Quantification Of Measurement Error Research Articles

Evaluation of measurement accuracy of wearable devices for heart rate variability

This paper proposed a method based on heart rate variability (HRV) for evaluating the accuracy of wearable devices in measuring heart rate. HRV refers to the variation in time intervals between successive heartbeats, widely used in many fields such as clinical and sports fields. Wearable devices such as Electrocardiogram (ECG) electrode patches have gained popularity due to their portability and ease of use. However, they can be prone to measurement interference caused by environmental noise, human respiration, etc. The proposed method consists of four main components: selection of "gold standard measurement devices", identification of HRV measurement metrics, construction of an HRV evaluation framework, and quantification of measurement errors. The method is validated through simulated experiments using ECG patches. The evaluation framework and quantification model established in this method have significant implications in establishment of industry standards and diagnosis of diseases in clinical practice.

Recording fine-scale movement of ground beetles by two methods: Potentials and methodological pitfalls.

Movement trajectories are usually recorded as a sequence of discrete movement events described by two parameters: step length (distance) and turning angle (bearing). One of the most widespread methods to record the geocoordinates of each step is by a GPS device. Such devices have limited suitability for recording fine movements of species with low dispersal ability including flightless carabid beetles at small spatio‐temporal scales. As an alternative, the distance‐bearing approach can avoid the measurement error of GPS units since it uses directly measured distances and compass azimuths. As no quantification of measurement error between distance‐bearing and GPS approaches exists so far, we generated artificial fine‐scale trajectories and in addition radio‐tracked living carabids in a temperate forest and recorded each movement step by both methods. Trajectories obtained from distance‐bearing were compared to those obtained by a GPS device in terms of movement parameters. Consequently, both types of trajectories were segmented by state‐switching modeling into two distinct movement stages typical for carabids: random walk and directed movement. We found that the measurement error of GPS compared to distance‐bearing was 1.878 m (SEM = 0.181 m) for distances and 31.330° (SEM = 2.066°) for bearings. Moreover, these errors increased under dense forest canopy and rainy weather. Distance error did not change with increasing distance recorded by distance‐bearing but bearings were significantly more sensitive to error at short distances. State‐switching models showed only slight, not significant, differences in movement states between the two methods in favor of the random walk in the distance‐bearing approach. However, the shape of the GPS‐measured trajectories considerably differed from those recorded by distance‐bearing caused especially by bearing error at short distances. Our study showed that distance‐bearing could be more appropriate for recording movement steps not only of ground‐dwelling beetles but also other small animals at fine spatio‐temporal scales.

Are geometric morphometric analyses replicable? Evaluating landmark measurement error and its impact on extant and fossil Microtus classification.

Geometric morphometric analyses are frequently employed to quantify biological shape and shape variation. Despite the popularity of this technique, quantification of measurement error in geometric morphometric datasets and its impact on statistical results is seldom assessed in the literature. Here, we evaluate error on 2D landmark coordinate configurations of the lower first molar of five North American Microtus (vole) species. We acquired data from the same specimens several times to quantify error from four data acquisition sources: specimen presentation, imaging devices, interobserver variation, and intraobserver variation. We then evaluated the impact of those errors on linear discriminant analysis‐based classifications of the five species using recent specimens of known species affinity and fossil specimens of unknown species affinity. Results indicate that data acquisition error can be substantial, sometimes explaining >30% of the total variation among datasets. Comparisons of datasets digitized by different individuals exhibit the greatest discrepancies in landmark precision, and comparison of datasets photographed from different presentation angles yields the greatest discrepancies in species classification results. All error sources impact statistical classification to some extent. For example, no two landmark dataset replicates exhibit the same predicted group memberships of recent or fossil specimens. Our findings emphasize the need to mitigate error as much as possible during geometric morphometric data collection. Though the impact of measurement error on statistical fidelity is likely analysis‐specific, we recommend that all geometric morphometric studies standardize specimen imaging equipment, specimen presentations (if analyses are 2D), and landmark digitizers to reduce error and subsequent analytical misinterpretations.

Integrated pipeline corrosion growth modeling and reliability analysis using dynamic Bayesian network and parameter learning technique

The present study integrates the corrosion growth modeling, reliability analysis and quantification of measurement errors of in-line inspection (ILI) tools in a single dynamic Bayesian network (DBN) model for the reliability-based corrosion management of oil and gas pipelines. The Expectation-Maximization algorithm in the context of the parameter learning technique is employed to learn the parameters of the DBN model. The application of the model on simulated and real-world corrosion data demonstrates the effectiveness of the parameter learning and accuracy of the corrosion growth predicted by the DBN model. In comparison with existing growth models, the integrating and graphical features of the developed model make the process of corrosion management more intuitive and transparent to users. The employment of the parameter learning technique provides an objective and convenient approach to elicit the probabilistic information from ILI and field measurement data. The above advantages make the model more amenable to the corrosion management practice in the pipeline industry.

Reliability and measurement error of multi-segment trunk kinematics and kinetics during cerebral palsy gait

Children with cerebral palsy (CP) have been shown to have altered trunk movements during gait resulting in increased loads at the lower lumbar spine. Detailed assessment is possible using 3D gait analysis. However, reliability and quantification of measurement error have not been established. The aim of this study was to evaluate test-retest reliability of thorax and lumbar segment kinematics and L5/S1 kinetics during gait in children with CP. Eight children with CP participated in this study with repeat assessments conducted within 1 week. Reliability was assessed using the one-way random ICC, standard error of measurement and an examination of extrinsic-to-intrinsic variability. Thorax kinematics demonstrated mixed level of reliability with SEM values ranging from 5.94o to 1.15o. Lumbar kinematics demonstrated poor-to-good reliability with the largest SEM values for peak lumbar flexion at 4.14o. L5/S1 moment values demonstrated only poor to good test-retest reliability while L5/S1 reactive forces demonstrated poor to excellent test-retest reliability.This study provides estimates of reliability and change needed to exceed measurement error. While reliability was mixed and some measures for thorax movement were above 5o, stated as a measure of acceptable error, the results of this study support the use of these measures in children with CP.

Quantification of Measurement Errors in the Lengths of Metal-Loss Corrosion Defects Reported by Inline Inspection Tools

Abstract This paper proposes a framework to quantify the measurement error associated with lengths of corrosion defects on oil and gas pipelines reported by inline inspection (ILI) tools based on a relatively large set of ILI-reported and field-measured defect data collected from different in-service pipelines in Canada. A log-logistic model is proposed to quantify the likelihood of a given ILI-reported defect being a type I defect (without clustering error) or a type II defect (with clustering error). The measurement error associated with the ILI-reported length of the defect is quantified as the average of those associated with the types I and II defects, weighted by the corresponding probabilities obtained from the log-logistic model. The implications of the proposed framework for the reliability analysis of corroded pipelines given the ILI information are investigated using a realistic pipeline example.

Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets.

Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of individuals, almost any desired relationship can be inferred with a suitable combination of covariates or analytic choices. Inspired by the genome-wide association study analysis paradigm that has transformed human genetics, X-wide association studies or “XWAS” have emerged as a popular approach to systematically analyzing nongenetic data sets and guarding against false positives. However, these studies often yield hundreds or thousands of associations characterized by modest effect sizes and miniscule P values. Many of these associations will be spurious and emerge due to confounding and other biases. One way of characterizing confounding in the genomics paradigm is the genomic inflation factor. An analogous “X-wide inflation factor,” denoted λX, can be defined and applied to published XWAS. Effects that arise in XWAS may be prioritized using replication, triangulation, quantification of measurement error, contextualization of each effect in the distribution of all effect sizes within a field, and pre-registration. Criteria like those of Bradford Hill need to be reconsidered in light of exposure-wide epidemiology to prioritize signals among signals.

Using the cervical range of motion (CROM) device to assess head repositioning accuracy in individuals with cervical radiculopathy in comparison to neck- healthy individuals

This study had two purposes: to compare head repositioning accuracy (HRA) using the cervical range of motion (CROM) device between individuals with cervical radiculopathy caused by disc disease (CDD; n = 71) and neck- healthy individuals (n = 173); and to evaluate the test–retest reliability of the CROM device in individuals with CDD, and criterion validity between the CROM device and a laser in neck-healthy individuals, with quantification of measurement errors. Parameters of reliability and validity were expressed with intra- class- correlation coefficients (ICCs), and measurement errors with standard error of measurement (SEM) and Bland Altman limits of agreement. HRA (Mdn, IQR) differed significantly between individuals with CDD and neck- healthy individuals after rotation right 2.7° (6.0), 1.7° (2.7); and rotation left 2.7° (3.3), 1.3° (2.7) (p < = 0.021); 31% of individuals with CDD were classified as having impairment in HRA. The test–retest reliability of the CROM device in individuals with CDD showed ICCs of 0.79- 0.85, and SEMs of 1.4°- 2°. The criterion validity between the CROM device and the laser in neck-healthy individuals showed ICCs of 0.43- 0.91 and SEMs of 0.8°- 1.3°. The results support the use of the CROM device for quantifying HRA impairment in individuals with CDD in clinical practice; however, criterion validity between the CROM device and a laser in neck-healthy individuals was questionable. HRA impairment in individuals with CDD may be important to consider during rehabilitation and evaluated with the criterion established with the CROM device in neck-healthy individuals.

Assessment of Mars atmospheric temperature retrievals from the Thermal Emission Spectrometer radiances

Motivated by the needs of Mars data assimilation, particularly quantification of measurement errors and generation of averaging kernels, we have evaluated atmospheric temperature retrievals from Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) radiances. Multiple sets of retrievals have been considered in this study: (1) retrievals available from the Planetary Data System (PDS), (2) retrievals based on variants of the retrieval algorithm used to generate the PDS retrievals, and (3) retrievals produced using the Mars 1-Dimensional Retrieval (M1R) algorithm based on the Optimal Spectral Sampling (OSS) forward model. The retrieved temperature profiles are compared to the MGS Radio Science (RS) temperature profiles. For the samples tested, the M1R temperature profiles can be made to agree within 2K (in the zonal mean) with the RS temperature profiles, but only after tuning the prior and error statistics. Use of a global prior that does not take into account the seasonal dependence leads errors of up 6K. In polar samples, errors relative to the RS temperature profiles are even larger. In these samples, the PDS temperature profiles also exhibit a poor fit with RS temperatures. This fit is worse than reported in previous studies, indicating that the lack of fit is due to a bias correction to TES radiances implemented after 2004. To explain the differences between the PDS and M1R temperatures, the algorithms are compared directly, with the OSS forward model inserted into the PDS algorithm. Factors such as the filtering parameter, the use of linear versus nonlinear constrained inversion, and the choice of the forward model, are found to contribute heavily to the differences in the temperature profiles retrieved in the polar regions, resulting in uncertainties of up to 6K. Even outside the poles, changes in the a priori statistics result in different profile shapes which all fit the radiances within the specified error. The importance of the a priori statistics prevents reliable global retrievals based a single a priori and strongly implies that a robust science analysis must instead rely on retrievals employing localized a priori information, for example from an ensemble based data assimilation system such as the Local Ensemble Transform Kalman Filter (LETKF).

Measurement and calculation of surge-arrester residual voltages

This paper presents accurate measurement of residual voltages of surge arresters (SA) by two designed charge meters (D-dot probes) as well as simulation validation in the high-conduction regime. Simultaneous impulse voltage measurements were conducted by using two D-dot probes, two uncompensated commercial mixed resistive–capacitive (RC) probes and an uncompensated damped capacitive voltage divider (CD). Residual voltage measured by two commercial mixed RC probes and the CD showed longer rise time, an initial inductive overshoot superimposed on the waveform and a significant decay even before the current peak instant, contrary to those measured by the two D-dot probes. Theoretical investigations are also introduced for the inductive coupling in the CD loop and analysis of the test circuit. Quantification of measurement errors has shown the superiority of D-dot probe over the other devices used. Finally, good agreement was found between the measured current and voltage waveforms for 33-kV SA and those computed from the test circuit analysis.

Measurement error and reliability testing: Application to rehabilitation

Aims: Effective measurement of clinical outcomes is dependent on reliable outcome instruments. Measurement error and reliability testing are fundamental underpinnings of reliability. This article defi nes and illustrates sources of measurement error, outlines strategies for error minimization, and gives an overview of the types of reliability studies. Content: The two main sources of measurement error: systematic bias and random error, are discussed. The three major types of reliability evaluation are then illustrated: test-retest; intra-rater and inter-rater reliability, and the relationship between reliability and validity is explained. Discussion and conclusions: Quantifi cation of measurement error is integral to determining the true effect of therapy, as quantifi ed by outcome measurement. Interpretation of reliability data involves consideration of many factors including demographic, diagnostic and clinical characteristics of the study sample.

Determination of the lateral resolution of electron-beam testers and quantification of measurement errors

The technique of applying a linescan to a sharp structural edge in order to determine the lateral resolution of an e-beam tester was performed on the aluminum lines of an appropriate test chip. However, as aluminum interconnection lines do not present an optimal structure for this purpose, measurements were also taken on gold-plated interconnection lines, a silicon wafer edge and a platinum diaphrag9m, and the attainable accuracy was compared. Recommendations for a measuring technique could be derived from these results. The local field effect can have a significantly adverse influence on the accuracy achievable in waveform measurements, even if the primary beam is adequately focused. A suitable measuring specification would help to quantify the measurement errors.

Measurement error in compositional data

Compositional data, consisting of vectors of proportions summing to unity such as the geochemical compositions of rocks, have proved difficult to analyze. Recently, the introduction of logistic and logratio transformations between the d-dimensional simplex and Euclidean space has allowed the use of familiar multivariate methods. The problem of how to model and analyze measurement errors in such data is approached through the concept of a perturbation of a composition. Such modeling allows investigation of the role of “rescaling,” quantification of measurement error, analysis of observor error, and assessment of the effect of measurement error on inferences.