Deep learning (DL) has been used for electromyographic (EMG) signal recognition and achieved high accuracy for multiple classification tasks. However, implementation in resource-constrained prostheses and human-computer interaction devices remains challenging. To overcome these problems, this paper implemented a low-power system for EMG gesture and force level recognition using Zynq architecture. Firstly, a lightweight network model structure was proposed by Ultra-lightweight depth separable convolution (UL-DSC) and channel attention-global average pooling (CA-GAP) to reduce the computational complexity while maintaining accuracy. A wearable EMG acquisition device for real-time data acquisition was subsequently developed with size of 36mm×28mm×4mm. Finally, a highly parallelized dedicated hardware accelerator architecture was designed for inference computation. 18 gestures were tested, including force levels from 22 healthy subjects. The results indicate that the average accuracy rate was 94.92% for a model with 5.0k parameters and a size of 0.026MB. Specifically, the average recognition accuracy for static and force-level gestures was 98.47% and 89.92%, respectively. The proposed hardware accelerator architecture was deployed with 8-bit precision, a single-frame signal inference time of 41.9µs, a power consumption of 0.317W, and a data throughput of 78.6 GOP/s.
Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of the amount of training data on benchmark performance. Two amputees who had undergone amputations years prior were recruited, and seven sessions of data were collected for analysis from each of them. Ninapro DB6, a publicly available database containing data from ten healthy subjects across ten sessions, was also included in this study. The experimental results show that the calibration data improved the average accuracy by 3.03%, 6.16%, and 9.73% for the two subjects and Ninapro DB6, respectively, compared to the baseline results. Moreover, it was discovered that increasing the number of training sessions was more effective in improving accuracy than increasing the number of trials. Three potential strategies are proposed in light of these findings to enhance cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, while also offering effective strategies to maximize the utilization of the entire dataset.
Amputees , Artificial Limbs , Humans , Electromyography/methods , Calibration , Pattern Recognition, Automated/methods , Upper Extremity , Algorithms
The development of pressure sensors with high sensitivity and a low detection limit for subtle mechanical force monitoring and the understanding of the sensing mechanism behind subtle mechanical force monitoring are of great significance for intelligent technology. Here, we proposed a graphene-based two-stage enhancement pressure sensor (GTEPS), and we analyzed the difference between subtle mechanical force monitoring and conventional mechanical force monitoring. The GTEPS exhibited a high sensitivity of 62.2 kPa-1 and a low detection limit of 0.1 Pa. Leveraging its excellent performance, the GTEPS was successfully applied in various subtle mechanical force monitoring applications, including acoustic wave detection, voice-print recognition, and pulse wave monitoring. In acoustic wave detection, the GTEPS achieved a 100% recognition accuracy for six words. In voiceprint recognition, the sensor exhibited accurate identification of distinct voiceprints among individuals. Furthermore, in pulse wave monitoring, GTEPS demonstrated effective detection of pulse waves. By combination of the pulse wave signals with electrocardiogram (ECG) signals, it enabled the assessment of blood pressure. These results demonstrate the excellent performance of GTEPS and highlight its great potential for subtle mechanical force monitoring and its various applications. The current results indicate that GTEPS shows great potential for applications in subtle mechanical force monitoring.
Flexible wearable devices have been widely used in biomedical applications, the Internet of Things, and other fields, attracting the attention of many researchers. The physiological and biochemical information on the human body reflects various health states, providing essential data for human health examination and personalized medical treatment. Meanwhile, physiological and biochemical information reveals the moving state and position of the human body, and it is the data basis for realizing human-computer interactions. Flexible wearable physiological and biochemical sensors provide real-time, human-friendly monitoring because of their light weight, wearability, and high flexibility. This paper reviews the latest advancements, strategies, and technologies of flexibly wearable physiological and biochemical sensors (pressure, strain, humidity, saliva, sweat, and tears). Next, we systematically summarize the integration principles of flexible physiological and biochemical sensors with the current research progress. Finally, important directions and challenges of physiological, biochemical, and multimodal sensors are proposed to realize their potential applications for human movement, health monitoring, and personalized medicine.
Wearable Electronic Devices , Humans , Sweat , Saliva , Tears
A miniature Fourier transform spectrometer is proposed using a thin-film lithium niobate electro-optical modulator instead of the conventional modulator made by titanium diffusion in lithium niobate. The modulator was fabricated by a contact lithography process, and its voltage-length and optical waveguide loss were 2.26 V·cm and 1.01 dB/cm, respectively. Based on the wavelength dispersion of the half-wave voltage of the fabricated modulator, the emission spectrum of the input signal was retrieved by Fourier transform processing of the interferogram, and the analysis of the experimental data of monochromatic light shows that the proposed miniaturized FTS can effectively identify the input signal wavelength.
Countless waveguides have been designed based on four basic bends: circular bend, sine/cosine bend, Euler bend (developed in 1744) and Bezier bend (developed in 1962). This paper proposes an n-adjustable (NA) bend, which has superior properties compared to other basic bends. Simulations and experiments indicate that the NA bends can show lower losses than other basic bends by adjusting n values. The circular bend and Euler bend are special cases of the proposed NA bend as n equals 0 and 1, respectively. The proposed bend are promising candidates for low-loss compact photonic integrated circuits.
Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.
Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.
Nanocomposites , Nanotubes, Carbon , Wearable Electronic Devices , Perception
Pb-free perovskite material is considered to be a promising material utilized in next-generation X-ray detectors due to its high X-ray absorption coefficient, decent carrier transport properties, and relatively low toxicity. However, the pixelation of the perovskite material with an industry-level photolithography processing method remains challenging due to its poor structural stability. Herein, we use Cs2AgBiBr6 perovskite material as the prototype and investigate its interaction with photolithographic polar solvents. Inspired by that, we propose a wafer-scale photolithography patterning method, where the pixeled perovskite array devices for X-ray detection are successfully prepared. The devices based on pixeled Pb-free perovskite material show a high detection sensitivity up to 19118 ± 763 µC Gyair-1 cm-2, which is comparable to devices with Pb-based perovskite materials and superior to the detection sensitivity (â¼20 µC Gyair-1 cm-2) of the commercial a-Se detector. After pixelation, the devices achieve an improved spatial resolution capacity with the spatial frequency from 2.7 to 7.8 lp mm-1 at modulation-transfer-function (MTF) = 0.2. Thus, this work may contribute to the development of high-performance array X-ray detectors based on Cs2AgBiBr6 perovskite material.
We report an artificial eardrum using an acoustic sensor based on two-dimensional MXene (Ti3C2Tx), which mimics the function of a human eardrum for realizing voice detection and recognition. Using MXene with a large interlayer distance and micropyramid polydimethylsiloxane arrays can enable a two-stage amplification of pressure and acoustic sensing. The MXene artificial eardrum shows an extremely high sensitivity of 62 kPa-1 and a very low detection limit of 0.1 Pa. Notably, benefiting from the ultrasensitive MXene eardrum, the machine-learning algorithm for real-time voice classification can be realized with high accuracy. The 280 voice signals are successfully classified for seven categories, and a high accuracy of 96.4 and 95% can be achieved by the training dataset and the test dataset, respectively. The current results indicate that the MXene artificial intelligent eardrum shows great potential for applications in wearable acoustical health care devices.
Ultra-scaled transistors are of interest in the development of next-generation electronic devices1-3. Although atomically thin molybdenum disulfide (MoS2) transistors have been reported4, the fabrication of devices with gate lengths below 1 nm has been challenging5. Here we demonstrate side-wall MoS2 transistors with an atomically thin channel and a physical gate length of sub-1 nm using the edge of a graphene layer as the gate electrode. The approach uses large-area graphene and MoS2 films grown by chemical vapour deposition for the fabrication of side-wall transistors on a 2-inch wafer. These devices have On/Off ratios up to 1.02 × 105 and subthreshold swing values down to 117 mV dec-1. Simulation results indicate that the MoS2 side-wall effective channel length approaches 0.34 nm in the On state and 4.54 nm in the Off state. This work can promote Moore's law of the scaling down of transistors for next-generation electronics.
Cracks play an important role in strain sensors. However, a systematic analysis of how cracks influence the strain sensors has not been proposed. In this work, an intelligent and highly sensitive strain sensor based on indium tin oxide (ITO)/polyurethane (PU) micromesh is realized. The micromesh has good skin compatibility, water vapor permeability, and stability. Due to the color of the ITO/PU micromesh, it can be invisible on the skin. Based on the fragility of ITO, the density and resistance of cracks in the micromesh are greatly improved. Therefore, the ITO/PU micromesh strain sensor (IMSS) has an ultrahigh gauge factor (744.3). In addition, a finite element model based on four resistance layers is proposed to explain the performance of the IMSS and show the importance of high-density cracks. Compared with other strain sensors based on low-density cracks, the IMSS based on high-density cracks has larger sensitivity and better linearity. Physiological signals, such as respiration, pulse, and joint motion, can be monitored using the IMSS self-fixed on the skin. Finally, an invisible and artificial throat has been realized by combining the IMSS with a convolutional neural network algorithm. The artificial throat can translate the throat vibrations of the tester automatically with an accuracy of 86.5%. This work has great potential in health care and language function reconstruction.
Electronic skin (e-skin) has attracted tremendous interest due to its diverse potential applications, including in physiological signal detection, health monitoring, and artificial throats. However, the major drawbacks of traditional e-skin are the weak adhesion of substrates, incompatibility between sensitivity and stretchability, and its single function. These shortcomings limit the application of e-skin and increase the complexity of its multifunctional integration. Herein, the synergistic network of crosslinked SWCNTs within and between multilayered graphene layers was directly drip coated onto the PU thin film with self-adhesion to fabricate versatile e-skin. The excellent mechanical properties of prepared e-skin arise from the sufficient conductive paths guaranteed by SWCNTs in small and large deformation under various strains. The prepared e-skin exhibits a low detection limit, as small as 0.5% strain, and compatibility between sensitivity and stretchability with a gauge factor (GF) of 964 at a strain of 0-30%, and 2743 at a strain of 30-60%. In physiological signals detection application, the e-skin demonstrates the detection of subtle motions, such as artery pulse and blinking, as well as large body motions, such as knee joint bending, elbow movement, and neck movement. In artificial throat application, the e-skin integrates sound recognition and sound emitting and shows clear and distinct responses between different throat muscle movements and different words for sound signal acquisition and recognition, in conjunction with superior sound emission performance with a sound spectrum response of 71 dB (f = 12.5 kHz). Overall, the presented comprehensive study of novel materials, structures, properties, and mechanisms offers promising potential in physiological signals detection and artificial throat applications.
Two-dimensional (2D) heterostructures show great potential in achieving negative differential resistance (NDR) effects by Esaki diodes and or resonant tunneling diodes. However, most of the reported Esaki diode-based NDR devices realized by bulk 2D films lack sufficient gate tunability, and the tuning of NDR behavior from appearing to vanishing remains elusive. Here, a gate-tunable NDR device is reported based on a vertically stacked black phosphorus (BP) and molybdenum disulfide (MoS2) thin 2D heterojunction. At room temperature, a rectifying ratio of â¼6 orders of magnitude from a reverse rectifying diode to a forward rectifying diode by gate modulation is obtained. Through analyzing the temperature-dependent electrical properties, the tunneling mechanism at a certain gate voltage range is revealed. Moreover, the switchable and continuously gate-tunable NDR behavior is realized with a maximum peak-to-valley ratio of 1.23 at 77 K, as shown in the IDS mappings by sweeping VDS under different VGS. In addition, a compact model for gate-tunable NDR behavior in 2D heterostructures is proposed. To our best knowledge, this is the first demonstration of NDR behavior in BP-MoS2 heterostructures. Consequently, this work sheds light on the gate-tunable NDR devices and reconfigurable logic devices for realizing ternary and reconfigurable logic systems.
High-performance electromagnetic interference (EMI) shielding materials with ultralow density, excellent flexibility, and good mechanical properties are highly desirable for aerospace and wearable electronics. Herein, honeycomb porous graphene (HPG) fabricated by laser scribing technology is reported for EMI shielding and wearable applications. Due to the honeycomb structure, the HPG exhibits an EMI shielding effectiveness (SE) up to 45 dB at a thickness of 48.3 µm. The single-piece HPG exhibits an ultrahigh absolute shielding effectiveness (SSE/t) of 240â¯123 dB cm2/g with an ultralow density of 0.0388 g/cm3, which is significantly superior to the reported materials such as carbon-based, MXene, and metal materials. Furthermore, MXene and AgNWs are employed to cover the honeycomb holes of the HPG to enhance surface reflection; thus, the SSE/t of the HPG/AgNWs composite membrane can reach up to 292â¯754 dB cm2/g. More importantly, the HPG exhibits excellent mechanical stability and durability in cyclic stretching and bending, which can be used to monitor weak physiological signals such as pulse, respiration, and laryngeal movement of humans. Therefore, the lightweight and flexible HPG exhibits excellent EMI shielding performance and mechanical properties, along with its low cost and ease of mass production, which is promising for practical applications in EMI shielding and wearable electronics.
Graphite , Wearable Electronic Devices , Electromagnetic Phenomena , Humans
Current wearable sensors are fabricated with substrates, which limits the comfort, flexibility, stretchability, and induces interface mismatch. In addition, the substrate prevents the evaporation of sweat and is harmful to skin health. In this work, we have enabled the substrate-free laser scribed graphene (SFG) electronic skin (e-skin) with multifunctions. Compared with the e-skin with the substrate, the SFG has good gas permeability, low impedance, and flexibility. Only assisted using water, the SFG can be transferred to almost any objects including silicon and human skin and it can even be suspended. Many through-holes like stomas in leaf can be formed in the SFG, which make it breathable. After designing the pattern, the gauge factor (GF) of graphene electronic skin (GES) can be designed as the strain sensor. Physiological signals such as respiration, human motion, and electrocardiogram (ECG) can be detected. Moreover, the suspended SFG detect vibrations with high sensitivity. Due to the substrate-free structure, the impedance between SFG e-skin and the human body decreases greatly. Finally, an ECG detecting system has been designed based on the GES, which can monitor the body condition in real time. To analyze the ECG signals automatically, a convolutional neural network (CNN) was built and trained successfully. This work has high potential in the field of health telemonitoring.
Artificial Intelligence , Graphite/chemistry , Wearable Electronic Devices , Humans , Particle Size , Surface Properties
Thermoacoustic (TA) effect has been discovered for more than 130 years. However, limited by the material characteristics, the performance of a TA sound source could not be compared with magnetoelectric and piezoelectric loudspeakers. Recently, graphene, a two-dimensional material with the lowest heat capacity per unit area, was discovered to have a good TA performance. Compared with a traditional sound source, graphene TA sound sources (GTASSs) have many advantages, such as small volume, no diaphragm vibration, wide frequency range, high transparency, good flexibility, and high sound pressure level (SPL). Therefore, graphene has a great potential as a next-generation sound source. Photoacoustic (PA) imaging can also be applied to the diagnosis and treatment of diseases using the photothermo-acoustic (PTA) effect. Therefore, in this review, we will introduce the history of TA devices. Then, the theory and simulation model of TA will be analyzed in detail. After that, we will talk about the graphene synthesis method. To improve the performance of GTASSs, many strategies such as lowering the thickness and using porous or suspended structures will be introduced. With a good PTA effect and large specific area, graphene PA imaging and drug delivery is a promising prospect in cancer treatment. Finally, the challenges and prospects of GTASSs will be discussed.
Single-crystal (SC) perovskite is currently a promising material due to its high quantum efficiency and long diffusion length. However, the reported perovskite photodetection range (<800 nm) and response time (>10 µs) are still limited. Here, to promote the development of perovskite-integrated optoelectronic devices, this work demonstrates wider photodetection range and shorter response time perovskite photodetector by integrating the SC CH3NH3PbBr3 (MAPbBr3) perovskite on silicon (Si). The Si/MAPbBr3 heterojunction photodetector with an improved interface exhibits high-speed, broad-spectrum, and long-term stability performances. To the best of our knowledge, the measured detectable spectrum (405-1064 nm) largely expands the widest response range reported in previous perovskite-based photodetectors. In addition, the rise time is as fast as 520 ns, which is comparable to that of commercial germanium photodetectors. Moreover, the Si/MAPbBr3 device can maintain excellent photocurrent performance for up to 3 months. Furthermore, typical gray scale face imaging is realized by scanning the Si/MAPbBr3 single-pixel photodetector. This work using an ultrafast photodetector by directly integrating perovskite on Si can promote advances in next-generation integrated optoelectronic technology.
Humidity sensors based on flexible sensitive nanomaterials are very attractive in noncontact healthcare monitoring. However, the existing humidity sensors have some shortcomings such as limited sensitivity, narrow relative humidity (RH) range, and a complex process. Herein, we show that a tin sulphide (SnS) nanoflakes-based sensor presents high humidity sensing behaviour both in rigid and flexible substrate. The sensing mechanism based on the Schottky nature of a SnS-metal contact endows the as-fabricated sensor with a high response of 2491000% towards a wide RH range from 3% RH to 99% RH. The response and recovery time of the sensor are 6 s and 4 s, respectively. Besides, the flexible SnS nanoflakes-based humidity sensor with a polyimide substrate can be well attached to the skin and exhibits stable humidity sensing performance in the natural flat state and under bending loading. Moreover, the first-principles analysis is performed to prove the high specificity of SnS to the moisture (H2O) in the air. Benefiting from its promising advantages, we explore some application of the SnS nanoflakes-based sensors in detection of breathing patterns and non-contact finger tips sensing behaviour. The sensor can monitor the respiration pattern of a human being accurately, and recognize the movement of the fingertip speedily. This novel humidity sensor shows great promising application in physiological and physical monitoring, portable diagnosis system, and noncontact interface localization.
Biosensing Techniques/instrumentation , Humidity , Nanostructures/chemistry , Sulfides/chemistry , Tin Compounds/chemistry , Water/chemistry , Humans , Monitoring, Ambulatory/instrumentation , Nanostructures/ultrastructure , Reaction Time , Silicon Dioxide/chemistry , Surface Properties
Two-dimensional (2D) hybrid perovskite sandwiched between two long-chain organic layers is an emerging class of low-cost semiconductor materials with unique optical properties and improved moisture stability. Unlike conventional semiconductors, ion migration in perovskite is a unique phenomenon possibly responsible for long carrier lifetime, current-voltage hysteresis, and low-frequency giant dielectric response. While there are many studies of ion migration in bulk hybrid perovskite, not much is known for its 2D counterparts, especially for ion migration induced by light excitation. Here, we construct an exfoliated 2D perovskite/carbon nanotube (CNT) heterostructure field effect transistor (FET), not only to demonstrate its potential in photomemory applications, but also to study the light induced ion migration mechanisms. We show that the FET I-V characteristic curve can be regulated by light and shows two opposite trends under different CNT oxygen doping conditions. Our temperature-dependent study indicates that the change in the I-V curve is probably caused by ion redistribution in the 2D hybrid perovskite. The first principle calculation shows the reduction of the migration barrier of I vacancy under light excitation. The device simulation shows that the increase of 2D hybrid perovskite dielectric constant (enabled by the increased ion migration) can change the I-V curve in the trends observed experimentally. Finally, the so synthesized FET shows the multilevel photomemory function. Our work shows that not only we could understand the unique ion migration behavior in 2D hybrid perovskite, it might also be used for many future memory function related applications not realizable in traditional semiconductors.
