Search Results (232)

Dopamine (DA) is a neurotransmitter responsible for important functions in mammals’ bodies, including mood, movement and motivation. High or low dopamine levels are associated mainly with mental illnesses such as schizophrenia and depression. Therefore, contributing to the development of electrochemical devices to precisely determine the DA levels in urine samples, a simple and low-cost sensor is proposed in this work. The proposed sensor design is based on crosslinked chitosan films combining carbon black (CB) and deep eutectic solvents (DESs), incorporated onto the surface of a glassy carbon electrode (GCE). Fourier Transform Infrared Spectroscopy (FT-IR) was applied to characterize the produced DESs and their precursors, while the films were characterized by scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The sensor modified with CB and DES–ethaline (DES (ETHA)-CB/GCE) showed a significantly enhanced analytical signal for DA using differential pulse voltammetry under the optimized working conditions. Moreover, a better heterogeneous electron transfer rate constant (k⁰) was obtained, about 45 times higher than that of the bare GCE. The proposed sensor achieved a linear response range of 0.498 to 26.8 µmol L⁻¹ and limits of detection and quantification of 80.7 and 269 nmol L⁻¹, respectively. Moreover, the sensor was successfully applied in the quantification of DA in the synthetic urine samples, with recovery results close to 100%. Furthermore, the sensor presented good precision, as shown from the repeatability tests. The presented method to electrochemically detect DA has proven to be efficient and simple compared to the conventional methods commonly reported. Full article

In the Direct Energy Deposition (DED) process, the deposited material experiences intricate thermo-mechanical processes. Subsequent thermal cycling can trigger Dynamic Recrystallization (DRX) under suitable conditions, with specific strain and temperature parameters facilitating grain refinement and homogenization. While prior research has examined the impact of thermal cycling in continuous wave (CW) lasers on DRX in 316 L stainless steel deposits, this study delves into the effects of pulsed wave (PW) laser thermal cycling on DRX. Here, the thermo-mechanical response to PW cyclic thermal loading is empirically assessed, and the evolution of microstructure, grain morphology, geometric dislocation density (GND), and misorientation map during PW DED of 316 L stainless steel is scrutinized. Findings reveal that DRX is activated between the 8th and 44th thermal cycles, with temperatures fluctuating in the range of 680 K–750 K–640 K and grains evolving within a 5.6%–6.2%–5.2% strain range. After 90 thermal cycles, the grain microstructure undergoes significant alteration. Throughout the thermal cycling, dynamic recovery (DRV) occurs, marked by sub-grain formation and low-angle grain boundaries (LAGBs). Continuous dynamic recrystallization (CDRX) accompanies discontinuous dynamic recrystallization (DDRX), with LAGBs progressively converting into high-angle grain boundaries (HAGBs). Elevated temperatures and accumulated strain drive dislocation movement and entanglement, augmenting GND. The study also probes the influence of frequency and duty cycle on grain microstructure, finding that low pulse frequency spurs CDRX, high pulse frequency favors DRV, and the duty cycle has minimal impact on grain microstructure under PW cyclic thermal load. Full article

The Upper Carboniferous strata in the eastern Qaidam Basin, comprising several hundred meters of thick, mixed clastic–carbonate successions that have been little reported or explained, provide an excellent geological record of paleoenvironmental and paleo-sea level changes during the Late Carboniferous icehouse period. This tropical carbonate–clastic system offers critical constraints for correlating equatorial sea level responses with high-latitude glacial cycles during the Late Paleozoic Ice Age. Based on detailed outcrop observations and interpretations, five facies assemblages, including fluvial channel, tide-dominated estuary, wave-dominated shoreface, tide-influenced delta, and carbonate-dominated marine, have been identified and organized into cyclical stacking patterns. Correspondingly, four third-order sequences were recognized, each composed of lowstand, transgressive, and highstand system tracts (LST, TST, and HST). LST is generally dominated by fluvial channels as a result of river juvenation when the sea level falls. The TST is characterized by tide-dominated estuaries, followed by retrogradational, carbonated-dominated marine deposits formed during a period of sea level rise. The HST is dominated by aggradational marine deposits, wave-dominated shoreface environments, or tide-influenced deltas, caused by subsequent sea level falls and increased debris supply. The sequence stratigraphic evolution and geochemical records, based on carbon and oxygen isotopes and trace elements, suggest that during the Late Carboniferous period, the eastern Qaidam Basin experienced at least four significant sea level fluctuation events, and an overall long-term sea level rise. These were primarily driven by the Gondwana glacio-eustasy and regionally ascribed to the Paleo-Tethys Ocean expansion induced by the late Hercynian movement. Assessing the history of glacio-eustasy-driven sea level changes in the eastern Qaidam Basin is useful for predicting the distribution and evolution of mixed cyclic succession in and around the Tibetan Plateau. Full article

Articular cartilage (AC) plays an important role in the biomechanics of synovial joints. Its task is to enable smooth movement and transfer of mechanical loads with minimised friction. AC is characterised by unique mechanical properties resulting from its complex structure, in which the dominant components are type II collagen, proteoglycans and water. Healthy articular cartilage shows elasticity in compression, viscoelastic properties, and the ability to relax stresses under the influence of cyclic loads. In response to different loading modes, it shows anisotropic and non-uniform behaviour, which translates into its cushioning and protective function for the subchondral bone. Significant changes occur in the structure and mechanical properties of cartilage with age as a result of mechanical overload or degenerative diseases, such as osteoarthritis. This results in a deterioration of the cushioning and mechanical function, which leads to progressive degradation of joint tissues. Understanding the mechanical properties of AC is crucial for developing effective diagnostic methods. Analysis of changes in mechanical properties contributes to the early detection of pathological changes. The aim of this paper is to review the current state of knowledge regarding the structure and biomechanical properties of articular cartilage, and to analyse conventional and alternative diagnostic methods in the context of their suitability for assessing the state of AC, particularly in the early stages of degenerative processes. Full article

Monitoring posture and movement accurately and efficiently is essential for both physical therapy and athletic training evaluation and interventions. Motion Tape (MT), a self-adhesive wearable skin-strain sensor made of piezoresistive graphene nanosheets (GNS), has demonstrated promise in capturing low back posture and movements. However, to address some of its limitations, this work explores alternative materials by replacing GNS with multi-walled carbon nanotubes (MWCNT). This study aimed to characterize the electromechanical properties of MWCNT-based MT. Cyclic load tests for different peak tensile strains ranging from 1% to 10% were performed on MWCNT-MT made with an aqueous ink of 2% MWCNT. Additional tests to examine load rate sensitivity and fatigue were also conducted. After characterizing the properties of MWCNT-MT, a human subject study with 10 participants was designed to test its ability to capture different postures and movements. Sets of six sensors were made from each material (GNS and MWCNT) and applied in pairs at three levels along each side of the lumbar spine. To record movement of the lower back, all participants performed forward flexion, left and right bending, and left and right rotation movements. The results showed that MWCNT-MT exceeded GNS-MT with respect to consistency of signal stability even when strain limits were surpassed. In addition, both types of MT could assess lower back movements. Full article

Incremental capacity analysis (ICA), where incremental charge (Q) movements associated with changes in potential are tracked, and cyclic voltammetry (CV), where current response to a linear voltage sweep is recorded, are used to investigate the properties of electrochemical systems. Electrochemical impedance spectroscopy (EIS), on the other hand, is a powerful, non-destructive technique that can be used to determine small-signal AC impedance over a wide frequency range. It is frequently used to design battery equivalent-circuit models. This manuscript explores the relationships between ICA, CV and EIS and demonstrates how sweep rate in CV is related to charging (C) rate in ICA. In addition, it shows the connection between observations linked to rate of charge movement in CV and ICA and intermittent, irregular behavior seen in EIS when performed on a battery. It also explains the use of an additional DC stimulus during EIS to ensure reliability of battery impedance data and to facilitate equivalent-circuit modeling, and suggests a method for obtaining data analogous to CV from a whole battery without risking its destruction. Full article

The rapidly evolving field of functional yarns has garnered substantial research attention due to their exceptional potential in enabling next-generation electronic textiles for wearable health monitoring, human–machine interfaces, and soft robotics. Despite notable advancements, the development of yarn-based strain sensors that simultaneously achieve high flexibility, stretchability, superior comfort, extended operational stability, and exceptional electrical performance remains a critical challenge, hindered by material limitations and structural design constraints. Here, we present a bioinspired, hierarchically structured core-sheath yarn sensor (CSSYS) engineered through an efficient dip-coating process, which synergistically integrates the two-dimensional conductive MXene nanosheets and one-dimensional silver nanowires (AgNWs). Furthermore, the sensor is encapsulated using a yarn-based protective layer, which not only preserves its inherent flexibility and wearability but also effectively mitigates oxidative degradation of the sensitive materials, thereby significantly enhancing long-term durability. Drawing inspiration from the natural architecture of plant stems—where the inner core provides structural integrity while a flexible outer sheath ensures adaptive protection—the CSSYS exhibits outstanding mechanical and electrical performance, including an ultralow strain detection limit (0.05%), an ultrahigh gauge factor (up to 744.45), rapid response kinetics (80 ms), a broad sensing range (0–230% strain), and exceptional cyclic stability (>20,000 cycles). These remarkable characteristics enable the CSSYS to precisely capture a broad spectrum of physiological signals, ranging from subtle arterial pulsations and respiratory rhythms to large-scale joint movements, demonstrating its immense potential for next-generation wearable health monitoring systems. Full article

Prolonged and high-intensity human–robot interaction can cause muscle fatigue. This fatigue leads to changes in both the time domain and frequency domain of the surface electromyography (sEMG) signals, which are closely related to human body movements. Consequently, these changes affect the accuracy and stability of using sEMG signals to recognize human body movements. Although numerous studies have confirmed that the median frequency of sEMG signals decreases as the degree of muscle fatigue increases—and this has been used for classifying fatigue and non-fatigue states— there is still a lack of quantitative characterization of the degree of muscle fatigue. Therefore, this paper proposes a method for quantitatively characterizing the degree of muscle fatigue during periodic exercise, based on the high-frequency components obtained through ensemble empirical mode decomposition (EEMD). Firstly, the sEMG signals of the estimated individuals are subjected to EEMD to obtain the high-frequency components, and the short-time Fourier transform is used to calculate the median frequency (MF) of these high-frequency components. Secondly, the obtained median frequencies are linearly fitted, and based on this, a standardized median frequency distribution range (SMFDR) of sEMG signals under muscle fatigue is established. Finally, a muscle fatigue estimator is proposed to achieve the quantification of the degree of muscle fatigue based on the SMFDR. Experimental validation across five subjects demonstrated that this method effectively quantifies cyclical muscle fatigue, with results revealing the methodology exhibits superiority in identifying multiple fatigue states during cyclical movements under consistent loading conditions. Full article

A vole foraging-inspired dynamic path-planning method considering slippery road conditions is proposed for wheeled humanoid robots. Glazed and oily roads create a high risk of slipping for wheeled humanoid robots and hinder the realization of high-speed movement. But in a dynamic environment, road conditions such as material, texture, and attachments vary uncertainly in both space and time, and cannot be processed as quickly and easily as moving obstacles. Inspired by the process of voles searching for food, to address this challenge, a slip-risk-assessment method based on time–space decoupling is designed and integrated into a grid-based environmental model. On this basis, the dynamic path-planning model is constructed by combining the cost functions and constraints based on the slip-risk information. A two-level non-periodic cyclical dynamic planning mechanism is proposed based on conditional triggering. It adaptively and cyclically calls the global planning algorithm and the local re-planning algorithm according to the characteristics of environmental changes to autonomously avoid high-slip-risk areas and moving obstacles in real time. The experimental results show the effectiveness and practicality of the proposed planning method. Full article

Flexible wearable strain sensors demonstrate promising application prospects in health monitoring, human-machine interaction, motion tracking, and the detection of human physiological signals. Although laser-induced graphene (LIG) materials have been extensively utilized in these scenarios, traditional types of LIG sensors are constrained by intrinsic limitations, including discontinuous conductive networks and electromechanical responsive hysteresis. These limitations hinder their applications in micro-strain detection scenarios. Consequently, enhancing the performance of LIG-based sensors has become a crucial priority. To address this challenge, we developed a novel MXene/LIG composite featuring optimized conductive networks and interfacial coupling effects through the systematic enhancement of LIG. The flexible strain sensor fabricated using this composite exhibits exceptional performance, including an ultra-low sheet resistance of 14.1 Ω, a high sensitivity of 20.7, a micro-strain detection limit of 0.05%, and a rapid response time of approximately 65 ms. These improvements significantly enhance electromechanical responsiveness and strain detection sensitivity. Furthermore, the sensor exhibits remarkable stability under varying tensile strains, particularly showing outstanding repeatability across 2500 cyclic tests. Notably, when applied to the pilot health monitoring scenarios, the MXene/LIG-based sensor demonstrates robust capability in detecting body movement signals such as micro-expressions and joint movements. This establishes a novel and highly effective technological solution for the real-time monitoring of pilots’ motion states during operational scenarios. Full article

Activity recognition and localization in outdoor environments involve identifying and tracking human movements using sensor data, computer vision, or deep learning techniques. This process is crucial for applications such as smart surveillance, autonomous systems, healthcare monitoring, and human–computer interaction. However, several challenges arise in outdoor settings, including varying lighting conditions, occlusions caused by obstacles, environmental noise, and the complexity of differentiating between similar activities. This study presents a hybrid deep learning approach that integrates human activity recognition and localization in outdoor environments using Wi-Fi signal data. The study focuses on applying the hybrid long short-term memory–bi-gated recurrent unit (LSTM-BIGRU) architecture, designed to enhance the accuracy of activity recognition and location estimation. Moreover, experiments were conducted using a real-world dataset collected with the PicoScene Wi-Fi sensing device, which captures both magnitude and phase information. The results demonstrated a significant improvement in accuracy for both activity recognition and localization tasks. To mitigate data scarcity, this study utilized the conditional tabular generative adversarial network (CTGAN) to generate synthetic channel state information (CSI) data. Additionally, carrier frequency offset (CFO) and cyclic shift delay (CSD) preprocessing techniques were implemented to mitigate phase fluctuations. The experiments were conducted in a line-of-sight (LoS) outdoor environment, where CSI data were collected using the PicoScene Wi-Fi sensor platform across four different activities at outdoor locations. Finally, a comparative analysis of the experimental results highlights the superior performance of the proposed hybrid LSTM-BIGRU model, achieving 99.81% and 98.93% accuracy for activity recognition and location prediction, respectively. Full article

The CRTS III (China Railway Track System Type III)-girder is susceptible to fatigue damage under high-frequency train loads. However, existing research lacks sufficient focus on the CRTS III-girder and the transverse wheel–rail forces encountered during train operation. To better replicate the stress conditions experienced by high-speed railway track systems, a 1:4 scale CRTS III-girder was fabricated following the principle of mid-span concrete stress equivalence. Subsequently, 9 million transverse and vertical fatigue load cycles were applied to the specimen, leading to the following conclusions: First, no visible cracks appeared on the CRTS III-girder surface during the experiment, indicating strong fatigue resistance under train loads. Second, the box girder primarily exhibited a linear elastic response with minimal stiffness variation. Meanwhile, the upper ballastless track structure experienced a highly complex stress state, with significant variations observed across different layers under cyclic fatigue loading. Third, under fatigue loading, the longitudinal strain of the mid-span track slab and the self-compacting concrete (SCC) layer exhibited an overall decreasing trend, with reduction rates of −66% and −57.9%, respectively. Conversely, the longitudinal strain of the base plate and the top and bottom of the box girder gradually increased, with respective increases of 38.6%, 10.4%, and 12.2%. Finally, the connection between the base plate and the box girder remained robust, showing no relative slippage in the transverse, longitudinal, or vertical directions. The sliding layer exhibited stable performance in the longitudinal direction, with no significant degradation observed under cyclic fatigue loading. However, with increasing load cycles, the transverse relative displacement of the sliding layer gradually increased, reaching a maximum of 0.1 mm. This displacement, in turn, contributed to transverse rail movement, potentially affecting driving safety. Full article

Diabetic Foot Ulcers (DFUs) are a significant health and economic burden, potentially leading to limb amputation, with a severe impact on a person’s quality of life. During active movements like gait, the monitoring of shear has been suggested as an important factor for effective prevention of DFUs. It is proposed that, in textiles, strain can be measured as a proxy for shear stress at the skin. This paper presents the conceptualisation and development of a novel strain-sensing approach that can be unobtrusively integrated within sock textiles and worn within the shoe. Working with close clinical and patient engagement, a sensor specification was identified, and 12 load-sensing approaches for the prevention of DFU were evaluated. A lead concept using a conductive adhesive was selected for further development. The method was developed using a Lycra sample, before being translated onto a knitted ‘sock’ substrate. The resultant strain sensor can be integrated within mass-produced textiles fabricated using industrial knitting machines. A case-study was used to demonstrate a proof-of-concept version of the strain sensor, which changes resistance with applied mechanical strain. A range of static and dynamic laboratory testing was used to assess the sensor’s performance, which demonstrated a resolution of 0.013 $\mathrm{\Omega }$ across a range of 0–430 $\mathrm{\Omega }$ and a range of interest of 0–20 $\mathrm{\Omega }$. In cyclic testing, the sensor exhibited a cyclic strain threshold of 6% and a sensitivity gradient of 0.3 ± 0.02, with a low dynamic drift of 0.039 to 0.045% of the total range. Overall, this work demonstrates a viable textile-based strain sensor capable of integration within worn knitted structures. It provides a promising first step towards developing a sock-based strain sensor for the prevention of DFU formation. Full article

Wearable inertial measurement units (IMUs) have been widely used in human movement analysis outside the laboratory. However, the IMU-based orientation estimation remains challenging, particularly in scenarios involving relatively fast movements. Increased sampling rate has the potential to improve accuracy, but it also increases power consumption and computational complexity. The relationship between sampling frequencies and accuracies remains unclear. We thus investigated the specific influence of IMU sampling frequency on orientation estimation across a spectrum of movement speeds and recommended sufficient sampling rates. Seventeen healthy subjects wore IMUs on their thigh, shank, and foot and performed walking (1.2 m/s) and running (2.2 m/s) trials on a treadmill, and a motion testbed with an IMU was used to mimic high-frequency cyclic human movements up to 3.0 Hz. Four widely used IMU sensor fusion algorithms computed orientations at 10, 25, 50, 100, 200, 400, 800, and 1600 Hz and were compared with marker-based optical motion capture (OMC) orientations to determine accuracy. Results suggest that the sufficient IMU sampling rate for walking is 100 Hz, running is 200 Hz, and high-speed cyclic movements is 400 Hz. The accelerometer sampling rate is less important than the gyroscope sampling rate. Further, accelerometer sampling rates exceeding 100 Hz even resulted in decreased accuracy because excessive orientation updates using distorted accelerations and angular velocity introduced more error than merely using angular velocity. These findings could serve as a foundation to inform wearable IMU development or selection across a spectrum of human gait movement speeds. Full article

Background/Objectives: The GO-TRYKE^® Kid (GTK^®) is an arm- and leg-powered tricycle which, in addition to promoting strength, endurance, and coordination, aims to reactivate the central pattern generators of the spine for locomotion through cyclical movements. The present study investigated the effects of GTK^® training on walking ability, GTK^® riding performance, and health-related quality of life in children with walking disabilities. Methods: Nine children trained with the device twice a week for nine weeks. Short- and long-term effects on walking ability were measured using the timed up and go test (TUG) and the two-minute walk test (2MWT). GTK^® riding performance and health-related quality of life were compared before and after the intervention period. Results: While no long-term effect on walking was found, a significant short-term effect on functional walking ability was observed (p = 0.009). GTK^® riding performance improved significantly over the training period (p = 0.004). There were no significant changes in health-related quality of life. Conclusions: GTK^® enables children with walking disabilities to participate in cycling as part of play and sport. Further research is required to investigate its functional and participatory effects, as there is significant potential to improve physical activity and overall well-being in this population. Full article