Selection of Entropy-Measure Parameters for Force Plate-Based Human Balance Evaluation

Montesinos, Luis; Castaldo, Rossana; Pecchia, Leandro

doi:10.1007/978-981-10-9038-7_59

Cited by 7 publications

(4 citation statements)

References 16 publications

(22 reference statements)

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“…The present study was motivated by the promising results obtained in a preliminary study, in which the issue of the adequate selection of ApEn and SampEn parameter values for COP time-series analysis was partially addressed [29]. However, the present study represents a more comprehensive investigation of this issue, as it covered a wider range of parameter values, included the ML direction in addition to the AP direction and compared the ability of ApEn and SampEn to discriminate between experimental groups to that of traditional measures of COP displacement (e.g.…”

Section: Introductionmentioning

confidence: 99%

On the use of approximate entropy and sample entropy with centre of pressure time-series

Montesinos

Castaldo

Pecchia

2018

J NeuroEngineering Rehabil

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122

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BackgroundApproximate entropy (ApEn) and sample entropy (SampEn) have been previously used to quantify the regularity in centre of pressure (COP) time-series in different experimental groups and/or conditions. ApEn and SampEn are very sensitive to their input parameters: m (subseries length), r (tolerance) and N (data length). Yet, the effects of changing those parameters have been scarcely investigated in the analysis of COP time-series. This study aimed to investigate the effects of changing parameters m, r and N on ApEn and SampEn values in COP time-series, as well as the ability of these entropy measures to discriminate between groups.MethodsA public dataset of COP time-series was used. ApEn and SampEn were calculated for m = {2, 3, 4, 5}, r = {0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5} and N = {600, 1200} (30 and 60 s, respectively). Subjects were stratified in young adults (age < 60, n = 85), and older adults (age ≥ 60) with (n = 18) and without (n = 56) falls in the last year. The effects of changing parameters m, r and N on ApEn and SampEn were investigated with a three-way ANOVA. The ability of ApEn and SampEn to discriminate between groups was investigated with a mixed ANOVA (within-subject factors: m, r and N; between-subject factor: group). Specific combinations of m, r and N producing significant differences between groups were identified using the Tukey’s honest significant difference procedure.ResultsA significant three-way interaction between m, r and N confirmed the sensitivity of ApEn and SampEn to the input parameters. SampEn showed a higher consistency and ability to discriminate between groups than ApEn. Significant differences between groups were mostly observed in longer (N = 1200) COP time-series in the anterior-posterior direction. Those differences were observed for specific combinations of m and r, highlighting the importance of an adequate selection of input parameters.ConclusionsFuture studies should favour SampEn over ApEn and longer time-series (≥ 60 s) over shorter ones (e.g. 30 s). The use of parameter combinations such as SampEn (m = {4, 5}, r = {0.25, 0.3, 0.35}) is recommended.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0465-9) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

On the use of approximate entropy and sample entropy with centre of pressure time-series

Montesinos

Castaldo

Pecchia

2018

J NeuroEngineering Rehabil

Self Cite

122

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“…Fuzzy Entropy (FuzzyEn): This also uses the Heaviside function, although similarity is evaluated by a fuzzy function that computes a membership coefficient ranging from 0 to 1. Consequently, in addition to the selection of N (length over which to compute entropy), m (subsequence length), and r (tolerance in terms of the number of SDs), FuzzyEn requires a fourth parameter, n , the gradient of the boundary of the exponential function used to assess similarity [ 39 ]. FuzzyEn provides a graded similarity instead of binary similarity between parts of the time series [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study

Bijlani¹,

Nilforooshan²,

Kouchaki³

2022

JMIR Aging

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Background Sensor-based remote health monitoring can be used for the timely detection of health deterioration in people living with dementia with minimal impact on their day-to-day living. Anomaly detection approaches have been widely applied in various domains, including remote health monitoring. However, current approaches are challenged by noisy, multivariate data and low generalizability. Objective This study aims to develop an online, lightweight unsupervised learning–based approach to detect anomalies representing adverse health conditions using activity changes in people living with dementia. We demonstrated its effectiveness over state-of-the-art methods on a real-world data set of 9363 days collected from 15 participant households by the UK Dementia Research Institute between August 2019 and July 2021. Our approach was applied to household movement data to detect urinary tract infections (UTIs) and hospitalizations. Methods We propose and evaluate a solution based on Contextual Matrix Profile (CMP), an exact, ultrafast distance-based anomaly detection algorithm. Using daily aggregated household movement data collected via passive infrared sensors, we generated CMPs for location-wise sensor counts, duration, and change in hourly movement patterns for each patient. We computed a normalized anomaly score in 2 ways: by combining univariate CMPs and by developing a multidimensional CMP. The performance of our method was evaluated relative to Angle-Based Outlier Detection, Copula-Based Outlier Detection, and Lightweight Online Detector of Anomalies. We used the multidimensional CMP to discover and present the important features associated with adverse health conditions in people living with dementia. Results The multidimensional CMP yielded, on average, 84.3% recall with 32.1 alerts, or a 5.1% alert rate, offering the best balance of recall and relative precision compared with Copula-Based and Angle-Based Outlier Detection and Lightweight Online Detector of Anomalies when evaluated for UTI and hospitalization. Midnight to 6 AM bathroom activity was shown to be the most important cross-patient digital biomarker of anomalies indicative of UTI, contributing approximately 30% to the anomaly score. We also demonstrated how CMP-based anomaly scoring can be used for a cross-patient view of anomaly patterns. Conclusions To the best of our knowledge, this is the first real-world study to adapt the CMP to continuous anomaly detection in a health care scenario. The CMP inherits the speed, accuracy, and simplicity of the Matrix Profile, providing configurability, the ability to denoise and detect patterns, and explainability to clinical practitioners. We addressed the need for anomaly scoring in multivariate time series health care data by developing the multidimensional CMP. With high sensitivity, a low alert rate, better overall performance than state-of-the-art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique extensible to multimodal data for dementia and other health care scenarios.

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“…Postural control is the result of a complex and proper interaction between physiologic systems of the human body (1)(2)(3). Two main purposes of the postural control system are the ability to maintain appropriate body orientation in Postural Control of Incontinent Females http://mjiri.iums.ac.ir Med J Islam Repub Iran.…”

Section: Introductionmentioning

confidence: 99%

“…To determine the center of pressure (COP) displacement using the force plate in quiet standing is one of the most common methods for measuring postural control (3,4). The COP is not considered as an anatomical point.…”

mentioning

confidence: 99%

Comparison of Postural Control of Females with and without Urinary Incontinence: A Case-Control Study

Alizadeh

Nikjooy

Vasaghi‐Gharamaleki

et al. 2022

Med. J. Islam. Republ. Iran

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Selection of Entropy-Measure Parameters for Force Plate-Based Human Balance Evaluation

Cited by 7 publications

References 16 publications

On the use of approximate entropy and sample entropy with centre of pressure time-series

On the use of approximate entropy and sample entropy with centre of pressure time-series

An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study

Comparison of Postural Control of Females with and without Urinary Incontinence: A Case-Control Study

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