Measurement in soft systems: Epistemological framework and a case study

Mari, Luca; Lazzarotti, Valentina; Manzini, Raffaella

doi:10.1016/j.measurement.2008.06.004

Cited by 20 publications

(6 citation statements)

References 22 publications

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“…Concepts and definitions for so-called 'soft' systems are being studied e.g. by Mari et al (2009), and reviewed by Berglund et al (2011) and Wilson et al (2015). The present work focuses particularly on ordinal values, which we consider to be qualitative measurement data, as opposed to nominal results which are mere categorizations without a numerical scale.…”

Section: Metrology Of Human-based and Other Qualitative Measurementsmentioning

confidence: 99%

Metrology of human-based and other qualitative measurements

Pendrill¹,

Petersson²

2016

Meas. Sci. Technol.

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The metrology of human-based and other qualitative measurements is in its infancy-concepts such as traceability and uncertainty are as yet poorly developed. This paper reviews how a measurement system analysis approach, particularly invoking as performance metric the ability of a probe (such as a human being) acting as a measurement instrument to make a successful decision, can enable a more general metrological treatment of qualitative observations. Measures based on human observations are typically qualitative, not only in sectors, such as health care, services and safety, where the human factor is obvious, but also in customer perception of traditional products of all kinds. A principal challenge is that the usual tools of statistics normally employed for expressing measurement accuracy and uncertainty will probably not work reliably if relations between distances on different portions of scales are not fully known, as is typical of ordinal or other qualitative measurements. A key enabling insight is to connect the treatment of decision risks associated with measurement uncertainty to generalized linear modelling (GLM). Handling qualitative observations in this way unites information theory, the perceptive identification and choice paradigms of psychophysics. The Rasch invariant measure psychometric GLM approach in particular enables a proper treatment of ordinal data; a clear separation of probe and item attribute estimates; simple expressions for instrument sensitivity; etc. Examples include two aspects of the care of breast cancer patients, from diagnosis to rehabilitation. The Rasch approach leads in turn to opportunities of establishing metrological references for quality assurance of qualitative measurements. In psychometrics, one could imagine a certified reference for knowledge challenge, for example, a particular concept in understanding physics or for product quality of a certain health care service. Multivariate methods, such as Principal Component Regression, can also be improved by exploiting the increased resolution of the Rasch approach.

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Section: Metrology Of Human-based and Other Qualitative Measurementsmentioning

confidence: 99%

Metrology of human-based and other qualitative measurements

Pendrill¹,

Petersson²

2016

Meas. Sci. Technol.

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“…[19][20][21][22] Judging the appearance of a clinical presentation, such as corneal staining, falls into this soft measurement realm and is considered complex because of the many characteristics of staining, such as area, depth, and intensity. [23][24][25][26][27] Psychophysics is the examination of the relationship between one's internal mental representation, such as the clinical grading of corneal staining, with external physical attributes, such as measurement of the luminance or area of staining. To our knowledge, the bulbar redness scale is the only ocular surface clinical scale that has been developed using these psychophysical principles to relate measured physical attributes to the scale.…”

Section: Introductionmentioning

confidence: 99%

Canonical Grading Scales of Corneal and Conjunctival Staining Based on Psychophysical and Physical Attributes

Simpson

Begley

Situ

et al. 2021

Trans. Vis. Sci. Tech.

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Purpose In this study, we apply psychophysical scaling principles based on physical (photometric) attributes of images to better understand the factors involved in clinician judgement of ocular surface staining and, using that knowledge, to develop photographic scales for the assessment of staining for dry eye (DE) and related conditions. Methods Subjects with noninfectious ocular surface staining were enrolled at five clinical sites. Following instillation of fluorescein, photographs of corneal staining were taken every 30 seconds for at least 5 minutes. The same procedure was followed for conjunctival staining after instillation of 2 µl of 1% lissamine green. A subset of the best corneal and bulbar conjunctival staining images were anonymized and a spectroradiometer measured photometric attributes (luminance and chromaticity). The images were scaled psychophysically by study investigators, who participated in constructing grading scales based on physical and psychophysical analyses. The final grading scales were refined following consultation with outside DE experts. Results Photographs were collected from 142 subjects (81% women), with an average age of 58 ± 17 years; 89% were diagnosed with DE. There was a monotonic relationship between between physical measurements and psychophysically scaled staining of both corneal (fluorescein) and bulbar (lissamine green) staining. Michelson contrast and u’ (chromaticity) accounted for 66% and 64% of the variability in the psychophysically scaled images of fluorescein corneal and lissamine green conjunctival staining, respectively. Translational Relevance This paper provides examples of the first ever clinically usable ocular surface staining scales validated using psychophysical scaling and the physical attributes (luminance and chromaticity) of the staining itself. In addition, it provides a generalizable method for the development of other clinical scales of ocular appearance.

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“…In this sense, technologies and strategies have been developed to use the available information of other variables in the process and computational models infer the value of those that cannot be directly measured [1][2][3][4][5]. These strategies have been used in several contexts, such as oil refinery [6], chemical industry [7], air conditioning systems [8], automobiles [9], semiconductor manufacturing [10], and the quantification of properties determined by human perception (such as smell, taste, color, etc), among others [11][12][13][14][15][16][17]. Depending on the context, the type of indirect measurement strategy addressed in this paper can be referred to as soft sensor, virtual sensor, estimator, inferential sensor, virtual metrology, soft metrology, among others.…”

Section: Introductionmentioning

confidence: 99%

Soft metrology based on machine learning: a review

Vallejo

Espriella

Gómez-Santamaría

et al. 2019

Meas. Sci. Technol.

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Soft metrology has been defined as a set of measurement techniques and models that allow the objective quantification of properties usually determined by human perception such as smell, sound or taste. The development of a soft metrology system requires the measurement of physical parameters and the construction of a model to correlate them with the variables that need to be quantified. This paper presents a review of indirect measurement with the aim of understanding the state of development in this area, as well as the current challenges and opportunities; and proposes to gather all the different designations under the term soft metrology, broadening its definition. For this purpose, the literature on indirect measurement techniques and systems has been reviewed, encompassing recent as well as a few older key documents to present a time line of development and map out application contexts and designations. As machine learning techniques have been extensively used in indirect measurement strategies, this review highlights them, and also makes an effort to describe the state of the art regarding the determination of uncertainty. This study does not delve into developments and applications for human and social sciences, although the proposed definition considers the use that this term has had in these areas.

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Measurement in soft systems: Epistemological framework and a case study

Cited by 20 publications

References 22 publications

Metrology of human-based and other qualitative measurements

Metrology of human-based and other qualitative measurements

Canonical Grading Scales of Corneal and Conjunctival Staining Based on Psychophysical and Physical Attributes

Soft metrology based on machine learning: a review

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