Modeling and Evaluation of Low-Cost Force Sensors

Lebosse, C.; Renaud, Pierre; Bayle, B.; Mathelin, Michel de

doi:10.1109/tro.2011.2119850

Cited by 79 publications

(64 citation statements)

References 13 publications

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“…In loading conditions similar to those used in this study [31][32][33]. In this context, and following the results presented in Table 2, we concluded that interpolation generally affects the features extracted from pressure maps, but the effects are not extreme.…”

Section: Primary Findingssupporting

confidence: 80%

Effect of interpolation on parameters extracted from seating interface pressure arrays

Wininger

Crane

2014

J Rehabil Res Dev

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Abstract-Interpolation is a common data processing step in the study of interface pressure data collected at the wheelchair seating interface. However, there has been no focused study on the effect of interpolation on features extracted from these pressure maps, nor on whether these parameters are sensitive to the manner in which the interpolation is implemented. Here, two different interpolation paradigms, bilinear versus bicubic spline, are tested for their influence on parameters extracted from pressure array data and compared against a conventional low-pass filtering operation. Additionally, analysis of the effect of tandem filtering and interpolation, as well as the interpolation degree (interpolating to 2, 4, and 8 times sampling density), was undertaken. The following recommendations are made regarding approaches that minimized distortion of features extracted from the pressure maps: (1) filter prior to interpolate (strong effect); (2) use cubic interpolation versus linear (slight effect); and (3) ensure nominal difference between interpolation orders of 2, 4, and 8 times (negligible effect). We invite other investigators to perform similar benchmark analyses on their own data in the interest of establishing a community consensus of best practices in pressure array data processing.

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Section: Primary Findingssupporting

confidence: 80%

Effect of interpolation on parameters extracted from seating interface pressure arrays

Wininger

Crane

2014

J Rehabil Res Dev

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“…The performance of the CMA to estimate F was comparatively evaluated with the linear regression model (1), which is the manufacturer recommended method. It must be noted that the linear regression model is the preferred method in research applications [4,5,[9][10][11][12][13]. Two sets of m and b constants were calculated using the triangle force profiles of Section III.B.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Since this paper focusses on reducing the force MSE through conductance readings only, capacitance measurements are not henceforth considered. For linearization purposes, conductance variations -measured through Vo1 -have been preferably used in several studies to estimate F [8,9,12,15]; this is possible by inverting the model of Fig. 1e, which yields:…”

Section: Experimental Set-upmentioning

confidence: 99%

“…Unfortunately, the overall performance of FSRs is poor compared to well-established force sensing solutions such as load cells and strain gauges. Previous works from Lebosse [9], Hollinger [10] and Komi [11] present a comprehensive review on FSR limitations. Hysteresis and drift are typically one or two orders of magnitude greater in FSRs than in load cells.…”

Section: Introductionmentioning

confidence: 99%

“…Relevant works on this scope have been developed by Lebosse [9], Schofield [5], Dabling [12], Vecchi [13] and Urban [14]. Likewise, authors' previous work has demonstrated that the A201 sensors, working on the piezoresistive principle, are also capable of exhibiting a piezocapacitive response.…”

Section: Introductionmentioning

confidence: 99%

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Experimental characterization, modeling and compensation of hysteresis in force sensing resistors

Paredes-Madrid

Matute

Cruz-Pacheco

et al. 2018

DYNA

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Force Sensing Resistors (FSRs) exhibit considerable amounts of hysteresis and repeatability error inhibiting their usage in applications that require high-accurate force readings. This paper presents the hysteresis characterization and modeling of the Tekscan A201-1 FSR employing the Preisach Operator (PO) function. In order to compensate for hysteresis during sensor operation, the inverse PO was numerically found on the basis of the Closest Match Algorithm (CMA). A test bench, capable of handling sixteen sensors simultaneously, was built, which allowed the characterization and later testing of the CMA. Grip force profiles were applied to the sensors during testing and the experimental results showed a considerable reduction in the force estimation error compared with the linear regression method proposed by the manufacturer. These results enable a wider use of FSRs in applications with tight accuracy requirements. Finally, a generalized sensor model for hysteresis compensation that simplifies the obtaining of PO parameters is presented.Keywords: hysteresis; force sensing resistors; Preisach operator; closest match algorithm.Caracterización experimental, modelado y compensación de la histéresis en sensores de fuerza resistivos Resumen Los Sensores de Fuerza Resistivos (FSRs) despliegan cantidades considerables de histéresis y de error de repetitividad que inhiben su uso en aplicaciones que requieren lecturas de fuerza de alta precisión. En este trabajo se presenta la caracterización y modelado de histéresis del sensor de presión Tekscan A201-1 empleando la función Operador de Preisach (OP). Con el fin de compensar la histéresis durante el funcionamiento del sensor, el OP inverso se halló numéricamente sobre la base del algoritmo de coincidencia más cercana (CMA). Se construyó un banco de pruebas, capaz de manejar dieciséis sensores simultáneamente, lo que permitió la caracterización y posterior prueba del CMA. Los perfiles de fuerza de agarre se aplicaron a los sensores durante la prueba y los resultados experimentales mostraron una reducción considerable del error de estimación de la fuerza en comparación con el método de regresión lineal propuesto por el fabricante. Estos resultados abren el camino para un uso más amplio de los FSRs en aplicaciones con exigentes requisitos de precisión. Finalmente, un modelo de sensor generalizado para compensación de histéresis que simplifica la obtención de los parámetros PO, es presentado.Palabras clave: sensores de fuerza resistivos; operador de Preisach; algoritmo de coincidencia más cercana.

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Introduction

Richter¹

2013

Robotized Transcranial Magnetic Stimulation

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Modeling and Evaluation of Low-Cost Force Sensors

Cited by 79 publications

References 13 publications

Effect of interpolation on parameters extracted from seating interface pressure arrays

Effect of interpolation on parameters extracted from seating interface pressure arrays

Experimental characterization, modeling and compensation of hysteresis in force sensing resistors

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