Fig 4 - uploaded by Manuel Reis Carneiro
Content may be subject to copyright.
A: Relation between the pressure sensor's resistance and the applied pressure. B: Length of finger skin in resting position -8cm -(in red) and angle of finger bending (in yellow). C: Length of finger skin in bent (stretched) position -10.5cm -(in red) and angle of finger bending (in yellow). D: Relation between the strain-sensor's resistance and the bending angle of the user's finger. E,F,G,H,I: Output visualization for one strain sensor (Red) and one pressure sensor (Blue), printed on a glove. E: Bending finger. F: Relaxing finger. G, H, I: Fingertip touches.
Source publication
This article introduces a tailor-made smart glove that integrates resistive strain and pressure sensors. A homemade silver-based stretchable ink is screen printed over textile to create a strain sensor that estimates finger bending for all fingers. Another piezoresistive ink was synthesized, and screen printed on fingertips, using a specific archit...
Contexts in source publication
Context 1
... removed, the stencil leaves behind a full strain sensor ( Fig. 1Aiv, Bv), which is let to dry at room temperature for another 30 minutes. In Fig 1D 4 strain sensors printed on a glove are presented. The fabricated strain sensor increases its resistance with the increase of the applied strain, since the carbon particles get more separated from each other, as observed in Fig. 1C. ...
Context 2
... simple experiment was performed in order to evaluate which stretch displacement would a textile placed in a finger joint be subjected to. Following the procedure shown in Fig. 4B and C, in red, the length of the upper part of the finger was measured in the resting position (Fig. 4B) and in the maximum bent position (Fig. 4C). The obtained values were respectively 8cm and 10.5cm, corresponding to a strain of 31.25%. Fig. 4D shows the relation between the sensor's resistance output (acquired with a Gw Instek ...
Context 3
... simple experiment was performed in order to evaluate which stretch displacement would a textile placed in a finger joint be subjected to. Following the procedure shown in Fig. 4B and C, in red, the length of the upper part of the finger was measured in the resting position (Fig. 4B) and in the maximum bent position (Fig. 4C). The obtained values were respectively 8cm and 10.5cm, corresponding to a strain of 31.25%. Fig. 4D shows the relation between the sensor's resistance output (acquired with a Gw Instek GDM-8351) and the degree to which a finger is bent. This angle of finger bending was defined, as the angle ...
Context 4
... simple experiment was performed in order to evaluate which stretch displacement would a textile placed in a finger joint be subjected to. Following the procedure shown in Fig. 4B and C, in red, the length of the upper part of the finger was measured in the resting position (Fig. 4B) and in the maximum bent position (Fig. 4C). The obtained values were respectively 8cm and 10.5cm, corresponding to a strain of 31.25%. Fig. 4D shows the relation between the sensor's resistance output (acquired with a Gw Instek GDM-8351) and the degree to which a finger is bent. This angle of finger bending was defined, as the angle formed by the fingertip, the proximal ...
Context 5
... would a textile placed in a finger joint be subjected to. Following the procedure shown in Fig. 4B and C, in red, the length of the upper part of the finger was measured in the resting position (Fig. 4B) and in the maximum bent position (Fig. 4C). The obtained values were respectively 8cm and 10.5cm, corresponding to a strain of 31.25%. Fig. 4D shows the relation between the sensor's resistance output (acquired with a Gw Instek GDM-8351) and the degree to which a finger is bent. This angle of finger bending was defined, as the angle formed by the fingertip, the proximal interphalangeal joint and the metacarpophalangeal joint, as observed in Fig 4B and C, in yellow. Since the ...
Context 6
... 4D shows the relation between the sensor's resistance output (acquired with a Gw Instek GDM-8351) and the degree to which a finger is bent. This angle of finger bending was defined, as the angle formed by the fingertip, the proximal interphalangeal joint and the metacarpophalangeal joint, as observed in Fig 4B and C, in yellow. Since the developed printed strain sensors were printed below the distal interphalangeal joint, the contribution of this joint for bending the finger was disregarded and thus the finger movement leads to the almost-linear behaviour of the strain sensor observed in Fig. 4D. ...
Context 7
... joint and the metacarpophalangeal joint, as observed in Fig 4B and C, in yellow. Since the developed printed strain sensors were printed below the distal interphalangeal joint, the contribution of this joint for bending the finger was disregarded and thus the finger movement leads to the almost-linear behaviour of the strain sensor observed in Fig. 4D. The average hystheresis shown by this sensor is 220Ω (min 150Ω and max ...
Context 8
... pressure sensor was connected to a Multimeter (Gw Instek GDM-8351) in order to read its output resistance when the applied pressure varied. To vary the pressure, the weight placed on top of the pressure sensor (1cm 2 contact area) was sequentially increased, leading to the results in the plot depicted in Fig. 4A. It is observable that the pressure sensor's response is consistent and repeatable over sequential cycles. Although when the fingertip is not pressed and at lower pressures (below 2000Pa) the data shows some hysteresis, this this can be overcome by considering that a fingertip touch happens only above 2000Pa, which is enough for most ...
Context 9
... Fig. 4E, F, G, H, I, the output of one strain sensor and one pressure sensor printed on a glove can be observed in the form of a graph. The results of this output visualization show that fingertip touches are easily recognized as "valleys" in the blue signal (Fig. 4G, H, I). On the other hand, finger bending leads to a rise in the red signal that is ...
Context 10
... Fig. 4E, F, G, H, I, the output of one strain sensor and one pressure sensor printed on a glove can be observed in the form of a graph. The results of this output visualization show that fingertip touches are easily recognized as "valleys" in the blue signal (Fig. 4G, H, I). On the other hand, finger bending leads to a rise in the red signal that is proportional to the bending degree of the finger (Fig. 4E), while relaxing the finger decreases the output value (Fig. 4F). It can also be observed that the recovery of the sensor is very fast: looking at Fig. 4E, F , one can see that, as soon as the finger ...
Context 11
... and one pressure sensor printed on a glove can be observed in the form of a graph. The results of this output visualization show that fingertip touches are easily recognized as "valleys" in the blue signal (Fig. 4G, H, I). On the other hand, finger bending leads to a rise in the red signal that is proportional to the bending degree of the finger (Fig. 4E), while relaxing the finger decreases the output value (Fig. 4F). It can also be observed that the recovery of the sensor is very fast: looking at Fig. 4E, F , one can see that, as soon as the finger in fully straightened (final portion of the red signal in Fig 7F), the signal rapidly stabilizes in the same value as before bending the ...
Context 12
... form of a graph. The results of this output visualization show that fingertip touches are easily recognized as "valleys" in the blue signal (Fig. 4G, H, I). On the other hand, finger bending leads to a rise in the red signal that is proportional to the bending degree of the finger (Fig. 4E), while relaxing the finger decreases the output value (Fig. 4F). It can also be observed that the recovery of the sensor is very fast: looking at Fig. 4E, F , one can see that, as soon as the finger in fully straightened (final portion of the red signal in Fig 7F), the signal rapidly stabilizes in the same value as before bending the finger. Regarding the touch sensor, one can also observe in the ...
Context 13
... easily recognized as "valleys" in the blue signal (Fig. 4G, H, I). On the other hand, finger bending leads to a rise in the red signal that is proportional to the bending degree of the finger (Fig. 4E), while relaxing the finger decreases the output value (Fig. 4F). It can also be observed that the recovery of the sensor is very fast: looking at Fig. 4E, F , one can see that, as soon as the finger in fully straightened (final portion of the red signal in Fig 7F), the signal rapidly stabilizes in the same value as before bending the finger. Regarding the touch sensor, one can also observe in the blue signal from Fig. 4G, H, I that when the fingertip stops applying pressure, the signal ...
Context 14
... can also be observed that the recovery of the sensor is very fast: looking at Fig. 4E, F , one can see that, as soon as the finger in fully straightened (final portion of the red signal in Fig 7F), the signal rapidly stabilizes in the same value as before bending the finger. Regarding the touch sensor, one can also observe in the blue signal from Fig. 4G, H, I that when the fingertip stops applying pressure, the signal returns almost immediately to the resting plateau (to be noticed the close-to-vertical slope of the signal between the full pressure and the resting points). The resulting output of the sensors shows both good precision and repeatability over the tested cycles, with typical ...
Citations
... Despite these advances, there are still many challenges to overcome in order to create robotic hands that can match the capabilities of the human hand. One of the main challenges is the integration of tactile and proprioceptive sensors that can provide real-time feedback on contact forces and hand position [18]. This sensory feedback is crucial for precise control of grip force and manipulation of delicate objects [19]. ...
This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist–antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems.
... For optimal functionality and wear comfort, smart gloves must maintain a target fit range without compromising hand dexterity [12]. Prior research has shown that smart glove malfunction can occur even with small changes in glove fit, produced by different wearers and wearer movement [5], [13]. ...
... In this work a specifically designed coplanar dipole antenna has been employed as a reader with the goal of placing it on the robot hand in order to determine the location of the hand on either the object or the work plane. The coplanar dipole antenna and an example of the adopted resonator can be observed in Fig. 2. All the simulations have been conducted utilizing CST Microwave Studio, whereby the antenna and the resonator is modeled with a resistive impedance of 0.4 Ohm/sq which is considered from characterization of the special conductive ink [13], [14] that is intended for use in the fabrication process. The antenna and resonators were placed onto a substrate measuring 65mm x 55mm, which was made of EVA foam. ...
This study presents a system for detecting the location of a robotic hand on a planar surface. The system uses a radiofrequency (RF) probe attached to the robotic hand and printed resonators on the object surface to detect the hand's orientation. The system employs an ad-hoc designed coplanar antenna with a ground plane for the probe and bent dipole resonators of various sizes on the surface. The sensing is performed within a close range along one direction. The system is numerically designed and then fabricated using a 3D printer and patented conductive ink. Measurements are carried out to assess the simulated results. The proposed probe-resonator configuration allows for the identification of the hand's location relative to the resonators on the work surface with a promising level of accuracy.
... Another group of approaches for hand detection and gesture recognition is based on using gloves [10,[20][21][22] and wristbands [23,24] equipped with sensors, tracking devices, or motion-capturing markers such as magnetic or infrared markers. Gloves equipped with sensors or tracking devices can capture data, such as hand position, orientation, and movement, which can be used to identify and recognize gestures as shown in a comprehensive overview that analyses commercial smart gloves [25]. ...
... A similar application was explored by DelPreto et al. [30], which utilized electromyography-based sensing system for gesture recognition. Carneiro et al. [21] used gestures to teleoperate a walking robot, while Roda-Sanchez et al. [23] navigated the robot to pre-programmed positions using position sensing and wrist rotation. Liu et al. [11] presented a system for modifying a robot's path using gestures: the application allowed editing the position of points of an already defined path. ...
The article explores the possibilities of using hand gestures as a control interface for robotic systems in a collaborative workspace. The development of hand gesture control interfaces has become increasingly important in everyday life as well as professional contexts such as manufacturing processes. We present a system designed to facilitate collaboration between humans and robots in manufacturing processes that require frequent revisions of the robot path and that allows direct definition of the waypoints, which differentiates our system from the existing ones. We introduce a novel and intuitive approach to human–robot cooperation through the use of simple gestures. As part of a robotic workspace, a proposed interface was developed and implemented utilising three RGB-D sensors for monitoring the operator’s hand movements within the workspace. The system employs distributed data processing through multiple Jetson Nano units, with each unit processing data from a single camera. MediaPipe solution is utilised to localise the hand landmarks in the RGB image, enabling gesture recognition. We compare the conventional methods of defining robot trajectories with their developed gesture-based system through an experiment with 20 volunteers. The experiment involved verification of the system under realistic conditions in a real workspace closely resembling the intended industrial application. Data collected during the experiment included both objective and subjective parameters. The results indicate that the gesture-based interface enables users to define a given path objectively faster than conventional methods. We critically analyse the features and limitations of the developed system and suggest directions for future research. Overall, the experimental results indicate the usefulness of the developed system as it can speed up the definition of the robot’s path.
... All the simulations were carried out by using CST Microwave Studio, where the antenna is considered realized with Perfect Electric Conductor (PEC) whereas the resonator is modelled with a resistive impedance (0.3 Ohm/sq). An ongoing activity is dedicated to the RF characterization of the special conductive ink [13], [14] that will be used for the fabrication. As it was mentioned before, in [12] a simple half wavelength dipole antenna was mounted on a robot hand and the coupling with a dipole resonator on the object was used for detecting the angular orientation and distance of the robot from the tagged item, which is quite important in robot grasping applications. ...
A preliminary study aimed at detecting the placement of a robotic hand working on a surface for layout and assembly purposes is presented. The goal is pursued by using a radiofrequency (RF) probe on the robotic hand and printed resonators on the surface. To detect the location of the hand orientation on the surface, the real part of the probe input impedance is examined for different orientations for each resonator placed on the surface. A special design of a coplanar dipole antenna with ground plane as a probe on the hand and a bended metal strip (bended dipole resonator) with different sizes placed on the sheet is employed. The sensing is performed within a near-range distance from the surface in one direction. It is observed that the proposed probe-resonator configuration allows the identification of the relative location of the hand with respect to the one of the resonators on the work surface with an encouraging level of accuracy.
... The article [45] presents a custom-made smart glove that integrates resistive strain and pressure sensors. Homemade silver-based tensile ink coatings are screen-printed onto fabric to create a strain sensor that evaluates finger flexion for all fingers. ...
... In the literature, some studies can be found separately on seam sealing techniques [8][9][10][11][12] and smart garments [13][14][15][16][17][18][19][20][21]. In the studies which subject the seam sealing techniques, standard test methods are used in order to reveal the strength or waterproofness of two-dimensional seam sealed fabrics to evaluate their usage in straight seams (i.e. ...
The importance of smart garments is increasing day by day for entertainment, sports, medical and military fields. Both hard electronics and textile based soft electronics find application in these areas. In our previous study, we focused on crew loss detection system that was applicable for team sports such as sailing. We designed a two pieces electronic system in which a master system was fixed on the boat and a slave system was implemented in the sailing garments. This study brought a new requirement; the need of a waterproof case that would protect the electronic slave system from sea water in case the sailors fall overboard. As a result of this need, in this study, we have designed and tested waterproof pockets that could serve as waterproof carriers for the slave system of sailing garments. For this purpose, studies were carried out using two different approaches. In the first approach, 15 types of pockets were developed by using three different fabric materials and 5 different seam sealing techniques. These pocket prototypes were tested by exposing them to the water for 12 h. In the second approach, an electronic circuit was developed by using a humidity sensor to test the pocket samples those were immersed to water. According to the results of the first approach, samples containing polyester base fabric, polyurethane and polyester membrane and double-sided sealing tape gave the best results. By converting the best pocket design into a garment sleeve, the humidity sensor containing test apparatus was tested and measurements were taken successfully from the system.
This article presents a low-cost, and scalable method for synthesizing large sheets of functionalized foam for soft resistive pressure sensing that is compatible with any commercially available non-conductive foam. The process is illustrated by soaking Pu (Polyurethane) foam in a carbon-based polymeric mixture resulting in a functional foam with large pressure detection range up to 500 kPa, while retaining its deformability and recoverability after cyclic compression. The foam also shows unprecedentedly fast response time of ~120 ms. The foam was integrated in a wearable respiration sensor and in a large multitouch sensing mat that allows for precise data acquisition on the position and intensity of various simultaneous touches without the occurrence of ghost touches.
When studying hand kinematics, it is key to differentiate between free motion and manipulation. This differentiation can be achieved using pressure sensors or through visual analysis in the absence of sensors. Certain data gloves, such as the CyberGlove II, allow recording hand kinematics with good accuracy when properly calibrated. Other gloves, such as the Virtual Motion Glove 30 (VMG30), are also equipped with pressure sensors to detect object contact. The aim of this study is to perform a technical validation to evaluate the feasibility of using virtual reality gloves with pressure sensors such as the VMG30 for hand kinematics characterization during product manipulation, testing its accuracy for motion recording when compared with CyberGlove as well as its ability to differentiate between free motion and manipulation using its pressure sensors in comparison to visual analysis. Firstly, both data gloves were calibrated using a specific protocol developed by the research group. Then, the active ranges of motion of 16 hand joints angles were recorded in three participants using both gloves and compared using repeated measures ANOVAs. The detection capability of pressure sensors was compared to visual analysis in two participants while performing six tasks involving product manipulation. The results revealed that kinematic data recordings from the VMG30 were less accurate than those from the CyberGlove. Furthermore, the pressure sensors did not provide additional precision with respect to the visual analysis technique. In fact, several pressure sensors were rarely activated, and the distribution of pressure sensors within the glove was questioned. Current available gloves such as the VMG30 would require design improvements to fit the requirements for kinematics characterization during product manipulation. The pressure sensors should have higher sensitivity, the pressure sensor’s location should comprise the palm, glove fit should be improved, and its overall stiffness should be reduced.
The use of conductive materials to be printed/embedded onto/within a polymeric matrix has gained increasing attention in the fabrication of next-generation soft electronics. In this review, we provide a comprehensive overview of the materials and approaches for the fabrication of 2D and 3D conductive structures while focusing on the importance of the compatibility between the particles’ shape, the polymeric matrix type, and the deposition method, along with the target application. The review presents a summary of the main conductive materials and the type of polymeric materials which were utilized for fabricating soft electronic devices that are bendable/twistable and stretchable. It is divided into two sections: Introduction section, which presents briefly conductive materials, polymeric matrix, and fabrication methods, and Fabrication section, presenting the main 6 approaches of fabrication. Each of the fabrication subsections presents the main recent reports in a detailed table. The review is concluded with an Outlook section, describing the current challenges in this field. Despite the progress in the fabrication of 2D bendable/twistable electronic devices toward industrial integration, there is still a need for tailoring and improving the durability and robustness of 3D stretchable electronic devices.
