suitability in analyzing nonlinear data [ 7 , 8 , 10 , 11 , 12 ]. Multiscale entropy (MSE) was introduced to quantify the complexity of limited length time-series data [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

“…Past studies used various approaches to measure human postural stability evaluation, such as the center of pressure (COP) [ 5 , 6 , 7 , 8 , 9 ], entropy-based methods [ 7 , 8 , 10 , 11 , 12 ], and the Star Excursion Balance Test [ 13 ]. The COP measurement using a force platform is the most commonly used method to evaluate postural stability.…”

Section: Introductionmentioning

confidence: 99%

“…The COP stabilogram metrics often used to determine the postural stability are mean displacement velocity, total excursion, sway area, and root mean square [ 7 , 8 , 9 ]. Compared to the COP stabilogram metrics, entropy-based methods such as approximate entropy [ 5 ], sample entropy [ 8 ], and multiscale entropy [ 7 , 10 , 11 , 12 ] could better describe the COP data since human physiological is nonlinear and nonstationary [ 10 ]. Despite its validity in detecting the instability in human postural control, the force platform is expensive, cumbersome, and impractical for human daily activity evaluation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Revised Stability Scales of the Postural Stability Index for Human Daily Activities

Chang

Jiang

Nurwulan

2020

Entropy

Self Cite

View full text Add to dashboard Cite

Evaluation of human postural stability is important to prevent falls. Recent studies have been carried out to develop postural stability evaluation in an attempt to fall prevention. The postural stability index (PSI) was proposed as a measure to evaluate the stability of human postures in performing daily activities. The objective of this study was to use the PSI in developing the stability scales for human daily activities. The current study used two open datasets collected from mobile devices. In addition, we also conducted three experiments to evaluate the effect of age, velocity, step counts, and devices on PSI values. The collected datasets were preprocessed using the ensemble empirical mode decomposition (EEMD), then the complexity index from each intrinsic mode function (IMF) was calculated using the multiscale entropy (MSE). From the evaluation, it can be concluded that the PSI can be applied to do daily monitoring of postural stability for both young and older adults, and the PSI is not affected by age. The revised stability scales developed in this current study can give better suggestions to users than the original one.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revised Stability Scales of the Postural Stability Index for Human Daily Activities

Chang

Jiang

Nurwulan

2020

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Secondly, the sliding window technique segments continuous data into several fragments. The window size and overlap percentage in the sliding window technique are important parameters, which may affect the feature extraction from sequences of data and the performance of classifiers [27][28][29][30]. However, there is no precise guideline for selecting the optimal window size.…”

Section: Gesture Recognitionmentioning

confidence: 99%

An Analysis of Fluid Intake Assessment Approaches for Fluid Intake Monitoring System

Hsieh,

Huang,

Chan

et al. 2023

Biosensors

View full text Add to dashboard Cite

Monitoring fluid intake is essential to help people manage their individual fluid intake behaviors and achieve adequate hydration. Previous studies of fluid intake assessment approaches based on inertial sensors can be categorized into wrist-worn-based and smart-container-based approaches. This study aims to analyze wrist-worn-based and smart-container-based fluid intake assessment approaches using inertial sensors. The comparison of these two approaches should be analyzed according to gesture recognition and volume estimation. In addition, the influence of the fill level and sip size information on the performance is explored in this study. The accuracy of gesture recognition with postprocessing is 92.89% and 91.8% for the wrist-worn-based approach and smart-container-based approach, respectively. For volume estimation, sip-size-dependent models can achieve better performance than general SVR models for both wrist-worn-based and smart-container-based approaches. The improvement of MAPE, MAD, and RMSE can reach over 50% except MAPE for small sip sizes. The results demonstrate that the sip size information and recognition performance are important for fluid intake assessment approaches.

show abstract

“…Based on Shannon's entropy, a number of entropy variants such as discrete entropy [29], spectral entropy [30] and sample entropy [31] have been proposed. Nurwulan et al compare features extracted by multi-scale entropy (MSE) and traditional features from 3-axis acceleration data, showing that the MSE performed better compared to the traditional features on the k-nearest neighbors (KNN) and random forest (RF) classification [32]. Bao et al extract frequency-domain entropy features from original acceleration data, which are used as the inputs with mean, energy, and correlation of the original data to build the model, and obtained ideal results [33].…”

Section: R Wmentioning

confidence: 99%

Information Theory Inspired Pattern Analysis for Time-series Data

Zhao¹,

Capstick²,

Palermo³

et al. 2023

Preprint

View full text Add to dashboard Cite

Current methods for pattern analysis in time series mainly rely on statistical features or probabilistic learning and inference methods to identify patterns and trends in the data. Such methods do not generalize well when applied to multivariate, multi-source, state-varying, and noisy time-series data. To address these issues, we propose a highly generalizable method that uses information theory-based features to identify and learn from patterns in multivariate time-series data. To demonstrate the proposed approach, we analyze pattern changes in human activity data. For applications with stochastic state transitions, features are developed based on Shannon's entropy of Markov chains, entropy rates of Markov chains, entropy production of Markov chains, and von Neumann entropy of Markov chains. For applications where state modeling is not applicable, we utilize five entropy variants, including approximate entropy, increment entropy, dispersion entropy, phase entropy, and slope entropy. The results show the proposed information theory-based features improve the recall rate, F1 score, and accuracy on average by up to 23.01% compared with the baseline models and a simpler model structure, with an average reduction of 18.75 times in the number of model parameters.

show abstract

Multiscale Entropy for Physical Activity Recognition

Cited by 5 publications

References 16 publications

Revised Stability Scales of the Postural Stability Index for Human Daily Activities

Revised Stability Scales of the Postural Stability Index for Human Daily Activities

An Analysis of Fluid Intake Assessment Approaches for Fluid Intake Monitoring System

Information Theory Inspired Pattern Analysis for Time-series Data

Contact Info

Product

Resources

About