A sensitivity study of piezoresistive pressure sensor for robotic hand

Almassri, Ahmed M. M.; Hasan, Wan Zuha Wan; Ahmad, Siti Anom; Aris, Ishak

doi:10.1109/rsm.2013.6706574

Cited by 11 publications

(9 citation statements)

References 16 publications

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“…The three phenomena have been extensively studied and used in different types of sensing applications. However, among these three types of physical phenomena, the piezoresistive materials allow a better matricial pressure distribution measuring in biomedical applications because of their low cost and their deterministic behavior [10,11]. These characteristics have allowed the developing of commercial applications in the biomechanics field [12].…”

Section: Introductionmentioning

confidence: 99%

Test and fabrication of piezoresistive sensors for contact pressure measurement

Valle-Lopera

Castaño-Franco

Londoño

et al. 2017

Rev. Fac. Ing. Univ. Antioquia

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ABSTRACT:The use of contact pressure sensors has become popular in various engineering disciplines in recent years. They are used in characterization of vehicle tires, bearings, wind tunnels, prosthesis design, ergonomic analysis among other areas. These sensors are fabricated with materials that have certain properties such as piezoelectricity, piezoresistance and variable capacitance; however, the most used characteristic is the piezoresistive effect. This paper describes the fabrication of three different sensors using piezoresistive materials. Furthermore, a comparative technical study including a commercial sensor as a benchmark is done with the aim of selecting a suitable material when measuring contact pressure. The repeatability and hysteresis of each sensor were evaluated in a response to load test realized several times. A time drift test with a dead load was also performed for evaluating stability. Materials such as piezoresistive fabric or ink show to be suitable for applications where deformation and flexible sensors are required, Velostat is the least accurate but suitable for basic applications and in which a high resolution is not needed. Finally, some recommendations are given regarding the type of material to be used in pressure sensors for engineering applications, particularly in the biomedical field. RESUMEN:El uso de sensores de presión de contacto se ha popularizado en diferentes disciplinas de la ingeniería en los últimos años. Se utilizan en la caracterización de llantas para vehículos, rodamientos, túneles de viento, diseño de prótesis, análisis ergonómicos, entre otras áreas. Estos sensores, son diseñados con materiales que poseen ciertas propiedades tales como piezoelectricidad, piezorresistencia y capacitancia variable; sin embargo, la característica más usada es la piezorresistencia. En este artículo se describe la fabricación de tres sensores de presión diferentes usando materiales piezorresistivos. Adicionalmente, se realizó un estudio técnico comparativo incluyendo un sensor comercial usado como punto de referencia con el fin de seleccionar el material idóneo para medir presión por contacto. La repetibilidad y la histéresis de cada sensor fueron evaluadas en una prueba de respuesta a la carga realizada varias veces. También se llevó a cabo una prueba de desviación en el tiempo para evaluar estabilidad de la medición de un peso muerto. Los materiales como la tela o tinta piezoresistiva muestran ser adecuados para aplicaciones en las que haya deformación y se necesite de sensores flexibles, el Velostat es el menos preciso pero adecuado para aplicaciones básicas y en las cuales no se necesite de mucha resolución. Finalmente se presentan recomendaciones respecto al tipo de material que se debe utilizar en sensores de presión para diversas aplicaciones en ingeniería en general y en el campo biomédico en particular.

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Section: Introductionmentioning

confidence: 99%

Test and fabrication of piezoresistive sensors for contact pressure measurement

Valle-Lopera

Castaño-Franco

Londoño

et al. 2017

Rev. Fac. Ing. Univ. Antioquia

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“…Reference [67] used the genetic algorithm (GA) to control an ARM exoskeleton. An EMG signal was used as an input signal, and an optimization was performed using a GA [13].…”

Section: Arm Exoskeleton Employs a Genetic Algorithm As A Control Methodsmentioning

confidence: 99%

A Review on Upper Limb Rehabilitation Robots

2020

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Rehabilitation is the process of treating post-stroke consequences. Impaired limbs are considered the common outcomes of stroke, which require a professional therapist to rehabilitate the impaired limbs and restore fully or partially its function. Due to the shortage in the number of therapists and other considerations, researchers have been working on developing robots that have the ability to perform the rehabilitation process. During the last two decades, different robots were invented to help in rehabilitation procedures. This paper explains the types of rehabilitation treatments and robot classifications. In addition, a few examples of well-known rehabilitation robots will be explained in terms of their efficiency and controlling mechanisms.

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“…The sensor consisted of two layers of substrate. This substrate is composed of polyester film that enhances the force sensing and improves the performance including linearity, hysteresis, drift and temperature sensitivity compared to any other thin film [ 34 , 35 , 36 , 37 , 38 ]. Furthermore, it is flexible and ultrathin enough so that the researchers and designers can use it in different integrated applications, as well as for applications that are oriented towards manipulative tasks based on wearable robotic hand glove.…”

Section: Methodsmentioning

confidence: 99%

Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network

Almassri

Hasan

Ahmad

et al. 2018

Sensors

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This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.

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A sensitivity study of piezoresistive pressure sensor for robotic hand

Cited by 11 publications

References 16 publications

Test and fabrication of piezoresistive sensors for contact pressure measurement

Test and fabrication of piezoresistive sensors for contact pressure measurement

A Review on Upper Limb Rehabilitation Robots

Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network

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