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Sensors 2018, 18(8), 2561; https://doi.org/10.3390/s18082561

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

1
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2–4 Hibikino, Wakamatsu-ku, Kitakyushu 808-0196, Japan
2
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
3
Institute of Advanced Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
4
Malaysian Research Institute on Ageing, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
*
Authors to whom correspondence should be addressed.
Received: 16 July 2018 / Revised: 29 July 2018 / Accepted: 1 August 2018 / Published: 5 August 2018
(This article belongs to the Special Issue Force and Pressure Based Sensing Medical Application)
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Abstract

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. View Full-Text
Keywords: pressure sensors; self-calibration algorithm; artificial neural network; pressure measurement system; real-time application; robotic hand glove; rehabilitation applications pressure sensors; self-calibration algorithm; artificial neural network; pressure measurement system; real-time application; robotic hand glove; rehabilitation applications
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Almassri, A.M.M.; Wan Hasan, W.Z.; Ahmad, S.A.; Shafie, S.; Wada, C.; Horio, K. Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network. Sensors 2018, 18, 2561.

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