Touch Force Research Articles

Viscoelastic modeling based force control framework for enhancing operation safety of soft tissue for orthopedic surgical robots

In robot-assisted laminectomy, precise control of touch force on the spinal cord during the dissection of ossified ligaments is critical to prevent dura mater tearing and ensure surgical safety. However, the complex viscoelastic characteristics of tissue at the lesion site pose a significant challenge for accurately applying touch force. This paper proposes a safe touch force servo framework based on tissue model identification and preoperative optimization of the force controller. First, a soft tissue model is established using elastic and viscoelastic elements, and the transfer function from tissue deformation to touch force is derived. Subsequently, the path and parameters of a safe pre-touch are designed, and force information required for tissue model identification is obtained through the pre-touch experiment. The model order and specific parameter values are then identified using a differential evolution algorithm. Next, force control simulation is conducted based on the identified model, and a specifically designed loss function is introduced to achieve preoperative tuning of the force controller. Finally, step force control experiments on sheep spines validate the effectiveness of the proposed method. This paper provides a safe and available touch motion control framework that can be expandable to impose precise force on various vulnerable soft tissues, with the potential for widespread application.

Neuron-astrocyte interaction-inspired percolative networks with metal microdendrites and nanostars for ultrasensitive and transparent electronic skins

Biological systems provide innovative designs for electronic devices, optimizing network configurations for high-performance signal transmission with minimal energy consumption. The brain, as one of the most complex biological structures, demonstrates efficient network design through the multiscale radial networks of neurons and astrocytes. Emulating these brain networks offers a blueprint for the development of ultrasensitive pressure sensors for electronic skin, aiming to provide a more intuitive and sensitive mode of interaction between humans and machines. Herein, we propose a neuromorphic percolative network inspired by neuron-astrocyte interactions for ultrasensitive pressure sensors employing metal microdendrites and nanostars. Electromechanical investigation through representative volume elements simulation reveals that the optimized arrangement of microdendrites and nanostars in the neuromorphic percolative system enhances the percolation threshold and probability. Following these simulation results, we developed a neuromorphic percolative polyurethane (NP-PU) matrix utilizing the metal microdendrite-nanostar networks. The augmented quantum tunneling effect in the NP-PU matrix was investigated through electrochemical impedance spectroscopy and capacitance analysis. The fabricated piezoresistive pressure sensor with the NP-PU matrix shows ultrahigh sensitivity (160.3 kPa−1) at a low pressure range and a low limit of detection resolution (4 Pa), enabled by multi-channel quantum tunneling in the metal particle networks. Furthermore, the sensor maintains excellent mechanical flexibility and high optical transparency (75.4 %), improving its efficacy in applications like electronic skin and force touch panel. Our study highlights the potential of leveraging biological system-inspired network designs for crafting advanced electronic devices.

Spike-Based Neuromorphic Hardware for Dynamic Tactile Perception with a Self-Powered Mechanoreceptor Array.

A self-powered mechanoreceptor array is demonstrated using four mechanoreceptor cells for recognition of dynamic touch gestures. Each cell consists of a triboelectric nanogenerator (TENG) for touch sensing and a bi-stable resistor (biristor) for spike encoding. It produces informative spike signals by sensing a force of an external touch and encoding the force into the number of spikes. An array of the mechanoreceptor cells is utilized to monitor various touch gestures and it successfully generated spike signals corresponding to all the gestures. To validate the practicality of the mechanoreceptor array, a spiking neural network (SNN), highly attractive for power consumption compared to the conventional von Neumann architecture, is used for the identification of touch gestures. The measured spiking signals are reflected as inputs for the SNN simulations. Consequently, touch gestures are classified with a high accuracy rate of 92.5%. The proposed mechanoreceptor array emerges as a promising candidate for a building block of tactile in-sensor computing in the era of the Internet of Things (IoT), due to the low cost and high manufacturability of the TENG. This eliminates the need for a power supply, coupled with the intrinsic high throughput of the Si-based biristor employing complementary metal-oxide-semiconductor (CMOS) technology.

Generation of keyboard handwriting during user authentication on mobile devices

Objectives. This article discusses a new way of generating keyboard handwriting using a touch keyboard for authentication in currently existing mobile systems.Methods. Due to the insufficient reliability of single password authentication, the proposal is to use it in combination with characteristics which correspond to handwriting on mobile devices. This article demonstrates the possibility of using individual user characteristics in the formulation of keyboard handwriting on devices with touch keyboards. The type of keyboard used affects the characteristics of keyboard handwriting, so this aspect can be used to improve password authentication reliability. The authentication process in the information environment can be supplemented with data on the nature of the impact on a touch keyboard. The use of the built-in 3D Touch function is also of interest. This is available when working on mobile devices and appliances equipped with a touch keyboard. The paper demonstrates that the use of one parameter only is insufficient for accurate authentication. The study proposes a method of determining an acceptable error range for both the touch force and the intermediate interval during authentication. For this purpose, the Laplace function which formulates the interval of each characteristic depending on the required probability of user recognition is used.Results. Touch force and the intermediate interval are sufficient to obtain the necessary characteristics, in order to formulate a refined user portrait depending on the user’s keyboard handwriting. Experimental statistics are given separately for an average sample of three different users depending on touch force. They also provide the results of authentication when using both standard deviations of pressing and the intervals when using the touch keyboard for the iOSXcode platform.Conclusions. The conclusion relates to the possibility of user authentication by keyboard handwriting, formulated on the basis of both the touch force on the keyboard symbols and intervals between pressing. Using the values of the sample mean and standard deviations allows authentication according to the required recognition probability.

Sharp Force Touch for On-Screen User Interface in LCD and Foldable OLED Display Application

Sharp Force Touch for On-Screen User Interface in LCD and Foldable OLED Display Application

Piezoelectric and Machine Learning Based Keystroke Dynamics for Highly Secure User Authentication

Cyber security is of significance in today’s e-commerce applications. In this article, we present a piezoelectric touch sensing supported keystroke dynamics based identity authentication technique, for providing a secure access manner to smartphones. Here, the polyvinylidene fluoride (PVDF) based piezoelectric touch panel can learn detailed force touch habits of users. With a support vector machine (SVM) algorithm, our proposed frequency domain features experimentally demonstrate a better authentication accuracy of 98.3%, compared to the traditional time domain features. The work showcases a feasible method of combining functional materials and artificial intelligence (AI) for satisfying highly secure requirements.

Finite element modeling of the electrical impedance tomography technique driven by machine learning

To create a human-like skin for a robotic application, current touch sensor technologies have a few drawbacks. Electrical Impedance Tomography (EIT) is a candidate for this application due to its applicability over complex geometries; nevertheless, it has accuracy concerns. This study employs artificial neural networks (ANNs) to investigate the accuracy and capability of EIT-based touch sensors. A finite element (FE) model is utilized to solve the forward EIT problem while simultaneously determining the system’s comprehensive mechanical response. The FE model is comprised of a polyurethane (PU) foam domain, a conductive spray layer and a set of sixteen electrodes. To replicate the process of touching the sensor body, a punch of varying diameters and touch forces is utilized. The mechanical response of the sensor body is modeled using the hyperfoam material model calibrated through experimental uniaxial and shear test data, while the electric conductivity of the sprayed skin surface is obtained experimentally as function of applied strain. The viscoelastic behavior of the PU foam material is also obtained experimentally. These experimental data were implemented in the FE model through user subroutines to model the mechanical and electrical properties of the sensor in the EIT forward problem. The traditional EIT inverse problem image reconstruction was replaced utilizing ANNs as an alternative to extract mechanics based parameters. The ANNs were created to predict the spatial coordinates of the touch point, and they were proven to be extremely accurate. Using the EIT voltage readings as input, the ANNs were utilized to forecast the system’s mechanical behavior such as contact pressure, contact area, indentation depth, and touching force.

Older Adults Disproportionately Hindered by Touch Screen Interfaces in Driving Tasks

Touch screens have become increasingly used for many personal technologies. However, older adults have trouble using touch screen interfaces. The general assumption is that older adults struggle with touch screen devices because they are unfamiliar with the technology and that with practice, they will become proficient. This study provides evidential support of a probable physiological barrier contributing to some older adults’ touch screen difficulties. In our study, participants (7 older adults, 10 younger adults) had to press a touch screen button to get information while driving. Older adults disproportionately failed attempts to hit this button. Video analysis showed that even when older adults hit the target button correctly while driving with an appropriate amount of force and duration of touch, the screen sometimes failed to register their command. This suggests that a minimally documented but present age-related physiological reason likely contributes to older adults’ reduced ability to successfully interact with touch screens.

A Tactile Skin System for Touch Sensing with Ultrasound Tomography.

The tomographic imaging method is promising in large-area touch-sensing applications. This paper presents a new type of such touch sensor using ultrasonic tomography (UST) via sound attenuation imaging. UST is gaining popularity as a portable, fast, and inexpensive imaging system for medical and industrial applications. UST can be developed in different operation modes. A transmission mode UST is being investigated as a force- and touch-sensitive skin. A prototype skin sensor was developed in a 200 mm diameter circular UST array containing two sets of 16 transducers, with one operating at a central frequency of 40 kHz and the other at 300 kHz. The extension of the sensor in terms of dimension, up to 400 mm diameter, and number of sensors, up to 32 transducers, is possible where eight points of contact were reconstructed successfully. The medium contains a 20 mm high water region, and a soft silicone membrane covers the liquid region. When touchpoints or forces are applied to the soft skin of the membrane, the sound pathway is disrupted, resulting in an image of the touch position and touch force intensity using a tomographic UST algorithm. Several static and dynamic experiments are conducted to demonstrate this novel application of UST. In addition, a correlation analysis is carried out to establish the force quantification potential for the UST-based tactile skin.

Characteristics of a Kapton triboelectric nanogenerator-based touch button’s voltage output

Since the Triboelectric Nanogenerator (TENG) was invented in 2012, Its potential in the area of energy generation and self-powered sensors has been very obvious, one of the uses of TENGs is as self-powered touch buttons, which are usually single-electrode TENGs, single-electrode TENGs are made of a layer of a material (which is to be triboelectrified) attached to an electrode which is connected to an electrical ground or a reference electrode, in this study, a cheap triboelectric button was made with Kapton and tested in contact and separation with aluminum and a human finger while varying parameters such as the grounding state of the person touching the button and the touch force. It was found that the tested button gave easily detectable signals if touched with aluminum foil or a human finger, however, the signal to noise ratio was low in case of being pressed by a human finger with the person touching the button also being grounded, this is due to an increased capacitance between the person pressing the button and the environment, it was also found that the output voltage of the TENG increased after pressing it lots of times consecutively due to an increased amount of total charge on the Kapton layer when pressed at slightly different spots, these output characteristics could help in better integration of such buttons in human-machine interaction applications.

31‐4: Sharp Force Touch for On‐Screen User Interface in LCD and Foldable OLED Display Application

We describe ultra‐high sensitive force sensor on flat display that can detect and differentiate between feather touch and press touch or tapping. The proposed unique sensor pattern design and pressure‐sensitive material enables ultra‐high force sensitivity, multi force detection (10 points). Our force touch technology with button‐shaped bumps cover enables blind input operations and usability as good as mechanical buttons. Therefore, our technology provides reduced input errors and yield more intuitive and safedrive. In addition, the proposed novel algorithm enables an operating temperature range for automotive applications.

Hold me or stroke me? Individual differences in static and dynamic affective touch.

Low-threshold mechanosensory C-fibres, C-tactile afferents (CTs), respond optimally to sensations associated with a human caress. Additionally, CT-stimulation activates brain regions associated with processing affective states. This evidence has led to the social touch hypothesis, that CTs have a key role in encoding the affective properties of social touch. Thus, to date, the affective touch literature has focussed on gentle stroking touch. However, social touch interactions involve many touch types, including static, higher force touch such as hugging and holding. This study aimed to broaden our understanding of the social touch hypothesis by investigating relative preference for static vs dynamic touch and the influence of force on these preferences. Additionally, as recent literature has highlighted individual differences in CT-touch sensitivity, this study investigated the influence of affective touch experiences and attitudes, autistic traits, depressive symptomology and perceived stress on CT-touch sensitivity. Directly experienced, robotic touch responses were obtained through a lab-based study and vicarious touch responses through an online study where participants rated affective touch videos. Individual differences were determined by self-report questionnaire measures. In general, static touch was preferred over CT-non-optimal stroking touch, however, consistent with previous reports, CT-optimal stroking (velocity 1-10 cm/s) was rated most pleasant. However, static and CT-optimal vicarious touch were rated comparably for dorsal hand touch. For all velocities, 0.4N was preferred over 0.05N and 1.5N robotic touch. Participant dynamic touch quadratic terms were calculated for robotic and vicarious touch as a proxy CT-sensitivity measure. Attitudes to intimate touch significantly predict robotic and vicarious quadratic terms, as well as vicarious static dorsal hand touch ratings. Perceived stress negatively predicted robotic static touch ratings. This study has identified individual difference predictors of CT-touch sensitivity. Additionally, it has highlighted the context dependence of affective touch responses and the need to consider static, as well as dynamic affective touch.

Adaptive Mode-switching From Autonomous Driving Mode to Manual Operation Mode of Mobile Robot Based on Body Sway

Transportation flexibility in manufacturing factories can be improved by introducing a function that allows the operator to physically interact with autonomous mobile robots (AMRs.) In our previous study, a manual operation mode was implemented with a sensorless power-assist scheme according to impedance control based on the touching force of the operator on a stationary AMR. In this paper, we propose a mode-switching algorithm for an AMR that switches from autonomous navigation to manual operation. Control-mode switching is achieved by detecting the body sway caused by the touching force of the operator. The experimental results demonstrate that the control mode can be successfully switched adaptively based on the intention of the operator, without any additional sensors.

Crack-inducing Strain Sensor Array using Inkjet-Printed Silver Thin Film for Underplate and Off-centered Force Sensing Applications.

The change in resistance upon bending in metal films as thick as 1 mm used for underpanel force touch applications is limited by the low sensitivity, thus requiring high-performance readout circuitry. In this paper, we report inkjet-printed silver thin films having crack-inducing underlayers, which further increases the sensitivity of their resistance changes under deformation. This allows for detecting weak vertical forces even through the plates (force-receiving layer), such as 0.4 or 1.2 mm thick polyethylene terephthalate or 0.4 mm thick glass. The underplate sensors will detect a force level as low as 10 gf, which corresponds to the amount of force required for fingerprint recognition. Furthermore, such highly sensitive strain sensors can potentially solve the inaccuracy issue of wearable devices, which can occur when misplaced sensors detect relatively weak biosignals, such as heart rate and blood pressure. The sensor detects the accurate pulse patterns of the wrist artery even though it is off-centered from the artery by 6 mm or larger. The crack-based strain sensor and its usage as a hidden underplate force sensing device will create various wearable and user-machine interface applications.

Robot-Assisted Retraction for Transoral Surgery

Tissue retraction is an important task in head and neck surgery to leave space for surgical operations. Because the contact between the retractor and soft tissue is not trivial to model, the retraction operation has not been well addressed by robots in modern robot-assisted surgery. We propose a human-robot collaboration approach to assist the retraction for transoral surgery. The surgeons only need to roughly place the retractors into the oral cavity and specify the recommended retraction force. Robot manipulators will automatically retract the tissues in a safe way. In order to keep the touching force safe, we employ a force-sensing system at the distal end of the retractor. By analyzing the real-time force sensor data, we propose a control strategy which combines active retraction angle compensation and passive torque compensation to adjust the retractor, further reducing potential slippage during the retraction. The proposed method ensures the retraction is adaptive to unknown perturbations of the human anatomy. Our system requires no extra recognition or calibration of the surgical scene or the human tissue models. The approach is validated with two robot manipulators and force-sensing retractors on a physical head phantom as well as a cadaveric experiment. We show that the robots stably retract the mouth under various configurations, where the relative error of the stable retraction force does not exceed 11.8%.

Invitation

Invitation

Haptically Quantifying Young's Modulus of Soft Materials Using a Self‐Locked Stretchable Strain Sensor (Adv. Mater. 25/2022)

Strain Sensors The Young's modulus of soft tissues can be haptically measured using a single stretchable strain sensor, as reported by Guoying Deng, Chuanlai Xu, Xiaodong Chen, and co-workers in article number 2104078. The technology will enable a doctor to quantify the softness of swollen tissues simply by touching, independent of the touching force. The new methodology for Young's modulus measurement is simple, rapid, and widely adaptable to various scenarios.

A force-sensing retractor for robot-assisted transoral surgery.

In robot-assisted transoral surgery, frequent retraction operations are essential to leave space for the surgical procedure. Commercial clinical retractors are simply composed of mechanical parts and cannot sense the touching force. We propose a new retractor for robot-assisted transoral surgery. It supports sensing of the touching force when retracting the tissues. By designing the structure of the force sensors based on small piezoresistive elements, we build a sensory system that is well integrated with the retractor for transoral surgery. After calibration of the system, a simple equation is computed to decode the resultant force as well as the center of the contact location. A standard measuring test is designed for the force-sensing retractor. The result shows that the measured force is up to 15 N, and the sensed force precision reaches 0.08 N with a sampling rate of 98 Hz. The dimensions of the sensory system fit the retractor well. The experimental results demonstrate the potential of the proposed retractor in robot-assisted surgery. The retractor supports the provision of force feedback in an interactive manipulation mode and produces haptic information for the remote side in a teleoperated surgical robot system.

Persistent Memory Allocation

A lever multiplies the force of a light touch, and the right software interfaces provide formidable leverage in multiple layers of code: A familiar interface enables a new persistent memory allocator to breathe new life into an enormous installed base of software and hardware. Compatibility allows a persistent heap to slide easily beneath a widely used scripting-language interpreter, thereby endowing all scripts with effortless on-demand persistence.

Endowing a NAO Robot With Practical Social-Touch Perception.

Social touch is essential to everyday interactions, but current socially assistive robots have limited touch-perception capabilities. Rather than build entirely new robotic systems, we propose to augment existing rigid-bodied robots with an external touch-perception system. This practical approach can enable researchers and caregivers to continue to use robotic technology they have already purchased and learned about, but with a myriad of new social-touch interactions possible. This paper presents a low-cost, easy-to-build, soft tactile-perception system that we created for the NAO robot, as well as participants’ feedback on touching this system. We installed four of our fabric-and-foam-based resistive sensors on the curved surfaces of a NAO’s left arm, including its hand, lower arm, upper arm, and shoulder. Fifteen adults then performed five types of affective touch-communication gestures (hitting, poking, squeezing, stroking, and tickling) at two force intensities (gentle and energetic) on the four sensor locations; we share this dataset of four time-varying resistances, our sensor patterns, and a characterization of the sensors’ physical performance. After training, a gesture-classification algorithm based on a random forest identified the correct combined touch gesture and force intensity on windows of held-out test data with an average accuracy of 74.1%, which is more than eight times better than chance. Participants rated the sensor-equipped arm as pleasant to touch and liked the robot’s presence significantly more after touch interactions. Our promising results show that this type of tactile-perception system can detect necessary social-touch communication cues from users, can be tailored to a variety of robot body parts, and can provide HRI researchers with the tools needed to implement social touch in their own systems.