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The Rasch model, named after Georg Rasch, is a psychometric model for analyzing categorical data, such as answers to questions on a reading assessment or questionnaire responses, as a function of the trade-off between the respondent's abilities, attitudes, or personality traits, and the item difficulty. For example, they may be used to estimate a student's reading ability or the extremity of a person's attitude to capital punishment from responses on a questionnaire. In addition to psychometrics and educational research, the Rasch model and its extensions are used in other areas, including the health profession, agriculture, and market research The mathematical theory underlying Rasch models is a special case of item response theory. However, there are important differences in the interpretation of the model parameters and its philosophical implications that separate proponents of the Rasch model from the item response modeling tradition. A central aspect of this divide relates to t

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Reference-Free Automated Magnetic Sensor Calibration for Angle Estimation in Smart Knee Prostheses

Kamiar Aminian, Arash Arami, Julien David Rechenmann

In this work, we present a method for automated calibration of an implanted anisotropic magnetoresistive (AMR) sensor for measuring the internal-external rotation (IE) in a prosthetic knee without using any reference measurement. The measurement system consists of a permanent magnet and a 2-D AMR sensor configured and embedded into the prosthesis. Using the physical relation between the measurements of channels of sensor, we proposed a reference-free calibration method through the optimization. The parameters of the measurement model of the sensor obtained via the calibration were directly used in an angle estimator. Two different optimization approaches were tested, and the resultant estimates were compared to the reference angle with the expected ± standard deviation of error of -1.69°±0.65° and -1.17°±1.13°. The reference-free calibrated estimators were also compared to the reference-dependent least-square estimators. This latter comparison showed an offset error difference among the estimators while the coefficients of determination for the both reference-free and reference-dependent estimators were high, i.e. larger than 0.91. The good performances were maintained even when using a small portion of data for the optimization. The sensor-magnet misalignment effect was also investigated. The misalignment slightly worsened the mean error of the estimates, but neither dramatically affected the variance of error nor the coefficient of determination.

Institute of Electrical and Electronics Engineers2014

Image reconstruction method based on a trained non-linear mapping

Marcel Arditi, Adrien Besson, Florian Martinez, Dimitris Perdios, Jean-Philippe Thiran

The present invention concerns an array processing image reconstruction method comprising: receiving (13) a set of echo waveforms as measurements from the object of interest; defining (15) a measurement model linking an unknown image of the object to the measurements; defining (17) a data fidelity functional using the measurement model; defining (19) a regularisation functional using a trained non-linear mapping, the regularisation functional comprising prior knowledge about the unknown image; defining (21) an optimisation problem involving the data fidelity functional and the regularisation functional for obtaining a first image estimate of the unknown image; and solving (23) the optimisation problem to obtain the first image estimate.

2020

Model-based image reconstruction method

Marcel Arditi, Adrien Besson, Dimitris Perdios, Jean-Philippe Thiran

The present invention concerns a pulse wave image reconstruction method to be used for example in ultrasound imaging. The proposed method is based on an image measurement model and its adjoint operator. The proposed method introduces matrix-free formulations of the measurement model and its adjoint operator. The proposed method has the advantage that the reconstructed image has a very high quality and that it can be reconstructed quickly.

2019

Psychometrics

Psychometrics is a field of study within psychology concerned with the theory and technique of measurement. Psychometrics generally refers to specialized fields within psychology and education dev

Item response theory

In psychometrics, item response theory (IRT) (also known as latent trait theory, strong true score theory, or modern mental test theory) is a paradigm for the design, analysis, and scoring of tests,

Level of measurement

Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables. Psychologist Stanley Smith Stevens developed the best-kno

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