Anti-Freezing and Ultrasensitive Zwitterionic Betaine Hydrogel-Based Strain Sensor for Motion Monitoring and Human-Machine Interaction.

Ultrasensitive and reliable conductive hydrogels are significant in the construction of human-machine twinning systems. However, in extremely cold environments, freezing severely limits the application of hydrogel-based sensors. Herein, building on biomimetics, a zwitterionic hydrogel was elaborated for human-machine interaction employing multichemical bonding synergies and experimental signal analyses. The covalent bonds, hydrogen bonds, and electrostatic interactions construct a dense double network structure favorable for stress dispersion and hydrogen bond regeneration. In particular, zwitterions and ionic conductors maintained excellent strain response (99 ms) and electrical sensitivity (gauge factor = 14.52) in the dense hydrogel structure while immobilizing water molecules to enhance the weather resistance (-68 °C). Inspired by the high sensitivity, zwitterionic hydrogel-based strain sensors and remote-control gloves were designed by analyzing the experimental signals, demonstrating promising potential applications within specialized flexible materials and human-machine symbiotic systems.

Self-healing flexible strain sensor fabricated through 3D printing template sacrifice for motion monitoring with enhanced healing and mechanical performance.

With the advancements in flexible materials and information technology, flexible sensors are becoming increasingly pervasive in various aspects of life and production. They hold immense potential for further development in areas such as motion detection, electronic skin, soft robots, and wearable devices. Aminopropyl-terminated polydimethylsiloxane (PDMS) was used as the raw material, while a diisocyanate reagent served as the cross-linking agent for the polymerization reaction, which involved the introduction of ureido groups, containing N-H and C=O bonds, into the long siloxane chain. The dynamic hydrogen bonding between the clusters completes the self-healing of the material. Using 1-[3-(trimethoxysilyl)propyl]urea as a grafting agent, the urea groups are introduced into graphene oxide and carbon nanotubes (CNTs) as conductive fillers. Subsequently, a flexible polymer is used as the substrate to prepare conductive flexible self-healing composites. By controlling the amount of conductive fillers, flexible strain materials with varying sensitivities are obtained. Design the structure of the flexible strain sensor using three-dimensional (3D) modeling software with deposition printing method.

Toward flexible piezoresistive strain sensors based on polymer nanocomposites: a review on fundamentals, performance, and applications.

The fundamentals, performance, and applications of piezoresistive strain sensors based on polymer nanocomposites are summarized herein. The addition of conductive nanoparticles to a flexible polymer matrix has emerged as a possible alternative to conventional strain gauges, which have limitations in detecting small strain levels and adapting to different surfaces. The evaluation of the properties or performance parameters of strain sensors such as the elongation at break, sensitivity, linearity, hysteresis, transient response, stability, and durability are explained in this review. Moreover, these nanocomposites can be exposed to different environmental conditions throughout their lifetime, including different temperature, humidity or acidity/alkalinity levels, that can affect performance parameters. The development of flexible piezoresistive sensors based on nanocomposites has emerged in recent years for applications related to the biomedical field, smart robotics, and structural health monitoring. However, there are still challenges to overcome in designing high-performance flexible sensors for practical implementation. Overall, this paper provides a comprehensive overview of the current state of research on flexible piezoresistive strain sensors based on polymer nanocomposites, which can be a viable option to address some of the major technological challenges that the future holds.

An Energy-Efficient Flexible Multi-Modal Wireless Sweat Sensing System Based on Laser Induced Graphene.

Real-time sweat monitoring is vital for athletes in order to reflect their physical conditions, quantify their exercise loads, and evaluate their training results. Therefore, a multi-modal sweat sensing system with a patch-relay-host topology was developed, which consisted of a wireless sensor patch, a wireless data relay, and a host controller. The wireless sensor patch can monitor the lactate, glucose, K+, and Na+ concentrations in real-time. The data is forwarded via a wireless data relay through Near Field Communication (NFC) and Bluetooth Low Energy (BLE) technology and it is finally available on the host controller. Meanwhile, existing enzyme sensors in sweat-based wearable sports monitoring systems have limited sensitivities. To improve their sensitivities, this paper proposes a dual enzyme sensing optimization strategy and demonstrates Laser-Induced Graphene (LIG)-based sweat sensors decorated with Single-Walled Carbon Nanotubes (SWCNT). Manufacturing an entire LIG array takes less than one minute and costs about 0.11 yuan in materials, making it suitable for mass production. The in vitro test result showed sensitivities of 0.53 µA/mM and 3.9 µA/mM for lactate and glucose sensing, and 32.5 mV/decade and 33.2 mV/decade for K+ and Na+ sensing, respectively. To demonstrate the ability to characterize personal physical fitness, an ex vivo sweat analysis test was also performed. Overall, the high-sensitivity lactate enzyme sensor based on SWCNT/LIG can meet the requirements of sweat-based wearable sports monitoring systems.

Serpentine-Inspired Strain Sensor with Predictable Cracks for Remote Bio-Mechanical Signal Monitoring.

Flexible strain sensors have attracted intense interest due to their application as intelligent wearable electronic devices. However, it is still a huge challenge to achieve a flexible sensor with simultaneous high sensitivity, excellent durability, and a wide sensing region. In this work, a crack-based strain sensor with a paired-serpentine conductive network is fabricated onto flexible film by screen printing. The innovative conductive network exhibits a controlled crack morphology during stretching, which endows the prepared sensor with outstanding sensing characteristics, including high sensitivity (gauge factor up to 2391.5), wide detection (rang up to 132%), low strain detection limit, a fast response time (about 40 ms), as well as excellent durability (more than 2000 stretching/releasing cycles). Benefiting from these excellent performances, full-range human body motions including subtle physiological signals and large motions are accurately detected by the prepared sensor. Furthermore, wearable electronic equipment integrated with a wireless transmitter and the prepared strain sensor shows great potential for remote motion monitoring and intelligent mobile diagnosis for humans. This work provides an effective strategy for the fabrication of novel strain sensors with highly comprehensive performance.

Easy-Scalable Flexible Sensors Made of Carbon Nanotube-Doped Polydimethylsiloxane: Analysis of Manufacturing Conditions and Proof of Concept.

Carbon nanotube (CNT) reinforced polydimethylsiloxane (PDMS) easy-scalable sensors for human motion monitoring are proposed. First, the analysis of the dispersion procedure of nanoparticles into the polymer matrix shows that the ultrasonication (US) technique provides a higher electrical sensitivity in comparison to three-roll milling (3RM) due to the higher homogeneity of the CNT distribution induced by the cavitation forces. Furthermore, the gauge factor (GF) calculated from tensile tests decreases with increasing the CNT content, as the interparticle distance between CNTs is reduced and, thus, the contribution of the tunnelling mechanisms diminishes. Therefore, the optimum conditions were set at 0.4 CNT wt.% dispersed by US procedure, providing a GF of approximately 37 for large strains. The electrical response under cycling load was tested at 2%, 5%, and 10% strain level, indicating a high robustness of the developed sensors. Thus, this strain sensor is in a privileged position with respect to the state-of-the-art, considering all the characteristics that this type of sensor must accomplish: high GF, high flexibility, high reproducibility, easy manufacturing, and friendly operation. Finally, a proof-of-concept of human motion monitoring by placing a sensor for elbow and finger movements is carried out. The electrical resistance was found to increase, as expected, with the bending angle and it is totally recovered after stretching, indicating that there is no prevalent damage and highlighting the huge robustness and applicability of the proposed materials as wearable sensors.

Evaluation of a Smart Knee Brace for Range of Motion and Velocity Monitoring during Rehabilitation Exercises and an Exergame.

Anterior cruciate ligament (ACL) injuries often require a lengthy duration of rehabilitation for patients to return to their prior level of function. Adherence to rehabilitation during this prolonged period can be subpar due to the treatment duration and poor adherence to home exercises. This work evaluates whether a smart instrumented knee brace system is capable of monitoring knee range of motion and velocity during a series of common knee rehabilitation exercises and an exergame. A total of 15 healthy participants completed a series of common knee rehabilitation exercises and played an exergame while wearing a smart instrumented knee brace. The range of motion (ROM) and velocity of the knee recorded by the knee brace was compared to a reference optoelectronic system. The results show good agreement between the knee brace system and the reference system for all exercises performed. Participants were able to quickly learn how to play the exergame and scored well within the game. The system investigated in this study has the potential to allow rehabilitation to occur outside of the clinic with the use of remote monitoring, and improve adherence and outcomes through the use of an exergame.

Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques.

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible.

An Effective Self-Powered Piezoelectric Sensor for Monitoring Basketball Skills.

Self-powered piezoelectric sensor can achieve real-time and harmless monitoring of motion processes without external power supply, which can be attached on body skin or joints to detect human motion and powered by mechanical energy. Here, a sensor for monitoring emergent motion is developed using the PVDF as active material and piezoelectric output as sensing signal. The multi-point control function enables the sensor to monitor the sequence of force order, angle change, and motion frequency of the "elbow lift, arm extension, and wrist compression" during shooting basketball. In addition, the sensor shows can simultaneously charge the capacitor to provide more power for intelligence, typically Bluetooth transmission. The sensor shows good performance in other field, such as rehabilitation monitoring and speech input systems. Therefore, the emerging application of flexible sensors have huge long-term prospects in sport big data collection and Internet of Things (IoT).

Interoperable Nanoparticle Sensor Capable of Strain and Vibration Measurement for Rotor Blade Monitoring.

Recent advances in nanomaterials technology create the new possibility to fabricate high performance sensors. However, there has been limitations in terms of multivariate measurable and interoperable sensors. In this study, we fabricated an interoperable silver nanoparticle sensor fabricated by an aerodynamically focused nanomaterial (AFN) printing system which is a direct printing technique for inorganic nanomaterials onto a flexible substrate. The printed sensor exhibited the maximum measurable frequency of 850 Hz, and a gauge factor of 290.62. Using a fabricated sensor, we evaluated the sensing performance and demonstrated the measurement independency of strain and vibration sensing. Furthermore, using the proposed signal separation algorithm based on the Kalman filter, strain and vibration were each measured in real time. Finally, we applied the printed sensor to quadrotor condition monitoring to predict the motion of a quadrotor.

Machine Embroidered Sensors for Limb Joint Movement-Monitoring Smart Clothing.

In this study, a strain gauge sensor based on a change of contact or network structure between conductive materials was implemented using the handle-machine embroidery technique, and the variables (embroidery shape, embroidery distance, embroidery size, and implementation location) affecting its performance were studied. As a result of Experiment I on the structure of embroidery suitable for joint motion monitoring, the embroidery distance, rather than the embroidery size, was found to have a significant effect on the electric resistance changes caused by elongation. Based on the results of Experiment I, two types of zigzag embroideries, four types of embroideries with few contact points, and two types of embroideries with more contact points (all with short distances (2.0)) were selected for Experiment II (the dummy motion experiment). As a result of the dummy motion experiment, it was found that the locations of the suitable embroidered sensors for joint motion monitoring was the HJP (Hinge Joint Position) in the 'types without a contact point' (zigzag) and the LHJP (Lower Hinge Joint Position) in the 'types with more contact points'. On the other hand, although there was no consistency among the 'types with few contact points', the resistance changes measured by the 2CP and 7CP embroidered sensors showed similar figures and patterns, and the HJP location was most suitable. The resistance changes measured by the 4CP and 6CP embroidered sensors exhibited no consistent patterns, but the LHJP locations were more suitable. These results indicate that the location of the HJP is suitable for measuring joint motion in the 'type without a contact point', and the location of the LHJP is suitable for measuring joint motion when the number of contact points exceeds a certain limit. Among them, the average resistance change of the 9CP sensor located at the LHJP was 40 Ω with the smallest standard deviation of less than 1, and it is thus considered to have the best performance among all the sensors.

SLIMM: Slice localization integrated MRI monitoring.

Functional MRI (fMRI) is extremely challenging to perform in subjects who move because subject motion disrupts blood oxygenation level dependent (BOLD) signal measurement. It has become common to use retrospective framewise motion detection and censoring in fMRI studies to eliminate artifacts arising from motion. Data censoring results in significant loss of data and statistical power unless the data acquisition is extended to acquire more data not corrupted by motion. Acquiring more data than is necessary leads to longer than necessary scan duration, which is more expensive and may lead to additional subject non-compliance. Therefore, it is well established that real-time prospective motion monitoring is crucial to ensure data quality and reduce imaging costs. In addition, real-time monitoring of motion allows for feedback to the operator and the subject during the acquisition, to enable intervention to reduce the subject motion. The most widely used form of motion monitoring for fMRI is based on volume-to-volume registration (VVR), which quantifies motion as the misalignment between subsequent volumes. However, motion is not constrained to occur only at the boundaries of volume acquisition, but instead may occur at any time. Consequently, each slice of an fMRI acquisition may be displaced by motion, and assessment of whole volume to volume motion may be insensitive to both intra-volume and inter-volume motion that is revealed by displacement of the slices. We developed the first slice-by-slice self-navigated motion monitoring system for fMRI by developing a real-time slice-to-volume registration (SVR) algorithm. Our real-time SVR algorithm, which is the core of the system, uses a local image patch-based matching criterion along with a Levenberg-Marquardt optimizer, all accelerated via symmetric multi-processing, with interleaved and simultaneous multi-slice acquisition schemes. Extensive experimental results on real motion data demonstrated that our fast motion monitoring system, named Slice Localization Integrated MRI Monitoring (SLIMM), provides more accurate motion measurements than a VVR based approach. Therefore, SLIMM offers improved online motion monitoring which is particularly important in fMRI for challenging patient populations. Real-time motion monitoring is crucial for online data quality control and assurance, for enabling feedback to the subject and the operator to act to mitigate motion, and in adaptive acquisition strategies that aim to ensure enough data of sufficient quality is acquired without acquiring excess data.

Cellulose II Aerogel-Based Triboelectric Nanogenerator.

Cellulose-based triboelectric nanogenerators (TENGs) have gained increasing attention. In this study, a novel method is demonstrated to synthesize cellulose-based aerogels and such aerogels are used to fabricate TENGs that can serve as mechanical energy harvesters and self-powered sensors. The cellulose II aerogel is fabricated via a dissolution-regeneration process in a green inorganic molten salt hydrate solvent (lithium bromide trihydrate), where. The as-fabricated cellulose II aerogel exhibits an interconnected open-pore 3D network structure, higher degree of flexibility, high porosity, and a high surface area of 221.3 m2 g-1. Given its architectural merits, the cellulose II aerogel-based TENG presents an excellent mechanical response sensitivity and high electrical output performance. By blending with other natural polysaccharides, i.e., chitosan and alginic acid, electron-donating and electron-withdrawing groups are introduced into the composite cellulose II aerogels, which significantly improves the triboelectric performance of the TENG. The cellulose II aerogel-based TENG is demonstrated to light up light-emitting diodes, charge commercial capacitors, power a calculator, and monitor human motions. This study demonstrates the facile fabrication of cellulose II aerogel and its application in TENG, which leads to a high-performance and eco-friendly energy harvesting and self-powered system.

A Highly Aligned Nanowire-Based Strain Sensor for Ultrasensitive Monitoring of Subtle Human Motion.

Achieving highly accurate responses to external stimuli during human motion is a considerable challenge for wearable devices. The present study leverages the intrinsically high surface-to-volume ratio as well as the mechanical robustness of nanostructures for obtaining highly-sensitive detection of motion. To do so, highly-aligned nanowires covering a large area were prepared by capillarity-based mechanism. The nanowires exhibit a strain sensor with excellent gauge factor (≈35.8), capable of high responses to various subtle external stimuli (≤200 µm deformation). The wearable strain sensor exhibits also a rapid response rate (≈230 ms), mechanical stability (1000 cycles) and reproducibility, low hysteresis (<8.1%), and low power consumption (<35 µW). Moreover, it achieves a gauge factor almost five times that of microwire-based sensors. The nanowire-based strain sensor can be used to monitor and discriminate subtle movements of fingers, wrist, and throat swallowing accurately, enabling such movements to be integrated further into a miniaturized analyzer to create a wearable motion monitoring system for mobile healthcare.

Large-Area Integrated Triboelectric Sensor Array for Wireless Static and Dynamic Pressure Detection and Mapping.

Large-area flexible pressure sensors are of paramount importance for various future applications, such as electronic skin, human-machine interfacing, and health-monitoring devices. Here, a self-powered and large-area integrated triboelectric sensor array (ITSA) based on coupling a triboelectric sensor array and an array chip of CD4066 through a traditional connection is reported. Enabled by a simple and cost-effective fabrication process, the size of the ITSA can be scaled up to 38 × 38 cm2 . In addition, unlike previously proposed triboelectric sensors arrays, which can only react to the dynamic interaction, this ITSA is able to detect static and dynamic pressure. Moreover, through integrating the ITSA with a signal processing circuit, a complete wireless sensing system is present. Diverse applications of the system are demonstrated in detail, including detecting pressure, identifying position, tracking trajectory, and recognizing the profile of external contact objects. Thus, the ITSA in this work opens a new route in the direction of large-area, self-powered, and wireless triboelectric sensing systems.

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis.

Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈 3 , 8 〉 and 〈 8 , 15 〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.

Self-Powered Non-Contact Triboelectric Rotation Sensor with Interdigitated Film.

Rotation detection is widely applied in industries. The current commonly used rotation detection system adopts a split structure, which requires stringent installation requirements and is difficult to miniaturize. This paper proposes a single-piece self-powered non-contact sensor with an interdigital sensitive layer to detect the rotation of objects. The electric field generated between a polyurethane (PU) film and a polytetrafluoroethylene (PTFE) film is utilized for perceiving the rotation. The surface of the PU film is subjected to wet etching with sulfuric acid to increase the surface area and charge density. Through finite element analysis and experimental testing, the effects of the areas of the sensitive films as well as the horizontal and vertical distances between them on the output voltage are analyzed. Tests are performed on adjustable-speed motors, human arms, and robotic arms. The results show that the sensor can detect the speed, the transient process of rotation, and the swing angle. The proposed rotation sensor has broad application prospects in the fields of mechanical automation, robotics, and Internet of Things (IoT).

The accuracy of a 2D video-based lifting monitor.

A widely used risk prediction tool, the revised NIOSH lifting equation (RNLE), provides the recommended weight limit (RWL), but is limited by analyst subjectivity, experience, and resources. This paper describes a robust, non-intrusive, straightforward approach to automatically extract spatial and temporal factors necessary for the RNLE using a single video camera in the sagittal plane. The participant's silhouette is segmented by motion information and the novel use of a ghosting effect provides accurate detection of lifting instances, and hand and feet location prediction. Laboratory tests using 6 participants, each performing 36 lifts, showed that a nominal 640 pixel × 480 pixel 2D video, in comparison to 3D motion capture, provided RWL estimations within 0.2 kg (SD = 1.0 kg). The linear regression between the video and 3D tracking RWL was R2 = 0.96 (slope = 1.0, intercept = 0.2 kg). Since low definition video was used in order to synchronise with motion capture, better performance is anticipated using high definition video. Practitioner's summary: An algorithm for automatically calculating the revised NIOSH lifting equation using a single video camera was evaluated in comparison to laboratory 3D motion capture. The results indicate that this method has suitable accuracy for practical use and may be, particularly, useful when multiple lifts are evaluated. Abbreviations: 2D: Two-dimensional; 3D: Three-dimensional; ACGIH: American Conference of Governmental Industrial Hygienists; AM: asymmetric multiplier; BOL: beginning of lift; CM: coupling multiplier; DM: distance multiplier; EOL: end of lift; FIRWL: frequency independent recommended weight limit; FM: frequency multiplier; H: horizontal distance; HM: horizontal multiplier; IMU: inertial measurement unit; ISO: International Organization for Standardization; LC: load constant; NIOSH: National Institute for Occupational Safety and Health; RGB: red, green, blue; RGB-D: red, green, blue - depth; RNLE: revised NIOSH lifting equation; RWL: recommended weight limit; SD: standard deviation; TLV: threshold limit value; VM: vertical multiplier; V: vertical distance.

Low-cost Kinect Version 2 imaging system for breath hold monitoring and gating: Proof of concept study for breast cancer VMAT radiotherapy.

Voluntary inspiration breath hold (VIBH) for left breast cancer patients has been shown to be a safe and effective method of reducing radiation dose to the heart. Currently, VIBH protocol compliance is monitored visually. In this work, we establish whether it is possible to gate the delivery of radiation from an Elekta linac using the Microsoft Kinect version 2 (Kinect v2) depth sensor to measure a patient breathing signal. This would allow contactless monitoring during VMAT treatment, as an alternative to equipment-assisted methods such as active breathing control (ABC). Breathing traces were acquired from six left breast radiotherapy patients during VIBH. We developed a gating interface to an Elekta linac, using the depth signal from a Kinect v2 to control radiation delivery to a programmable motion platform following patient breathing patterns. Radiation dose to a moving phantom with gating was verified using point dose measurements and a Delta4 verification phantom. 60 breathing traces were obtained with an acquisition success rate of 100%. Point dose measurements for gated deliveries to a moving phantom agreed to within 0.5% of ungated delivery to a static phantom using both a conventional and VMAT treatment plan. Dose measurements with the verification phantom showed that there was a median dose difference of better than 0.5% and a mean (3% 3 mm) gamma index of 92.6% for gated deliveries when using static phantom data as a reference. It is possible to use a Kinect v2 device to monitor voluntary breath hold protocol compliance in a cohort of left breast radiotherapy patients. Furthermore, it is possible to use the signal from a Kinect v2 to gate an Elekta linac to deliver radiation only during the peak inhale VIBH phase.

Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms.

Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research involved simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic application. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable product without unnecessary heavy and uncomfortable electronic devices.