Mechanoluminescent/Electric Dual-Mode Sensors Enabled by Trace Carbon Nanotubes.

Mechanoluminescence (ML)-based sensors are emerging as promising wearable devices, attracting attention for their self-powered visualization of mechanical stimuli. However, challenges such as weak brightness, high activation threshold, and intermittent signal output have hindered their development. Here, a mechanoluminescent/electric dual-mode strain sensor is presented that offers enhanced ML sensing and reliable electrical sensing simultaneously. The strain sensor is fabricated via an optimized dip-coating method, featuring a sandwich structure with a single-walled carbon nanotube (SWNT) interlayer and two polydimethylsiloxane (PDMS)/ZnS:Cu luminescence layers. The integral mechanical reinforcement framework provided by the SWNT interlayer improves the ML intensity of the SWNT/PDMS/ZnS:Cu composite film. Compared to conventional nanoparticle fillers, the ML intensity is enhanced nearly tenfold with a trace amount of SWNT (only 0.01 wt.%). In addition, the excellent electrical conductivity of SWNT forms a conductive network, ensuring continuous and stable electrical sensing. These strain sensors enable comprehensive and precise monitoring of human behavior through both electrical (relative resistance change) and optical (ML intensity) methods, paving the way for the development of advanced visual sensing and smart wearable electronics in the future.

Emerging MXene-Based Flexible Tactile Sensors for Health Monitoring and Haptic Perception.

Due to their potential applications in physiological monitoring, diagnosis, human prosthetics, haptic perception, and human-machine interaction, flexible tactile sensors have attracted wide research interest in recent years. Thanks to the advances in material engineering, high performance flexible tactile sensors have been obtained. Among the representative pressure sensing materials, 2D layered nanomaterials have many properties that are superior to those of bulk nanomaterials and are more suitable for high performance flexible sensors. As a class of 2D inorganic compounds in materials science, MXene has excellent electrical, mechanical, and biological compatibility. MXene-based composites have proven to be promising candidates for flexible tactile sensors due to their excellent stretchability and metallic conductivity. Therefore, great efforts have been devoted to the development of MXene-based composites for flexible sensor applications. In this paper, the controllable preparation and characterization of MXene are introduced. Then, the recent progresses on fabrication strategies, operating mechanisms, and device performance of MXene composite-based flexible tactile sensors, including flexible piezoresistive sensors, capacitive sensors, piezoelectric sensors, triboelectric sensors are reviewed. After that, the applications of MXene material-based flexible electronics in human motion monitoring, healthcare, prosthetics, and artificial intelligence are discussed. Finally, the challenges and perspectives for MXene-based tactile sensors are summarized.

Self-Healable Elastomeric Network with Dynamic Disulfide, Imine, and Hydrogen Bonds for Flexible Strain Sensor.

Self-healable and stretchable elastomeric material is essential for the development of flexible electronics devices to ensure their stable performance. In this study, a strain sensor (PIH2 T1 -tri/CNT-3) composed of self-repairable crosslinked elastomer substrate (PIH2 T1 -tri, containing multiple reversible repairing sites such as disulfide, imine, and hydrogen bonds) and conductive layer (carbon nanotube, CNT) was prepared. The PIH2 T1 -tri elastomer had excellent self-healing ability (healing efficiency=91 %). It exhibited good mechanical integrity in terms of elongation at break (672 %), tensile strength (1.41 MPa). The Young's modulus (0.39 MPa) was close to that of human skin. The PIH2 T1 -tri/CNT-3 sensor also demonstrated an effective self-healing function for electrical conduction and sensing property. Meanwhile, it had high sensitivity (gauge factor (GF)=24.1), short response time (120 ms), and long-term durability (4000 cycles). This study offers a novel self-healable elastomer platform with carbon based conductive components to develop flexible strain sensors towards high performance soft electronics.

A Probabilistic Model of Human Activity Recognition with Loose Clothing.

Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve higher activity recognition accuracy than rigid-attached sensors, particularly when predicting from short time windows. This work presents a probabilistic model that explains improved responsiveness and accuracy with fabric sensing from the increased statistical distance between movements recorded. The accuracy of the comfortable fabric-attached sensor can be increased by 67% more than rigid-attached sensors when the window size is 0.5s. Simulated and real human motion capture experiments with several participants confirm the model's predictions, demonstrating that this counterintuitive effect is accurately captured.

Sensor Fusion Approach for Multiple Human Motion Detection for Indoor Surveillance Use-Case.

Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by exploring the benefits of a low-level sensor fusion approach that combines grayscale and neuromorphic vision sensor (NVS) data. We first generate a custom dataset using an NVS camera in an indoor environment. We then conduct a comprehensive study by experimenting with different image features and deep learning networks, followed by a multi-input fusion strategy to optimize our experiments with respect to overfitting. Our primary goal is to determine the best input feature types for multi-human motion detection using statistical analysis. We find that there is a significant difference between the input features of optimized backbones, with the best strategy depending on the amount of available data. Specifically, under a low-data regime, event-based frames seem to be the preferred input feature type, while higher data availability benefits the combined use of grayscale and optical flow features. Our results demonstrate the potential of sensor fusion and deep learning techniques for multi-human tracking in indoor surveillance, although it is acknowledged that further studies are needed to confirm our findings.

Mechanically Ultra-Robust, Elastic, Conductive, and Multifunctional Hybrid Hydrogel for a Triboelectric Nanogenerator and Flexible/Wearable Sensor.

Flexibility/wearable electronics such as strain/pressure sensors in human-machine interactions (HMI) are highly developed nowadays. However, challenges remain because of the lack of flexibility, fatigue resistance, and versatility, leading to mechanical damage to device materials during practical applications. In this work, a triple-network conductive hydrogel is fabricated by combining 2D Ti3 C2 Tx nanosheets with two kinds of 1D polymer chains, polyacrylamide, and polyvinyl alcohol. The Ti3 C2 Tx nanosheets act as the crosslinkers, which combine the two polymer chains of PAM and PVA via hydrogen bonds. Such a unique structure endows the hydrogel (MPP-hydrogel) with merits such as mechanical ultra-robust, super-elasticity, and excellent fatigue resistance. More importantly, the introduced Ti3 C2 Tx nanosheets not only enhance the hydrogel's conductivity but help form double electric layers (DELs) between the MXene nanosheets and the free water molecules inside the MPP-hydrogel. When the MPP-hydrogel is used as the electrode of the triboelectric nanogenerator (MPP-TENG), due to the dynamic balance of the DELs under the initial potential difference generated from the contact electrification as the driving force, an enhanced electrical output of the TENG is generated. Moreover, flexible strain/pressure sensors for tiny and low-frequency human motion detection are achieved. This work demonstrates a promising flexible electronic material for e-skin and HMI.

Dual-Scale Porous Composite for Tactile Sensor with High Sensitivity over an Ultrawide Sensing Range.

Porous structures have been utilized in tactile sensors to improve sensitivity owing to their excellent deformability. Recently, tactile sensors using porous structures have been used in practical applications, such as bio-signal monitoring. However, highly sensitive responses are limited to the low-pressure range, and their sensitivity significantly decreases in a higher-pressure range. Several approaches for developing tactile sensors with high sensitivity overing a wide pressure range have been proposed; however, achieving high sensitivity and wide sensing range remains a crucial challenge. This report presents a carbon nanotube (CNT)-coated CNT-polydimethylsiloxane (PDMS) composite having dual-scale pores for tactile sensors with high sensitivity over a wide pressure range. The porous polymer frame formed with dense pores of dual sizes facilitates the closure of large and small pores at low and high pressures, respectively. This results in an apparent increase in the number of contact points between the CNT-CNT at the pores even under a wide pressure range. Furthermore, the piezoresistivity of the CNT-PDMS composite contributes to achieving a high sensitivity of the tactile sensor over a wide pressure range. Based on these mechanisms, various human movements over a broad pressure spectrum are monitored to investigate the practical usefulness of the sensor.

A highly sensitive strain sensor with a sandwich structure composed of two silver nanoparticles layers and one silver nanowires layer for human motion detection.

The fabrication of strain sensors with high sensitivity, large sensing range and excellent stability is highly desirable because of their promising applications in human motion detection, human-machine interface and electric skin, etc. Herein, by introducing a highly conductive silver nanowire (AgNW) layer between two serried silver nanoparticle (AgNP) layers, forming a sandwich structure, a strain sensor with high sensitivity (a large gauge factor of 2.8 × 105), large sensing range (up to 80% strain) and excellent stability (over 1000 cycles) can be achieved. A combination of experimental and mechanism studies shows that the high performance of the obtained strain sensor is ascribed to the synergy of the highly conductive AgNW layer, astatic AgNP layers and the presence of large cracks in stretching. As a proof-of-concept application, the obtained strain sensor can be used for highly effective human motion detection ranging from large scale motions, i.e. kneel bending and wrist flexion, to subtle scale motions, i.e. pulse and swallowing.

Customization of Conductive Elastomer Based on PVA/PEI for Stretchable Sensors.

Conductive, stretchable, environmentally-friendly, and strain-sensitive elastomers are attracting immense research interest because of their potential applications in various areas, such as human-machine interfaces, healthcare monitoring, and soft robots. Herein, a binary networked elastomer is reported based on a composite hydrogel of polyvinyl alcohol (PVA) and polyethyleneimine (PEI), which is demonstrated to be ultrastretchable, mechanically robust, biosafe, and antibacterial. The mechanical stretchability and toughness of the hydrogels are optimized by tuning the constituent ratio and water content. The optimal hydrogel (PVA2 PEI1 -75) displays an impressive tensile strain as high as 500% with a corresponding tensile stress of 0.6 MPa. Furthermore, the hydrogel elastomer is utilized to fabricate piezoresistive sensors. The as-made strain sensor displays seductive capability to monitor and distinguish multifarious human motions with high accuracy and sensitivity, like facial expressions and vocal signals. Therefore, the elastomer reported in this study holds great potential for sensing applications in the era of the Internet of Things (IoTs).

A Flexible Strain Sensor Based on the Porous Structure of a Carbon Black/Carbon Nanotube Conducting Network for Human Motion Detection.

High-performance flexible strain sensors are playing an increasingly important role in wearable electronics, such as human motion detection and health monitoring, with broad application prospects. This study developed a flexible resistance strain sensor with a porous structure composed of carbon black and multi-walled carbon nanotubes. A simple and low-cost spraying method for the surface of a porous polydimethylsiloxane substrate was used to form a layer of synergized conductive networks built by carbon black and multi-walled carbon nanotubes. By combining the advantages of the synergetic effects of mixed carbon black and carbon nanotubes and their porous polydimethylsiloxane structure, the performance of the sensor was improved. The results show that the sensor has a high sensitivity (GF) (up to 61.82), a wide strain range (0%-130%), a good linearity, and a high stability. Based on the excellent performance of the sensor, the flexible strain designed sensor was installed successfully on different joints of the human body, allowing for the monitoring of human movement and human respiratory changes. These results indicate that the sensor has promising potential for applications in human motion monitoring and physiological activity monitoring.

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor.

Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user's body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application.

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare.

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person's body. However, putting devices on a person's body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi.

As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively.

Sensitive and Flexible Polymeric Strain Sensor for Accurate Human Motion Monitoring.

Flexible electronic devices offer the capability to integrate and adapt with human body. These devices are mountable on surfaces with various shapes, which allow us to attach them to clothes or directly onto the body. This paper suggests a facile fabrication strategy via electrospinning to develop a stretchable, and sensitive poly (vinylidene fluoride) nanofibrous strain sensor for human motion monitoring. A complete characterization on the single PVDF nano fiber has been performed. The charge generated by PVDF electrospun strain sensor changes was employed as a parameter to control the finger motion of the robotic arm. As a proof of concept, we developed a smart glove with five sensors integrated into it to detect the fingers motion and transfer it to a robotic hand. Our results shows that the proposed strain sensors are able to detect tiny motion of fingers and successfully run the robotic hand.

Multi-Target Intense Human Motion Analysis and Detection Using Channel State Information.

Intense human motion, such as hitting, kicking, and falling, in some particular scenes indicates the occurrence of abnormal events like violence and school bullying. Camera-based human motion detection is an effective way to analyze human behavior and detect intense human motion. However, even if the camera is properly deployed, it will still generate blind spots. Moreover, camera-based methods cannot be used in places such as restrooms and dressing rooms due to privacy issues. In this paper, we propose a multi-target intense human motion detection scheme using commercial Wi-Fi infrastructures. Compared with human daily activities, intense human motion usually has the characteristics of intensity, rapid change, irregularity, large amplitude, and continuity. We studied the changing pattern of Channel State Information (CSI) influenced by intense human motion, and extracted features in the pattern by conducting a large number of experiments. Considering occlusion exists in some complex scenarios, we distinguished the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions in the case of obstacles appearing between the transmitter and the receiver, which further improves the overall performance. We implemented the intense human motion detection system using single commercial Wi-Fi devices, and evaluated it in real indoor environments. The experimental results show that our system can achieve intense human motion detection rate of 90%.

WiFi-Based Real-Time Calibration-Free Passive Human Motion Detection.

With the rapid development of WLAN technology, wireless device-free passive human detection becomes a newly-developing technique and holds more potential to worldwide and ubiquitous smart applications. Recently, indoor fine-grained device-free passive human motion detection based on the PHY layer information is rapidly developed. Previous wireless device-free passive human detection systems either rely on deploying specialized systems with dense transmitter-receiver links or elaborate off-line training process, which blocks rapid deployment and weakens system robustness. In the paper, we explore to research a novel fine-grained real-time calibration-free device-free passive human motion via physical layer information, which is independent of indoor scenarios and needs no prior-calibration and normal profile. We investigate sensitivities of amplitude and phase to human motion, and discover that phase feature is more sensitive to human motion, especially to slow human motion. Aiming at lightweight and robust device-free passive human motion detection, we develop two novel and practical schemes: short-term averaged variance ratio (SVR) and long-term averaged variance ratio (LVR). We realize system design with commercial WiFi devices and evaluate it in typical multipath-rich indoor scenarios. As demonstrated in the experiments, our approach can achieve a high detection rate and low false positive rate.

Multi-Layer Polyurethane-Fiber-Prepared Entangled Strain Sensor with Tunable Sensitivity and Working Range for Human Motion Detection.

The entanglement of fibers can form physical and topological structures, with the resulting bending and stretching strains causing localized changes in pressure. In this study, a multi-layer polyurethane-fiber-prepared (MPF) sensor was developed by coating the CNT/PU sensing layer on the outside of an elastic electrode through a wet-film method. The entangled topology of two MPFs was utilized to convert the stretching strain into localized pressure at the contact area, enabling the perception of stretching strain. The influence of coating mechanical properties and surface structure on strain sensing performance was investigated. A force regulator was introduced to regulate the mechanical properties of the entangled topology of MPF. By modifying the thickness and length proportion of the force regulator, the sensitivity factor and sensitivity range of the sensor could be controlled, achieving a high sensitivity factor of up to 127.74 and a sensitivity range of up to 58%. Eight sensors were integrated into a sensor array and integrated into a dance costume, successfully monitoring the multi-axis motion of the dancer's lumbar spine. This provides a new approach for wearable biomechanical sensors.

Polymerizable deep eutectic solvent treated lignocellulose: Green strategy for synergetic production of tough strain sensing elastomers and nanocellulose.

Liquid free ion-conductive elastomers (ICEs) have demonstrated promising potential in various advanced application scenarios including sensor, artificial skin, and human-machine interface. However, ICEs that synchronously possess toughness, adhesiveness, stability, and anti-bacterial capability are still difficult to achieve yet highly demanded. Here, a one-pot green and sustainable strategy was proposed to fabricate multifunctional ICEs by extracting non-cellulose components (mainly lignin and hemicellulose) from lignocellulose with polymerizable deep eutectic solvents (PDES) and the subsequent in-situ photo-polymerization process. Ascribing to the uniform dispersion of non-cellulose components in PDES, the resultant ICEs demonstrated promising mechanical strength (a tensile strength of ~1200 kPa), high toughness (~9.1 MJ m-3), favorable adhesion (a lap-shear strength up to ~61.5 kPa toward metal), conducive stabilities, and anti-bacterial capabilities. With the help of such advantages, the ICEs exhibited sensitive (a gauge factor of ~23.5) and stable (~4000 cycles) performances in human motion and physiological signal detection even under sub-zero temperatures (e.g., -20 °C). Besides, the residue cellulose can be mechanically isolated into nanoscale fibers, which matched the idea of green chemistry.

An adhesive, stretchable, and freeze-resistant conductive hydrogel strain sensor for handwriting recognition and depth motion monitoring.

Wearable electronics based on conductive hydrogels (CHs) offer remarkable flexibility, conductivity, and versatility. However, the flexibility, adhesiveness, and conductivity of traditional CHs deteriorate when they freeze, thereby limiting their utility in challenging environments. In this work, we introduce a PHEA-NaSS/G hydrogel that can be conveniently fabricated into a freeze-resistant conductive hydrogel by weakening the hydrogen bonds between water molecules. This is achieved through the synergistic interaction between the charged polar end group (-SO3-) and the glycerol-water binary solvent system. The conductive hydrogel is simultaneously endowed with tunable mechanical properties and conductive pathways by the modulation caused by varying material compositions. Due to the uniform interconnectivity of the network structure resulting from strong intermolecular interactions and the enhancement effect of charged polar end-groups, the resulting hydrogel exhibits 174 kPa tensile strength, 2105 % tensile strain, and excellent sensing ability (GF = 2.86, response time: 121 ms), and the sensor is well suited for repeatable and stable monitoring of human motion. Additionally, using the Full Convolutional Network (FCN) algorithm, the sensor can be used to recognize English letter handwriting with an accuracy of 96.4 %. This hydrogel strain sensor provides a simple method for creating multi-functional electronic devices, with significant potential in the fields of multifunctional electronics such as soft robotics, health monitoring, and human-computer interaction.

A synergistic enhancement strategy for mechanical and conductive properties of hydrogels with dual ionically cross-linked κ-carrageenan/poly(sodium acrylate-co-acrylamide) network.

Applying conductive hydrogels in electronic skin, health monitoring, and wearable devices has aroused great research interest. Yet, it remains a significant challenge to prepare conductive hydrogels simultaneously with superior mechanical, self-recovery, and conductivity performance. Herein, a dual ionically cross-linked double network (DN) hydrogel is fabricated based on K+ and Fe3+ ion cross-linked κ-carrageenan (κ-CG) and Fe3+ ion cross-linked poly(sodium acrylate-co-acrylamide) P(AANa-co-AM). Benefiting from the abundance of hydrogen bonds and metal coordination bonds, the conductive hydrogel has excellent mechanical properties (fracture strain up to 1420 %, fracture stress up to 2.30 MPa, and toughness up to 20.63 MJ/m3) and good self-recovery performance (the recovery rate of the toughness can reach 85 % after waiting for 1 h). Meanwhile, due to the introduction of dual metal ions of K+ and Fe3+, the ionic conductivity of conductive hydrogel is up to 1.42 S/m. Furthermore, the hydrogel strain sensor has good sensitivity with a gauge factor (GF) of 2.41 (0-100 %). It can be a wearable sensor that monitors different human motions, such as sit-ups. This work offers a new synergistic strategy for designing a hydrogel strain sensor with high mechanical, self-recovery, and conductive properties.