Velostat Sensor Array for Object Recognition

Yuan, Liangqi; Qu, Hongwei; Li, Jia

doi:10.1109/jsen.2021.3132793

Cited by 26 publications

(10 citation statements)

References 55 publications

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“…Building upon our previous research [ 23 ], piezoresistive sensors are applied across diverse domains, including medical applications [ 24 ], object recognition [ 25 ], facial recognition (smile and breath detection [ 26 ], and eye blink tracking [ 27 ]), and motion monitoring [ 28 ]. These sensors are able to measure changes in electrical resistance in response to pressure or strain, a phenomenon arising from the intricate reorganization of charged particles within the material.…”

Section: Description Of the Piezoresistive Sensormentioning

confidence: 99%

Design and Characterization of Piezoresistive Sensors for Non-Planar Surfaces and Pressure Mapping: A Case Study on Kayak Paddle

Laaraibi,

Jodin,

Depontailler

et al. 2023

Sensors

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This article focuses on the design of a sensor system for a non-planar surface, in particular a cylindrical shape, such as a kayak paddle. The main objective is to develop a piezoresistive sensor system to measure the pressure exerted by the hand on the shaft. The study begins with static characterization of the sensors, including dispersion analysis to assess their sensitivity, linearity and measurement range. A calibration process is carried out using a dedicated test bench, and an inverse viscoelastic model is used to establish an accurate relationship between the measured resistance and the corresponding pressure. The sensor system is connected to a data acquisition board equipped with an analog-to-digital converter (ADC) that enables the direct conversion of analog data into digital resistance values. Furthermore, Bluetooth Low Energy (BLE) wireless communication is employed to facilitate data transfer to a computer, enabling a detailed pressure mapping of the kayak paddle and real-time data collection. The calibrated sensors are then tested and validated on the kayak paddle, facilitating the mapping of pressure zones on the paddle surface. This mapping provides information for locating areas of high pressure exertion during kayaker movements.

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Section: Description Of the Piezoresistive Sensormentioning

confidence: 99%

Design and Characterization of Piezoresistive Sensors for Non-Planar Surfaces and Pressure Mapping: A Case Study on Kayak Paddle

Laaraibi,

Jodin,

Depontailler

et al. 2023

Sensors

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“…Velostat-based piezoresistive pressure sensor arrays have been widely studied and used in recent years. Despite its non-ideal electrical properties and crosstalk in numerous recent studies [22]- [24], its low price, flexibility, and scalability have attracted attention. In this paper, we select a Velostat sensor array as a tool to collect pressure distribution and complete HAR with deep learning (DL).…”

Section: A Velostat-based Applicationmentioning

confidence: 99%

“…Hu et al [32] developed an on-the-fly human sleep recognition system using pressure sensitive conductive sheet and a fourlayer CNN architecture for sleep classification, with transfer learning to prevent overfitting and improve classification accuracy. Yuan et al [22] established an object recognition broad to classify ten objects and conducted a systematic material analysis and study of Velostat, including resistance sensitivity, quasi-static response, and crosstalk issues. Zhang et al [37] focused on gait recognition using a combination of pressure signals and acceleration signals to make up for the lack of data provided by a single sensor and transmitted the data to a computer for signal processing, and building a k nearest neighbor (kNN) model to test gait pattern recognition effect.…”

Section: A Velostat-based Applicationmentioning

confidence: 99%

“…Consequently, after j CNNs are used to extract j image tensors I, a final layer process the relationship between images in the stream, as a DNN classifier of the proposed SPeM system. Specifically, we consider [22] proposed CNN RESNET-PI as the pressure image feature extractor of DNN. RESNET-PI reduces the number of model parameters by removing one residual block from ResNet-18 [46].…”

Section: Deep Neural Network For Image Streammentioning

confidence: 99%

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Smart Cushion Based on Pressure Sensor Array for Human Sitting Posture Recognition

Yuan

2021

2021 IEEE Sensors

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With the emphasis on healthcare, early childhood education, and fitness, non-invasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied due to its advantages of simple structure, easy access, visualization application, and harmlessness. This paper introduces a smart pressure e-mat (SPeM) system based on a piezoresistive material Velostat for human monitoring applications, including sleeping postures, sports, and yoga recognition. After a subsystem scans e-mat readings and processes the signal, it generates a pressure image stream. Deep neural networks (DNNs) are used to fit and train the pressure image stream and recognize the corresponding human behavior. Four sleeping postures and five dynamic activities inspired by Nintendo Switch Ring Fit Adventure (RFA) are used as a preliminary validation of the proposed SPeM system. The SPeM system achieves high accuracies on both applications, which demonstrates the high accuracy and generalization ability of the models. Compared with other pressure sensor-based systems, SPeM possesses more flexible applications and commercial application prospects, with reliable, robust, and repeatable properties.

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“…Wearable health management systems offer low-cost solutions for non-invasive personal health monitoring, enabling early diagnosis and better treatment for various medical conditions. The advanced intelligent IoT proposes the innovative textile pressure Velostat sensors to observe twenty-one people in four lying positions for preventing fall accidents and bedsores through the message queuing telemetry transport (MQTT) protocol and Arduino-based hardware [22]. Additionally, an intelligent Velostat mat for monitoring body position by obtaining pressure distribution on the patient's body and posture prediction by ANN for medical applications is presented in [23].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Medical Velostat Pressure Sensor Mat Based on Artificial Neural Network and Arduino Embedded System

Kciuk,

Kowalik,

Lo Sciuto

et al. 2023

ASI

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The promising research on flexible and tactile sensors requires conducting polymer materials and an accurate system for the transduction of pressure into electrical signals. In this paper, the intelligent sensitive mat, based on Velostat, which is a polymeric material impregnated with carbon black, is investigated. Various designs and geometries for home-made sensor mats have been proposed, and their electrical and mechanical properties, including reproducibility, have been studied through the tests performed. The mat pressure sensors have been interfaced with an Arduino microcontroller in order to monitor, read with high precision, and control the variation of the resistance under applied pressure. An approximation method was then developed based on a neural network algorithm to explore the relationship between different mat shapes, the pressure and stresses applied on the mat, the resistance of the conductive Velostat material, and the number of active sensing cells in order to control system input signal management.

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Velostat Sensor Array for Object Recognition

Cited by 26 publications

References 55 publications

Design and Characterization of Piezoresistive Sensors for Non-Planar Surfaces and Pressure Mapping: A Case Study on Kayak Paddle

Design and Characterization of Piezoresistive Sensors for Non-Planar Surfaces and Pressure Mapping: A Case Study on Kayak Paddle

Smart Cushion Based on Pressure Sensor Array for Human Sitting Posture Recognition

Intelligent Medical Velostat Pressure Sensor Mat Based on Artificial Neural Network and Arduino Embedded System

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