Search Results (578)

An assessment of protocol reliability is an essential step prior to human subject testing for injury prevention. The purpose of this study was to examine the inter-rater and intra-rater reliability of a portable cervical range of motion and isometric strength protocol designed for special forces combat soldiers who are at risk for cervical spine pain and injury due to exposure to head-supported mass. Eight individuals were tested three times to assess reliability, the standard error of the measurement (SEM), and the minimal detectable change across six range of motion measures and six strength measures of the cervical spine. One tester tested all participants twice for intra-rater reliability, and a second tester assessed the participants to examine inter-tester reliability. All reliability measures demonstrated good to excellent reliability (ICC = 0.70–0.96 (isometric strength); ICC = 0.85–0.94 (range of motion)). All SEM scores were 12% or lower for all reliability measures. The findings of this study demonstrate that the protocol developed for a longitudinal multi-site study is reliable and appropriate to implement for injury prevention in military personnel. Full article

Recognizing sleep posture is crucial for the monitoring of people with sleeping disorders. Existing contact-based systems might interfere with sleeping, while camera-based systems may raise privacy concerns. In contrast, radar-based sensors offer a promising solution with high penetration ability and the capability to detect vital bio-signals. This study propose a deep learning method for human sleep pose recognition from signals acquired from single-antenna Frequency-Modulated Continuous Wave (FMCW) radar device. To capture both frequency features and sequential features, we introduce ResTCN, an effective architecture combining Residual blocks and Temporal Convolution Network (TCN) to recognize different sleeping postures, from augmented statistical motion features of the radar time series. We rigorously evaluated our method with an experimentally acquired data set which contains sleeping radar sequences from 16 volunteers. We report a classification accuracy of 82.74% on average, which outperforms the state-of-the-art methods. Full article

Human gait recognition (HGR) leverages unique gait patterns to identify individuals, but the effectiveness of this technique can be hindered due to various factors such as carrying conditions, foot shadows, clothing variations, and changes in viewing angles. Traditional silhouette-based systems often neglect the critical role of instantaneous gait motion, which is essential for distinguishing individuals with similar features. We introduce the ”Enhanced Gait Feature Extraction Framework (GaitSTAR)”, a novel method that incorporates dynamic feature weighting through the discriminant analysis of temporal and spatial features within a channel-wise architecture. Key innovations in GaitSTAR include dynamic stride flow representation (DSFR) to address silhouette distortion, a transformer-based feature set transformation (FST) for integrating image-level features into set-level features, and dynamic feature reweighting (DFR) for capturing long-range interactions. DFR enhances contextual understanding and improves detection accuracy by computing attention distributions across channel dimensions. Empirical evaluations show that GaitSTAR achieves impressive accuracies of 98.5%, 98.0%, and 92.7% under NM, BG, and CL conditions, respectively, with the CASIA-B dataset; 67.3% with the CASIA-C dataset; and 54.21% with the Gait3D dataset. Despite its complexity, GaitSTAR demonstrates a favorable balance between accuracy and computational efficiency, making it a powerful tool for biometric identification based on gait patterns. Full article

Photoelectric dual-mode sensors, which respond to strain signal through photoelectric dual-signals, hold great promise as wearable sensors in human motion monitoring. In this work, a photoelectric dual-mode sensor based on photonic crystals hydrogel was developed for human joint motion detection. The optical signal of the sensor originated from the structural color of photonic crystals, which was achieved by tuning the polymethyl methacrylate (PMMA) microspheres diameter. The reflective peak of the sensor, based on 250 nm PMMA PCs, shifted from 623 nm to 492 nm with 100% strain. Graphene was employed to enhance the electrical signal of the sensor, resulting in a conductivity increase from 9.33 × 10⁻⁴ S/m to 2 × 10⁻³ S/m with an increase in graphene from 0 to 8 mg·mL⁻¹. Concurrently, the resistance of the hydrogel with 8 mg·mL⁻¹ graphene increased from 160 kΩ to 485 kΩ with a gauge factor (GF) = 0.02 under 100% strain, while maintaining a good cyclic stability. The results of the sensing and monitoring of finger joint bending revealed a significant shift in the reflective peak of the photoelectric dual-mode sensor from 624 nm to 526 nm. Additionally, its resistance change rate was measured at 1.72 with a 90° bending angle. These findings suggest that the photoelectric dual-mode sensor had the capability to detect the strain signal with photoelectric dual-mode signals, and indicates its great potential for the sensing and monitoring of joint motion. Full article

The nowcasting of strong convective precipitation is highly demanded and presents significant challenges, as it offers meteorological services to diverse socio-economic sectors to prevent catastrophic weather events accompanied by strong convective precipitation from causing substantial economic losses and human casualties. With the accumulation of dual-polarization radar data, deep learning models based on data have been widely applied in the nowcasting of precipitation. Deep learning models exhibit certain limitations in the nowcasting approach: The evolutionary method is prone to accumulate errors throughout the iterative process (where multiple autoregressive models generate future motion fields and intensity residuals and then implicitly iterate to yield predictions), and the “regression to average” issue of autoregressive model leads to the “blurring” phenomenon. The evolution method’s generator is a two-stage model: In the initial stage, the generator employs the evolution method to generate the provisional forecasted data; in the subsequent stage, the generator reprocesses the provisional forecasted data. Although the evolution method’s generator is a generative adversarial network, the adversarial strategy adopted by this model ignores the significance of temporary prediction data. Therefore, this study proposes an Adversarial Autoregressive Network (AANet): Firstly, the forecasted data are generated via the two-stage generators (where FURENet directly produces the provisional forecasted data, and the Semantic Synthesis Model reprocesses the provisional forecasted data); Subsequently, structural similarity loss (SSIM loss) is utilized to mitigate the influence of the “regression to average” issue; Finally, the two-stage adversarial (Tadv) strategy is adopted to assist the two-stage generators to generate more realistic and highly similar generated data. It has been experimentally verified that AANet outperforms NowcastNet in the nowcasting of the next 1 h, with a reduction of 0.0763 in normalized error (NE), 0.377 in root mean square error (RMSE), and 4.2% in false alarm rate (FAR), as well as an enhancement of 1.45 in peak signal-to-noise ratio (PSNR), 0.0208 in SSIM, 5.78% in critical success index (CSI), 6.25% in probability of detection (POD), and 5.7% in F1. Full article

In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of movement. Radar data are processed through principal component analysis (PCA) to identify unique detection signatures. We refine these features using k-means clustering and employ them to train hidden Markov models (HMMs). These models are tailored to distinguish between distinct movements, specifically focusing on differentiating sitting from falling motions. Our experimental findings reveal that integrating video-derived and radar-derived features significantly improves the accuracy of motion classification. Specifically, the combined approach enhanced the precision of detecting sitting motions by over 10% compared to using single-modality data. This integrated method not only boosts classification accuracy but also extends the practical applicability of motion detection systems in diverse real-world scenarios, such as healthcare monitoring and emergency response systems. Full article

Flexible electronics have revolutionized the field by overcoming the rigid limitations of traditional devices, offering superior flexibility and adaptability. Conductive ink performance is crucial, directly impacting the stability of flexible electronics. While metal filler-based inks exhibit excellent conductivity, they often lack mechanical stability. To address this challenge, we present a novel conductive ink utilizing a ternary composite filler system: liquid metal and two micron-sized silver morphologies (particles and flakes). We systematically investigated the influence of filler type, mass ratio, and sintering process parameters on the composite ink’s conductivity and mechanical stability. Our results demonstrate that flexible wires fabricated with the liquid metal/micron silver particle/micron silver flake composite filler exhibit remarkable conductivity and exceptional bending stability. Interestingly, increasing the liquid metal content results in a trade-off, compromising conductivity while enhancing mechanical performance. After enduring 5000 bending cycles, the resistance change in wires formulated with a 4:1 mass ratio of micron silver particles to flakes is only half that of wires with a 1:1 ratio. This study further investigates the mechanism governing resistance variations during flexible wire bending. Additionally, we observed a positive correlation between sintering temperature and pressure with the conductivity of flexible wires. The significance of the sintering parameters on conductivity follows a descending order: sintering temperature, sintering pressure, and sintering time. Finally, we demonstrate the practical application of this technology by integrating the composite ink-based flexible wires with conductive polymer-based strain sensors. This combination successfully achieved the detection of human movements, including finger and wrist bending. Full article

Microstructures have been proved as crucial factors for the sensing performance of flexible pressure sensors. In this study, polypyrrole (PPy)/sunflower pollen (SFP) (P/SFP) was prepared via the in situ growth of PPy on the surface of degreased SFP with a sea urchin-like microstructure; then, these P/SFP microspheres were sprayed onto a flat silk cocoon (FSC) to prepare a sensing layer P/SFP-FSC. PPy-FSC (P-FSC) was prepared as an electrode layer through the in situ polymerization of PPy on the FSC surface. The sensing layer P/SFP-FSC was placed between two P-FSC electrode layers to assemble a P/SFP-FSC pressure sensor together with a fork finger electrode. With 6 mg/cm² of optimized sprayed P/SFP microspheres, the prepared flexible pressure sensor has a sensitivity of up to 0.128 KPa⁻¹ in the range of 0–13.18 KPa and up to 0.13 KPa⁻¹ in the range of 13.18–30.65 KPa, a fast response/recovery time (90 ms/80 ms), and a minimum detection limit as low as 40 Pa. This fabricated flexible P/SFP-FSC sensor can monitor human motion and can also be used for the encrypted transmission of important information via Morse code. In conclusion, the developed flexible P/SFP-FSC pressure sensor based on microstructure modification in this study shows good application prospects in the field of human–computer interaction and wearable electronic devices. Full article

3D concrete printing (3DCP) requires precise adjustments to parameters to ensure accurate and high-quality prints. However, despite technological advancements, manual intervention still plays a prominent role in this process, leading to errors and inconsistencies in the final printed part. To address this issue, machine learning vision models have been developed and utilized to analyze captured images and videos of the printing process, detecting defects and deviations. The data collected enable automatic adjustments to print settings, improving quality without the need for human intervention. This work first examines various techniques for real-time and offline corrections. It then introduces a specialized computer vision setup designed for real-time control in robotic 3DCP. Our main focus is on a specific aspect of machine learning (ML) within this system, called speed control, which regulates layer width by adjusting the robot motion speed or material flow rate. The proposed framework consists of three main elements: (1) a data acquisition and processing pipeline for extracting printing parameters and constructing a synthetic training dataset, (2) a real-time ML model for parameter optimization, and (3) a depth camera installed on a customized 3D-printed rotary mechanism for close-range monitoring of the printed layer. Full article

The rapid progress of flexible electronics has met the growing need for detecting human movement information in exoskeleton auxiliary equipment. This study provides a review of recent advancements in the design and fabrication of flexible electronics used for human motion detection. Firstly, a comprehensive introduction is provided on various self-powered wearable flexible sensors employed in detecting human movement information. Subsequently, the algorithms utilized to provide feedback on human movement are presented, followed by a thorough discussion of their methods and effectiveness. Finally, the review concludes with perspectives on the current challenges and opportunities in implementing self-powered wearable flexible sensors in exoskeleton technology. Full article

Automatic power hacksaws are made for cutting different materials into different sizes. The main aim of making this machine is to save human effort, space, and time when cutting various materials to increase the amount of work that can be carried out. A special motor turns the hacksaw blade, and the circular motion of the motor is changed into a back-and-forth motion by a crank and a link connected to the saw. Engineers designed this machine using AUTO CAD 23.0 and it can cut materials that are between 10 mm and 14 mm thick. There are sensors on the machine that can detect when the cutting is finished, and a coolant is used during the cutting process. Full article

Inductive contact force sensors, known for their high precision and anti-interference capabilities, hold significant potential applications in fields such as wearable and medical monitoring devices. Most of the current research on inductive contact force sensors employed novel nanomaterials as sensitive elements to enhance their sensitivity and other performance characteristics. However, sensors developed through such methods typically involve complex preparation processes, high costs, and difficulty in biodegradation, which limit their further development. This article introduces a new flexible inductive contact force sensor using paper as a sensitive element. Paper inherently possesses micro- and nanostructures on its surface and interior, enabling it to sensitively convert changes in contact force into changes in displacement, making it suitable for use as the sensor’s sensitive element. Additionally, the advantages of paper also include its great flexibility, low cost, wide availability, and biodegradability. Performance testing on this flexible sensor showed good repeatability, hysteresis, sensitivity, and consistency. When used in experiments for monitoring human motion and respiration, this sensor also exhibited great detection performance. The proposed inductive paper-based flexible contact force sensor, with its simple structure, easy manufacturing process, cost-effectiveness, eco-friendliness, and good sensing performance, provides new insights into research for contact force sensors. Full article

Detecting slow-moving landslides is a crucial task for mitigating potential risk to human lives and infrastructures. In this research, Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) time series, provided by the European Ground Motion Service (EGMS), for the province of Frosinone in Italy are employed, and Sequential Turning Point Detection (STPD) is applied to them to estimate the dates when the displacement rates change. The estimated dates are classified based on the land cover/use of the province. Moreover, local precipitation time series are employed to investigate how precipitation rate changes might have triggered the landslides. Full article

Human pose estimation (HPE) is a technique used in computer vision and artificial intelligence to detect and track human body parts and poses using images or videos. Widely used in augmented reality, animation, fitness applications, and surveillance, HPE methods that employ monocular cameras are highly versatile and applicable to standard videos and CCTV footage. These methods have evolved from two-dimensional (2D) to three-dimensional (3D) pose estimation. However, in real-world environments, current 3D HPE methods trained on laboratory-based motion capture data encounter challenges, such as limited training data, depth ambiguity, left/right switching, and issues with occlusions. In this study, four 3D HPE methods were compared based on their strengths and weaknesses using real-world videos. Joint position correction techniques were proposed to eliminate and correct anomalies such as left/right inversion and false detections of joint positions in daily life motions. Joint angle trajectories were obtained for intuitive and informative human activity recognition using an optimization method based on a 3D humanoid simulator, with the joint position corrected by the proposed technique as the input. The efficacy of the proposed method was verified by applying it to three types of freehand gymnastic exercises and comparing the joint angle trajectories during motion. Full article

A significant percentage of bridges in the United States are serving beyond their 50-year design life, and many of them are in poor condition, making them vulnerable to fatigue cracks that can result in catastrophic failure. However, current fatigue crack inspection practice based on human vision is time-consuming, labor intensive, and prone to error. We present a novel human-centered bridge inspection methodology to enhance the efficiency and accuracy of fatigue crack detection by employing advanced technologies including computer vision and augmented reality (AR). In particular, a computer vision-based algorithm is developed to enable near-real-time fatigue crack detection by analyzing structural surface motion in a short video recorded by a moving camera of the AR headset. The approach monitors structural surfaces by tracking feature points and measuring variations in distances between feature point pairs to recognize the motion pattern associated with the crack opening and closing. Measuring distance changes between feature points, as opposed to their displacement changes before this improvement, eliminates the need of camera motion compensation and enables reliable and computationally efficient fatigue crack detection using the nonstationary AR headset. In addition, an AR environment is created and integrated with the computer vision algorithm. The crack detection results are transmitted to the AR headset worn by the bridge inspector, where they are converted into holograms and anchored on the bridge surface in the 3D real-world environment. The AR environment also provides virtual menus to support human-in-the-loop decision-making to determine optimal crack detection parameters. This human-centered approach with improved visualization and human–machine collaboration aids the inspector in making well-informed decisions in the field in a near-real-time fashion. The proposed crack detection method is comprehensively assessed using two laboratory test setups for both in-plane and out-of-plane fatigue cracks. Finally, using the integrated AR environment, a human-centered bridge inspection is conducted to demonstrate the efficacy and potential of the proposed methodology. Full article