Touch Signals Research Articles

Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.

Endophilin A2 controls touch and mechanical allodynia via kinesin-mediated Piezo2 trafficking

BackgroundTactile and mechanical pain are crucial to our interaction with the environment, yet the underpinning molecular mechanism is still elusive. Endophilin A2 (EndoA2) is an evolutionarily conserved protein that is documented in the endocytosis pathway. However, the role of EndoA2 in the regulation of mechanical sensitivity and its underlying mechanisms are currently unclear.MethodsMale and female C57BL/6 mice (8–12 weeks) and male cynomolgus monkeys (7–10 years old) were used in our experiments. Nerve injury-, inflammatory-, and chemotherapy-induced pathological pain models were established for this study. Behavioral tests of touch, mechanical pain, heat pain, and cold pain were performed in mice and nonhuman primates. Western blotting, immunostaining, co-immunoprecipitation, proximity ligation and patch-clamp recordings were performed to gain insight into the mechanisms.ResultsThe results showed that EndoA2 was primarily distributed in neurofilament-200-positive (NF200+) medium-to-large diameter dorsal root ganglion (DRG) neurons of mice and humans. Loss of EndoA2 in mouse NF200+ DRG neurons selectively impaired the tactile and mechanical allodynia. Furthermore, EndoA2 interacted with the mechanically sensitive ion channel Piezo2 and promoted the membrane trafficking of Piezo2 in DRG neurons. Moreover, as an adaptor protein, EndoA2 also bound to kinesin family member 5B (KIF5B), which was involved in the EndoA2-mediated membrane trafficking process of Piezo2. Loss of EndoA2 in mouse DRG neurons damaged Piezo2-mediated rapidly adapting mechanically activated currents, and re-expression of EndoA2 rescued the MA currents. In addition, interference with EndoA2 also suppressed touch sensitivity and mechanical hypersensitivity in nonhuman primates.ConclusionsOur data reveal that the KIF5B/EndoA2/Piezo2 complex is essential for Piezo2 trafficking and for sustaining transmission of touch and mechanical hypersensitivity signals. EndoA2 regulates touch and mechanical allodynia via kinesin-mediated Piezo2 trafficking in sensory neurons. Our findings identify a potential new target for the treatment of mechanical pain.

Touchscreens Can Reveal User Identity: Capacitive Plethysmogram-Based Biometrics

Biometrics are widely used for user identification/authentication, but the fact has rarely been noticed that general capacitive touchscreens can reveal user identities by touch signals. This paper proposes a new biometric method with inherent liveness detection for reliable user recognition based on the cardiac signal captured by the capacitive touchscreen, namely Capacitive Plethysmogram (CPG). And a systematic framework is designed for CPG collection, processing, and exploitation to identify users. Specifically, since the finger usually forms capacitors with multiple sensing electrodes during touching, we can extract several CPG signals simultaneously from the screen output. Then we propose a series of preprocessing algorithms to filter CPG for signal quality enhancement. Finally, to further leverage filtered CPG signals and extract efficient features for identifying users, we build an encoder based on 3D attention CNN and metric learning. Experimental results demonstrate that the proposed method can achieve an average accuracy of 96.73%, FAR of 3.03%, and FRR of 7.35% in the laboratory environment, which reveals the potential of CPG for user privacy protection and data security on various devices laced with capacitive touchscreens.

Photocurable Carbon Nanotube/Polymer Nanocomposite for the 3D Printing of Flexible Capacitive Pressure Sensors.

A photocurable resin/carbon nanotube (CNT) nanocomposite was fabricated from aligned CNTs in an acrylic matrix. The conductivity of the nanocomposite increased rapidly and then stabilized when the CNT content was increased up to and beyond the percolation threshold. Various structures were created using a digital light processing (DLP) 3D printer. Various polymeric dispersants (SMA-amide) were designed and synthesized to improve the CNT dispersion and prevent aggregation. The benzene rings and lone electron pairs on the dispersant interacted with aromatic groups on the CNTs, causing the former to wrap around the latter. This created steric hindrance, thereby stabilizing and dispersing the CNTs in the solvent. CNT/polymer nanocomposites were created by combining the dispersant, CNTs, and a photocurable resin. The CNT content of the nanocomposite and the 3D printing parameters were tuned to optimize the conductivity and printing quality. A touch-based human interface device (HID) that utilizes the intrinsic conductivity of the nanocomposite and reliably detects touch signals was fabricated, enabling the free design of sensors of various styles and shapes using a low-cost 3D printer. The production of sensors without complex circuitry was achieved, enabling novel innovations.

Superior compressive and tensile bi-directional strain sensing capabilities achieved using liquid metal Hybrid-Hydrogels empowered by Machine learning algorithms

Hydrogel-based flexible strain sensors have great potentials for applications in wearable electronics, human–machine interfaces, and soft robotics. Although great efforts have been made on enhancing tensile strain sensing capability of hydrogels, their sensing capabilities under compressive deformation have seldom been explored. Herein, we report a liquid metal-based hybrid hydrogel which achieves high stretchability (2300 %), superior bi-directional responses to both compressive strains (with a gauge factor of 33.43) and tensile strains (with a gauge factor of 9.59), fast response time (190 ms), and excellent durability (>1500 cycles). This conductive hybrid hydrogel with multi-interpenetrating networks is constructed by incorporating liquid metal, graphene oxide, poly-dopamine, and potassium chloride into a polymer double-network of polyacrylamide and poly(3,4-ethylenedioxythiophene): poly (styrene sulfonate). By integrating machine-learning algorithm with the hybrid hydrogel sensors, an intelligent dual-mode handwriting recognition system is developed for perceiving finger touch signals (in compressive-strain mode) and finger bending signals (in tensile-strain mode), with high accuracy (>93 %) and fast recognition time (<1s) when recognizing numbers, letters, and signature signals. Furthermore, this handwriting recognition system demonstrates advanced performance for human–machine interactions (e.g., playing music) and virtual reality interactions. This study offers a promising strategy to develop hydrogel electronics with bidirectional sensing capabilities for diversified practical applications.

Touch-text answer for human-robot interaction via supervised adversarial learning

In daily life, touch modality plays an important role in conveying human intentions and emotions. To further improve touch-based human-robot interaction, robots need to infer human emotions from touch signals and respond accordingly. Therefore, it is a major challenge to correlate the emotional state of touch gestures with text responses. At present, there are few researches on touch-text dialogue, and robots cannot respond to human tactile gestures with appropriate text, so touch-text-based human-robot interaction is not yet possible. To solve these problems, we first built a touch-text dialogue (TTD) corpus based on six basic emotions through experiments, which contains 1109 touch-text sample pairs. And then, we designed a supervised adversarial learning for touch-text answer (SATTA) model to realize the touch-text based human-robot interaction. The SATTA model correlates the data of text mode with that of touch mode by reducing the emotion discrimination loss in the public space and the feature difference between the sample pairs of two modes. At the same time, the feature representation is mapped into the label space to reduce the classification loss of samples. The experiment in the TTD corpus validates the proposed method.

Ethylene and jasmonate signaling converge on gibberellin catabolism during thigmomorphogenesis in Arabidopsis.

Touch induces marked morphological changes in plants, including reduced rosette diameters and delayed flowering, a process called thigmomorphogenesis. Previous studies have revealed that thigmomorphogenesis in Arabidopsis (Arabidopsis thaliana) results from touch-induced accumulation of jasmonic acid (JA) and GIBBERELLIN 2-OXIDASE7 (GA2ox7) transcripts, which encode a gibberellin (GA) catabolism enzyme, leading to reduced levels of active GAs. However, the mechanisms underlying thigmomorphogenesis remain uncharacterized. Here, we showed that touch induces ethylene (ET) production in Arabidopsis. After touch treatment, ET biosynthesis and signaling mutants exhibited even greater thigmomorphogenic changes and more decreased GA4 contents than did wild-type (WT) plants. Biochemical analysis indicated that the transcription factor ETHYLENE INSENSITIVE3 (EIN3) of the ET pathway binds to the promoter of GA2ox8 (encoding another GA 2-oxidase performing the same GA modification as GA2ox7) and represses GA2ox8 transcription. Moreover, MYC2, the master regulator of JA signaling, directly promoted GA2ox7 expression by binding the G-box motif on GA2ox7 promoter. Further genetic analysis suggested that the ET and JA pathways independently control the expression of GA2ox8 and GA2ox7, respectively. This study reveals that the ET pathway is a novel repressor of touch-induced thigmomorphogenesis and highlights that the ET and JA pathways converge on GA catabolism but play opposite roles to fine-tune GA4 content during thigmomorphogenesis.

Non-Invasive Intraoral Stand-Alone Tongue Control System Based on RSIC-V Edge Computing

The intelligent tongue control system is of great significance for assisting the independent life of patients with a limb disability. In order to more accurately control the assisted living equipment of incompetent patients and solve the power-loss problem of the intelligent tongue control system, this research designs a non-invasive pressure sensor array for tongue touch signal detection in the oral cavity and proposes a tongue control system based on RSIC-V edge computing. The system converts the tongue touch pressure data into specific control instructions on the edge of the RSIC-V chip and transmits them to the receiver, thus reducing the transmission of data. This study takes control of the wheelchair motor as the test object. In the experiment, the speed response time test, the center click task, and the power consumption experiment are carried out, whose results show that the adaptive fuzzy PID control algorithm has good robustness in the system; when the DC motor with a given speed of 750 r/min reaches the steady state, its rise time is 0.108 s and the adjustment time is 0.59 s. The dynamic power consumption of the non-intrusive intraoral stand-alone tongue control system proposed in this paper is found to be 3.745 MW, which is 11.5% lower than the total power consumption of the sTD system.

Mutational analysis of mechanosensitive ion channels in the carnivorous Venus flytrap plant

How the Venus flytrap (Dionaea muscipula) evolved the remarkable ability to sense, capture, and digest animal prey for nutrients has long puzzled the scientific community.1 Recent genome and transcriptome sequencing studies have provided clues to the genes thought to play a role in these tasks.2,3,4,5 However, proving a causal link between these and any aspect of the plant's hunting behavior has been challenging due to the genetic intractability of this non-model organism. Here, we use CRISPR-Cas9 methods to generate targeted modifications in the Venus flytrap genome. The plant detects prey using touch-sensitive trigger hairs located on its bilobed leaves.6 Upon bending, these hairs convert mechanical touch signals into changes in the membrane potential of sensory cells, leading to rapid closure of the leaf lobes to ensnare the animal.7 Here, we generate mutations in trigger-hair-expressed MscS-like (MSL)-family mechanosensitive ion channel genes FLYCATCHER1 (FLYC1) and FLYCATCHER2 (FLYC2)5 and find that double-mutant plants have a reduced leaf-closing response to mechanical ultrasound stimulation. While we cannot exclude off-target effects of the CRISPR-Cas9 system, our genetic analysis is consistent with these and other functionally redundant mechanosensitive ion channels acting together to generate the sensory system necessary for prey detection.

Artificial Tactile Sensing System Based on Triboelectric Nanogenerator and Synaptic Transistor

Artificial tactile sensing system could realize the perception of different types of tactile information. Nowadays, most tactile sensors mainly convert the external stimulation signals into different material conductivity value, and processed by complex external circuits to detect the human physiological signals. In this work, the artificial synaptic device was connected with triboelectric nanogenerator(TENG) to fabricate a tactile sensing system in a simple way. This system could detect the external pressure stimulations and accurately recognize small frequency touch signals of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1-2.5$ </tex-math></inline-formula> Hz. In addition, the complex biological behavior was also simulated, which enlightened significance for the future bionic tactile systems and the field of human-computer interaction.

In Vivo Calcium Imaging of Neuronal Ensembles in Networks of Primary Sensory Neurons in Intact Dorsal Root Ganglia.

Ca2+ imaging can be used as a proxy for cellular activity, including action potentials and various signaling mechanisms involving Ca2+ entry into the cytoplasm or the release of intracellular Ca2+ stores. Pirt-GCaMP3-based Ca2+ imaging of primary sensory neurons of the dorsal root ganglion (DRG) in mice offers the advantage of simultaneous measurement of a large number of cells. Up to 1,800 neurons can be monitored, allowing neuronal networks and somatosensory processes to be studied as an ensemble in their normal physiological context at a populational level in vivo. The large number of neurons monitored allows the detection of activity patterns that would be challenging to detect using other methods. Stimuli can be applied to the mouse hindpaw, allowing the direct effects of stimuli on the DRG neuron ensemble to be studied. The number of neurons producing Ca2+ transients as well as the amplitude of Ca2+ transients indicates sensitivity to specific sensory modalities. The diameter of neurons provides evidence of activated fiber types (non-noxious mechano vs. noxious pain fibers, Aβ, Aδ, and C fibers). Neurons expressing specific receptors can be genetically labeled with td-Tomato and specific Cre recombinases together with Pirt-GCaMP. Therefore, Pirt-GCaMP3 Ca2+ imaging of DRG provides a powerful tool and model for the analysis of specific sensory modalities and neuron subtypes acting as an ensemble at the populational level to study pain, itch, touch, and other somatosensory signals.

Research on fusion control of sensor information and biological reflection based on CPG

To enhance the environmental adaptability of the quadruped robot and realize its stability in terrain with obstacles, this paper studies the fusion of sensor information feedback and bio-reflection control algorithm. First, a central pattern generator (CPG) that generates basal rhythmic movements is constructed. Secondly, a simplified foot end that can be used to identify the touchdown and obstacle information is designed. To enhance the stability of the quadruped robot during the flexor reflex process and avoid the problem of solidification in the reflex process, the foot touch signal was introduced into the flexor reflex algorithm, which enhanced the flexibility of the reflex process. To avoid secondary collisions with obstacles, the postswing reflection process is optimized, and active obstacle avoidance is realized after the obstacle is touched. Finally, the organic fusion of sensor information feedback, CPG control network, and bio-reflection control algorithm is realized. The simulation results show that the robot’s ability to overcome obstacles is improved, the number of reflections is less, and the reflection process is more stable.

Selective Involvement of a Subset of Spinal Dorsal Horn Neurons Operated by a Prodynorphin Promoter in Aβ Fiber-Mediated Neuropathic Allodynia-Like Behavioral Responses in Rats.

Mechanical allodynia (pain produced by innocuous stimuli such as touch) is the main symptom of neuropathic pain. Its underlying mechanism remains to be elucidated, but peripheral nerve injury (PNI)-induced malfunction of neuronal circuits in the central nervous system, including the spinal dorsal horn (SDH), is thought to be involved in touch-pain conversion. Here, we found that intra-SDH injection of adeno-associated viral vectors including a prodynorphin promoter (AAV-PdynP) captured a subset of neurons that were mainly located in the superficial laminae, including lamina I, and exhibited mostly inhibitory characteristics. Using transgenic rats that enable optogenetic stimulation of touch-sensing Aβ fibers, we found that the light-evoked paw withdrawal behavior and aversive responses after PNI were attenuated by selective ablation of AAV-PdynP-captured SDH neurons. Notably, the ablation had no effect on withdrawal behavior from von Frey filaments. Furthermore, Aβ fiber stimulation did not excite AAV-PdynP+ SDH neurons under normal conditions, but after PNI, this induced excitation, possibly due to enhanced Aβ fiber-evoked excitatory synaptic inputs and elevated resting membrane potentials of these neurons. Moreover, the chemogenetic silencing of AAV-PdynP+ neurons of PNI rats attenuated the Aβ fiber-evoked paw withdrawal behavior and c-FOS expression in superficial SDH neurons. Our findings suggest that PNI renders AAV-PdynP-captured neurons excitable to Aβ fiber stimulation, which selectively contributes to the conversion of Aβ fiber-mediated touch signal to nociceptive. Thus, reducing the excitability of AAV-PdynP-captured neurons may be a new option for the treatment of neuropathic allodynia.

A Low-Computing-Complexity Touch Signal Detection Method and Analog Front-End Circuits Based on Cross-Correlation Technology for Large-Size Touch Panel.

This paper proposes a low-computing-complexity touch signal detection method and analog front-end (AFE) circuits based on cross-correlation technology for large mutual capacitance touch screen panels (TSPs). To solve the traditional touch signal detection method problem of lots of invalid data being sampled and processed in a large-size TSP, the proposed method only samples and processes the signals around the touch points rather than full-screen data to decrease the computing complexity and analog–digital convertor (ADC) acquisition number. Compared with the traditional method, the proposed touch points search algorithm complexity decreases from MN to M + nN where M, N, and n are the number of RX channels, TX channels, and touch points, respectively. The maximum ADC acquisition number of the proposed method decreases from MN to 18n. Based on the proposed touch signal detection method, the AFE circuits are designed by a 0.11 μm process. The proposed dual cross-correlation AFE achieves detection of the weak touch signal submerged in the large display panel noise. The average channel area and power consumption are decreased to 0.015 mm2 and 0.227 mW, respectively. The maximum frame rate is 384.6 Hz with 10 touch points. The proposed cross-correlation AFE achieves a high frame rate while reducing the die area and power consumption.

Touch signaling and thigmomorphogenesis are regulated by complementary CAMTA3- and JA-dependent pathways.

Plants respond to mechanical stimuli to direct their growth and counteract environmental threats. Mechanical stimulation triggers rapid gene expression changes and affects plant appearance (thigmomorphogenesis) and flowering. Previous studies reported the importance of jasmonic acid (JA) in touch signaling. Here, we used reverse genetics to further characterize the molecular mechanisms underlying touch signaling. We show that Piezo mechanosensitive ion channels have no major role in touch-induced gene expression and thigmomorphogenesis. In contrast, the receptor-like kinase Feronia acts as a strong negative regulator of the JA-dependent branch of touch signaling. Last, we show that calmodulin-binding transcriptional activators CAMTA1/2/3 are key regulators of JA-independent touch signaling. CAMTA1/2/3 cooperate to directly bind the promoters and activate gene expression of JA-independent touch marker genes like TCH2 and TCH4. In agreement, camta3 mutants show a near complete loss of thigmomorphogenesis and touch-induced delay of flowering. In conclusion, we have now identified key regulators of two independent touch-signaling pathways.

Clinical Manifestations of Body Memories: The Impact of Past Bodily Experiences on Mental Health.

Bodily experiences such as the feeling of touch, pain or inner signals of the body are deeply emotional and activate brain networks that mediate their perception and higher-order processing. While the ad hoc perception of bodily signals and their influence on behavior is empirically well studied, there is a knowledge gap on how we store and retrieve bodily experiences that we perceived in the past, and how this influences our everyday life. Here, we explore the hypothesis that negative body memories, that is, negative bodily experiences of the past that are stored in memory and influence behavior, contribute to the development of somatic manifestations of mental health problems including somatic symptoms, traumatic re-experiences or dissociative symptoms. By combining knowledge from the areas of cognitive neuroscience and clinical neuroscience with insights from psychotherapy, we identify Clinical Body Memory (CBM) mechanisms that specify how mental health problems could be driven by corporeal experiences stored in memory. The major argument is that the investigation of the neuronal mechanisms that underlie the storage and retrieval of body memories provides us with empirical access to reduce the negative impact of body memories on mental health.

Data-Driven Sparse Skin Stimulation Can Convey Social Touch Information to Humans.

During social interactions, people use auditory, visual, and haptic cues to convey their thoughts, emotions, and intentions. Due to weight, energy, and other hardware constraints, it is difficult to create devices that completely capture the complexity of human touch. Here we explore whether a sparse representation of human touch is sufficient to convey social touch signals. To test this we collected a dataset of social touch interactions using a soft wearable pressure sensor array, developed an algorithm to map recorded data to an array of actuators, then applied our algorithm to create signals that drive an array of normal indentation actuators placed on the arm. Using this wearable, low-resolution, low-force device, we find that users are able to distinguish the intended social meaning, and compare performance to results based on direct human touch. As online communication becomes more prevalent, such systems to convey haptic signals could allow for improved distant socializing and empathetic remote human-human interaction.

A Fully Flexible Intelligent Thermal Touch Panel Based on Intrinsically Plastic Ag2 S Semiconductor.

Wearable touch panels, a typical flexible electronic device, can recognize and feed back the information of finger touch and movement. Excellent wearable touch panels are required to accurately and quickly monitor the signals of finger movement as well as the capacity of bearing various types of deformation. High-performance thermistor materials are one of the key functional components, but to date, a long-standing bottleneck is that inorganic semiconductors are typically brittle while the electrical properties of organic semiconductors are quite low. Herein, a high-performance flexible temperature sensor is reported by using plastic Ag2 S with ultrahigh temperature coefficient of resistance of -4.7% K-1 and resolution of 0.05 K, and rapid response/recovery time of 0.11/0.11 s. Moreover, the temperature sensor shows excellent durability without performance damage or loss during force stimuli tests. In addition, a fully flexible intelligent touch panel composed of a 16 × 10 Ag2 S-film-based temperature sensor array, as well as a flexible printed circuit board and a deep-learning algorithm is designed for perceiving finger touch signals in real-time, and intelligent feedback of Chinese characters and letters on an app. These results strongly show that high-performance flexible inorganic semiconductors can be widely used in flexible electronics.

Connected Through Mediated Social Touch: “Better Than a Like on Facebook.” A Longitudinal Explorative Field Study Among Geographically Separated Romantic Couples

In recent years, there has been a significant increase in research on mediated communication via social touch. Previous studies indicated that mediated social touch (MST) can induce similar positive outcomes to interpersonal touch. However, studies investigating the user experience of MST technology predominantly involve brief experiments that are performed in well-controlled laboratory conditions. Hence, it is still unknown how MST affects the relationship and communication between physically separated partners in a romantic relationship, in a naturalistic setting and over a longer period of time. In a longitudinal explorative field study, the effects of MST on social connectedness and longing for touch among geographically separated romantic couples were investigated in a naturalistic setting. For 2 weeks, 17 couples used haptic bracelets, that were connected via the internet, to exchange mediated squeeze-like touch signals. Before and after this period, they reported their feelings of social connectedness and longing for touch through questionnaires. The results show that the use of haptic bracelets (1) enhanced social connectedness among geographically separated couples but (2) did not affect their longing for touch. Interviews conducted at the end of the study were analyzed following the thematic analysis method to generate prominent themes and patterns in using MST technology among participant couples. Two main themes were generated that captured (a) the way the bracelets fostered a positive one-to-one connection between partners and (b) the way in which participants worked around their frustrations with the bracelets. Detailed findings and limitations of this longitudinal field study are further discussed, and suggestions are made for future research.

A Self-Powered Triboelectric Hybrid Coder for Human-Machine Interaction.

Human-machine interfaces have penetrated various academia and industry fields such as smartphones, robotic, virtual reality, and wearable electronics, due to their abundant functional sensors and information interaction methods. Nevertheless, most sensors' complex structural design, monotonous parameter detection capability, and single information coding communication hinder their rapid development. As the frontier of self-powered sensors, the triboelectric nanogenerator (TENG) has multiple working modes and high structural adaptability, which is a potential solution for multi-parameter sensing and miniaturizing of traditional interactive electronic devices. Herein, a self-powered hybrid coder (SHC) based on TENG is reported to encode two action parameters of touch and press, which can be used as a smart interface for human-machine interaction. The top-down hollow structure of the SHC, not only constructs a compositing mode to generate stable touch and press signals but also builds a hybrid coding platform for generating action codes in synergy mode. When a finger touches or presses the SHC, Morse code and Gray code can be transmitted for text information or remote control of electric devices. This self-powered coder is of reference value for designing an alternative human-machine interface and having the potential to contribute to the next generation of highly integrated portable smart electronics.