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

Arami, Arash; Rechenmann, Julien David; Aminian, Kamiar

doi:10.1109/jsen.2014.2303644

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…Indeed, by fixing the PE insert with integrated Hall-effect sensors into the robotic knee simulator, human-like walking gait patterns can be performed and tested. Previous studies used only numerical simulations [19] or manual knee simulators to validate estimators' performances [16], [25]. However none of those techniques could closely simulate realistic gait patterns, which means that the reported errors cannot reflect the performance for normal walking condition.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Locally Linear Neuro-Fuzzy Estimate of the Prosthetic Knee Angle and Its Validation in a Robotic Simulator

2015

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Abstract-In this work we presented a low-power magnetic measurement system based on only two Hall-effect elements and a permanent magnet integrated into a smart knee prosthesis to accurately measure knee flexion-extension. The smart prosthesis was affixed in a robotic knee simulator that provides the squat movements, and also replicates different patterns of recorded gait from subjects. The squat movements were used to build linear and locally linear neuro-fuzzy estimators to translate the magnetic measurements into knee flexion angle. The simulated gait patterns then used to validate the models. The locally linear neuro-fuzzy estimator showed a clear benefit comparing to the linear regression models, by sequentially splitting the measurement space into subregions and find local fuzzy models for each region. The obtained RMS errors on test data were lower than 1.3° for the neuro-fuzzy estimates representing smaller than 3% of range of rotation. The result was compared with the estimates from a previously-designed configuration of three 2-D anisotropic magnetoresistive (AMR) sensors which tested in mentioned setup. We showed that by using the neurofuzzy model for two Hall-effect elements, similar performance to the AMR-based estimator can be obtained while the power consumption can be reduced more than three folds.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Locally Linear Neuro-Fuzzy Estimate of the Prosthetic Knee Angle and Its Validation in a Robotic Simulator

2015

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“…Samokalibracija AMR senzora opisana u [9] primenjuje pojednostavljeni linearni model senzora, čije parametre određuje korišćenjem gradijentnog algoritma. Pokazano je da rezidualna greška značajno zavisi od početne ocene parametara modela.…”

Section: Uvodunclassified

“…Parametrom o modelovan je rezidualni ofset analognog interfejsa, inherentni ofset mernih mostova [1,2,10], te stalno spoljašnje magnetno polje [5], dok parametri kx i ky modeluju različite osetljivosti mostova X i Y [1,9]. Kako je matrica G regularna, funkcija prenosa linearne kompenzacije ulaznog signala u dobija se inverzijom linearnog modela (1), kao u:…”

Section: Slika 2 -Struktura Mernih Mostova Mr Senzora Uglaunclassified

A method for selfcalibration of a magneto-resistive angular position sensor in servo systems

Ceperkovic¹,

Drndarevic²

2020

Tehnika

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Magneto-resistive and Hall angular position sensors are frequently used in servo systems, due to their reliability, longevity, and tolerance to environmental conditions. In this paper, a measurement error compensation is integrated into the servo system control structure. The compensation consists of two steps: a linear compensation of the magnetic field vector measurement error, and harmonic compensation of the angular position measurement error. The selfcalibration method uses data collected during rotation with maximal speed to identify parameters of the measurement process model. Identification is executed periodically, each time when the shaft makes a full rotation. The least squares method is utilized for identification of the linear compensation parameters, while the average rotational speed is used as an internal reference for the harmonic compensation parameters identification. The method, together with a fast convergence, increases the measurement accuracy by an order of magnitude. Experimentally achieved measurement error is below 0.7°, with residual variance below 0.05°, comparable with sensor differentiability.

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“…The sensor output is calculated from measurements obtained with magnetic field sensors. Commonly, sensors such as anisotropic magnetoresistive sensors (AMRs; Arami et al , 2014), giant magnetoresistive sensors (GMRs; Ausserlechner, 2010), tunneling magnetoresistive sensors (TMRs; Yamazaki et al , 2011) and Hall effect sensors (Wu et al , 2014) are used. Illustrations of the respective principles are shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of angular position sensors

Zangl

Faller

Granig

2017

COMPEL

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Purpose This paper aims to investigate the optimal placement and/or orientation of individual sensor elements within integrated angular position sensors, in particular magnetic sensors based on the Hall effect or magnetoresistive effects under consideration of random deviations (variations in the production process, environmental influences, noise) and correlations of these influences. Design/methodology/approach The authors utilize methods from optimal design of experiments to consider random deviations in a system-level model. In this sensor model, they include spatial dependencies of random deviations by means of a Gaussian random field. Based on this, an approach for fast determination of D-optimal designs is presented. Findings The results show that the intuitive and commonly used distributions of magnetic field sensors are actually optimal for the determination of in-phase and quadrature signals in the presence of spatial correlations, provided that the number of field sensors is higher or equal to three. However, in the uncorrelated case, the intuitive solutions are not the only optimal solutions or even not optimal at all. It is found that a restriction to symmetric designs is not necessary; thus, the design space can be extended to allow for further improvements, e.g. miniaturization, of such angular position sensors. Originality/value The proposed approach allows for the fast optimization based on a system model. Correlated random influences are considered by means of a Gaussian random field, which can be obtained either from measurements or from field simulations, e.g. using the finite element method. As this is done before the actual simulation, such evaluations are not needed during the optimization, which allows for very fast solution of the optimization problem. Therefore, the approach is well suited for application-dependent adjustment of sensor designs.

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

Cited by 8 publications

References 33 publications

Locally Linear Neuro-Fuzzy Estimate of the Prosthetic Knee Angle and Its Validation in a Robotic Simulator

Locally Linear Neuro-Fuzzy Estimate of the Prosthetic Knee Angle and Its Validation in a Robotic Simulator

A method for selfcalibration of a magneto-resistive angular position sensor in servo systems

Optimal design of angular position sensors

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