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Structure of the magnetostrictive tactile sensing unit; (a) Schematic illustration of the magnetostrictive tactile sensing unit; (b) Photograph of the magnetostrictive tactile sensing unit; (c) Section view of the magnetostrictive tactile sensing unit.
Source publication
A magnetostrictive tactile sensing unit and the integrated sensor array have been developed, based on the sensing process of human skin and the inverse magnetostrictive effect. The working mechanism of the magnetostrictive tactile sensing unit has theoretically been studied by using electromagnetism theory and cantilever beam theory, and the relati...
Contexts in source publication
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... signals from the receptors are transported to the brain through nerve fibres [21]. Based the structure and perception process of human skin, the magnetostrictive tactile sensing unit was designed, which is composed of Galfenol wires, Hall sensor, contact pin, rectangular permanent magnets, and resin shell, as shown in Fig.1 (a). When the contact pin of the tactile sensing unit touches an object, the Galfenol wires are deformed, and further cause the change of output voltage for the Hall sensor. ...
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... the resin shell and the contact pin are made of an opaque white SOMOS Imagine 8000 material by 3D printing, and its Young's modulus, Poisson's ratio, and Shore hardness are 2510 MPa, 0.41, and 79 HD, respectively. The size of the sensing unit is 9.5 mm × 6 mm × 6 mm, and the weight is 0.535 g, as shown in Fig.1 (b). ...
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... the magnetization of the Galfenol wires will change due to the inverse magnetostrictive effect, and the output voltage signal can be measured by the Hall sensor. Therefore, by using the sensing unit, the applied external force acted on the contact pin could be measured (Fig.1 (c)). ...
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... length and radial directions of the Galfenol wires are, respectively, set as x-axis and z-axis ( Fig.1(c)). According to Euler-Bernoulli beam theory, the stress can be expressed as σ (x, z) = F(l − x)z/3 I , when the Galfenol wires are bent with external force F. ...
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... to the results in Fig.8, the minimum distance for l l is 9 mm and for l v is 17.5 mm can be determined. The magnetostrictive tactile sensing unit in Fig.1 has been integrated into a sensor array, and Fig.9 (a) shows a schematic of a 2 × 4 sensor array. ...
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... The size of a sensing unit is 9.5 mm × 6 mm × 6 mm. The distance l l between two adjacent units, laterally arranged, is 9 mm and the distance of l v , vertically arranged, is 17.5 mm for the sensor array. It is fabricated on a printed circuit board (PCB), and the photograph of the as-fabricated magnetostrictive tactile sensor array is shown in Fig.10 (a). The magnetostrictive tactile sensor array is packaged in the white resin shell, as shown in Fig.10 (b), and its size is 22 mm × 37 mm × 14 mm and its weight is 16.7 ...
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... arranged, is 9 mm and the distance of l v , vertically arranged, is 17.5 mm for the sensor array. It is fabricated on a printed circuit board (PCB), and the photograph of the as-fabricated magnetostrictive tactile sensor array is shown in Fig.10 (a). The magnetostrictive tactile sensor array is packaged in the white resin shell, as shown in Fig.10 (b), and its size is 22 mm × 37 mm × 14 mm and its weight is 16.7 ...
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... sensing unit of the sensor array has three electrical lines that are: a power line, a ground line and a signal line. Four sensing units of the sensor array share the same power line and the ground line. Fig.10 (c) exhibits the cable arrangement of the magnetostrictive tactile sensor array, including four signal lines, one power line, and one ground line. The analog voltage signals from the signal lines of the sensor array are converted into digital voltage signals through the analog-to-digital (AD) converter and sent to the PC for further ...
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... manipulator has two fingers, and each finger has two degrees of freedom, actuated by a servo motor. The proximal phalanx, flexible joints, and distal phalanx make up the fingers of manipulator (Fig.11). The joints are made from flexible materials. ...
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... order to determine the exact position of each contact point, each sensing unit in sensor arrays is marked ''A to D'' for each row and each column is numbered ''1 to 4'' (1 and 4 are up column, 2 and 3 are down column). So the sensing units are marked with a combination of letters and numbers from ''A1'' to ''D4'', as shown in Fig.11. ...
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... apple is gripped according to the above-mentioned control method, as shown in Fig.12. The output voltage of four sensing units with the greatest voltage variation (B1, B4, C4, and C3) of the magnetostrictive tactile sensor arrays during the grip is displayed in Fig.13. ...
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... apple is gripped according to the above-mentioned control method, as shown in Fig.12. The output voltage of four sensing units with the greatest voltage variation (B1, B4, C4, and C3) of the magnetostrictive tactile sensor arrays during the grip is displayed in Fig.13. From 0 to 2.1 s, the fingers are controlled to move to the apple at a constant speed. ...
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... magnetostrictive tactile sensor arrays have the ability to sense the magnitude and distribution of tactile force. When the above grip reaches steady state, the contact force distribution and magnitude of the sensor arrays can be obtained, as shown in Fig.14. The sensor arrays can differentiate the contact force in different load-bearing areas, and it can be used to perceive the force distribution. ...
Citations
... Soft tactile sensors are mainly designed using biological inspiration from human skin, which has distributed mechanical receptors that can sense force, stiffness, temperature, texture, and vibration [8], [9], [10]. These sensors are based on optical [11], [12], [13], [14], resistive [15], [16], [17], capacitive [18], [19], [20], [21], magnetic [22], [23], [24], [25], inductive [26], [27], [28] and piezoelectric [29], [30], [31], [32], [33] principles. Besides these soft tactile sensors, vision-based sensors are also used for tactile perception in robotics [34], [35], [36], [37], [38], [39]. ...
Contact localization is an essential feature of tactile sensors for biomedical, rehabilitation, surgical, and service robot applications where interaction with the environment is needed. In this work, we have developed a piezoelectric (PZT) based soft tactile sensor using the layer-by-layer fabrication technique. We placed
$4\times 4$
mm2 size PZT elements in
$3\times 3$
matrix form with 3 mm spaces and embedded them in silicone layers. Although the sensor has discrete brittle sensing elements, the silicone layer distributes the contact force on the surface area, which enables continuous contact localization on the sensor. Here, we studied the estimation using the collected and conditioned output voltage signals of PZT elements. First, we used a logic-based algorithm that gives results qualitatively using a developed user interface. Then we used machine learning algorithms to estimate the contact position coordinates numerically. Finally, we applied Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), and k-Nearest Neighbor (KNN) algorithms and compared the results. We got our best results using a two-layer ANN algorithm which provides a mean deviation of 0.08 mm for the X-axis and 0.07 mm for the Y-axis. Besides, we had 97.20% of testing estimations with an error lower than 0.35 mm. Therefore, we believe our tactile sensor gives promising results and can be used in small-scale applications where contact localization estimation is desired.
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.
A finite element model for Magnetoelectric (ME) effect of Metglas/PVDF laminates is built in this study, considering the mechanical energy loss and bonding layer. By introducing a loss factor, the ME voltage value (αME) of composite reduces from 14,467.6 to 148.4 V/(cm·Oe), which are more consistent with the experimental results. The detailed analysis focuses on the influences of geometric parameters, mechanical energy loss, and the adhesive layer on the magnetostrictive effect in ME composites. It was observed that reducing the length and increasing the thickness enhanced demagnetization of the magnetostrictive phase while diminishing ME effect. Furthermore, an increase in the thickness of the piezoelectric phase and epoxy resin resulted in a reduction in stress transfer and a decrease in the ME effect. The amplification of the material loss factor led to an increase in stress loss, consequently weakening the ME effect. The ME effect reached its peak value of 192.7 V/(cm·Oe) when the Young's modulus of epoxy resin was approximately 0.1 GPa, and a significant increase in resonance frequency was observed with an increasing Young's modulus of epoxy resin. This work can make the modeling results closer to the actual experimental phenomena, which is crucial in guiding the development and performance optimization of ME materials, as well as encouraging their practical applications.
Tactile sensors enabling human-like perception are crucial for modern intelligent robotics. This study developed a biomimetic magnetostrictive tactile sensor consisting of Galfenol wires, permanent magnets, and a Hall sensor. This novel tactile sensor exhibits a static sensitivity of 23.86 mV/N and a measurement range up to 3 N. The sensitivity is stable up to 4 Hz. A theoretical model of the tactile sensor unit was derived to correlate the output voltage with the applied force. The modeling result shows an error of 1.65% in sensitivity compared to the experimental data. The response time and recovery time of the sensor are only 11 ms and 10 ms, faster than nanoscale type of sensor. A
$3\times 3$
magnetostrictive sensor array was constructed by integrating multiple magnetostrictive tactile sensors on a flexible printed circuit board. Since the outputs of the sensing units are not affected by each other, the array can be expanded as needed. Experimental results show that the sensor array is able to detect the geometry of the object in contact.
