Magnetostrictive Tactile Sensor Array Based on L-Shaped Galfenol Wire and Application for Tilt Detection

Yang, Huiwen; Weng, Ling; Wang, Bowen; Li, Zhuolin; Sun, Ying; Huang, Wei-Lin; Zhang, Xuehui

doi:10.1109/jsen.2022.3177207

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…According to the magnetization theory model of magnetostrictive materials, the magnetic induction strength inside the Fe-Ni alloy wire can be obtained as follows: where a is the hysteresis magnetization shape factor, M S is the saturation magnetization strength, a is the domain-wall interaction coefficient, λ S is the saturation magnetostriction coefficient and σ is the stress applied to the Fe-Ni alloy wire (Yang et al , 2022a, 2022b).…”

Section: Sensor Output Voltage Model and Output Characteristicsmentioning

confidence: 99%

“…where a is the hysteresis magnetization shape factor, M S is the saturation magnetization strength, a is the domain-wall interaction coefficient, l S is the saturation magnetostriction coefficient and s is the stress applied to the Fe-Ni alloy wire (Yang et al, 2022a(Yang et al, , 2022b.…”

Section: Output Voltage Modelmentioning

confidence: 99%

“…Gao et al (2021) designed a biomimetic magnetostrictive tactile sensor using Fe-Ga alloy, permanent magnets and Hall sensors, which is suitable for robotic gripping of lightweight and irregular objects, and provides timely feedback of the stress information on the gripping surface and can be used to detect the geometry of the touching object. Yang et al (2022a, 2022b) designed a high-sensitivity, high-resolution magnetostrictive sensor array using tunneling magnetoresistive (TMR) elements as magnetic detection elements, which was mounted on an intelligent robotic hand to make it sensitive to touch and to perform fine manipulation and could be used to recognize the hardness of the object. Weng et al (2023) designed a cantilever beam type magnetostrictive sensor using Hall elements as magnetic detection elements, which can accurately measure the depth information on the surface of an object by sliding motion, and can be used for the recognition and reconstruction of 3D contours.…”

Section: Introductionmentioning

confidence: 99%

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Structural design of magnetostrictive sensing glove and its application for gesture recognition

Hu,

Weng,

Liu

et al. 2024

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Purpose Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition. Design/methodology/approach A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network. Findings The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures. Research limitations/implications The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures. Practical implications A new approach to gesture recognition using wearable devices. Social implications This study has a broad application prospect in the field of human–computer interaction. Originality/value The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

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Section: Sensor Output Voltage Model and Output Characteristicsmentioning

confidence: 99%

Section: Output Voltage Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structural design of magnetostrictive sensing glove and its application for gesture recognition

Hu,

Weng,

Liu

et al. 2024

Self Cite

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“…Human activity recognition (HAR) [1], [2], [3], [4] is a crucial application that greatly enhances machine understanding and analysis of human activities from a human perspective. This understanding has significant implications for various fields, including augmented reality (AR) [5], human-computer interaction [6], [7], and health monitoring [8], [9]. Wearable sensors enable more convenient self-centered data collection and the ability to acquire a continuous stream of new data during usage, without specific shooting paradigms, e.g.…”

Section: Introductionmentioning

confidence: 99%

Continual Egocentric Activity Recognition with Foreseeable-Generalized Visual-IMU Representations

He,

Cheng,

Qiu

et al. 2024

Preprint

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The rapid development of wearable sensors promotes convenient data collection in human daily life. Human Activity Recognition (HAR), as a prominent research direction for wearable applications, has made remarkable progress in recent years. However, existing efforts mostly focus on improving recognition accuracy, paying limited attention to the model’s functional scalability, specifically its ability for continual learning. This limitation greatly restricts its application in open-world scenarios. Moreover, due to storage and privacy concerns, it is often impractical to retain the activity data of different users for subsequent tasks, especially egocentric visual information. Furthermore, the imbalance between visual-based and inertial-measurement-unit (IMU) sensing modality introduces challenges of lack of generalization when employing conventional continual learning techniques. In this paper, we propose a motivational learning scheme to address the limited generalization caused by the modal imbalance, enabling foreseeable generalization in a visual-IMU multimodal network. To overcome forgetting, we introduce a robust representation estimation technique and a pseudo-representation generation strategy for continual learning. Experimental results on the egocentric multimodal activity dataset UESTC-MMEA-CL demonstrate the effectiveness of our proposed method. Furthermore, our method effectively leverages the generalization capabilities of IMU-based modal representations, outperforming general and state-of-the-art continual learning methods in various task settings.

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Design of Magnetic Tactile Sensor Arrays for Intelligent Floorboard Based on the Demand of Older People

Wang,

Weng,

Wang

2023

Lecture Notes in Electrical Engineering

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Magnetostrictive Tactile Sensor Array Based on L-Shaped Galfenol Wire and Application for Tilt Detection

Cited by 8 publications

References 25 publications

Structural design of magnetostrictive sensing glove and its application for gesture recognition

Structural design of magnetostrictive sensing glove and its application for gesture recognition

Continual Egocentric Activity Recognition with Foreseeable-Generalized Visual-IMU Representations

Design of Magnetic Tactile Sensor Arrays for Intelligent Floorboard Based on the Demand of Older People

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