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Traditional object detection methods usually underperform when locating tiny or small drones against complex backgrounds, since the appearance features of the targets and the backgrounds are highly similar. To address this, inspired by the magnocellular motion processing mechanisms, we proposed to utilize the spatial–temporal characteristics of the flying drones based on spiking neural networks, thereby developing the Magno-Spiking Neural Network (MG-SNN) for drone detection. The MG-SNN can learn to identify potential regions of moving targets through motion saliency estimation and subsequently integrates the information into the popular object detection algorithms to design the retinal-inspired spiking neural network module for drone motion extraction and object detection architecture, which integrates motion and spatial features before object detection to enhance detection accuracy. To design and train the MG-SNN, we propose a new backpropagation method called Dynamic Threshold Multi-frame Spike Time Sequence (DT-MSTS), and establish a dataset for the training and validation of MG-SNN, effectively extracting and updating visual motion features. Experimental results in terms of drone detection performance indicate that the incorporation of MG-SNN significantly improves the accuracy of low-altitude drone detection tasks compared to popular small object detection algorithms, acting as a cheap plug-and-play module in detecting small flying targets against complex backgrounds. [ABSTRACT FROM AUTHOR]

This study presents the design and development of an AI-based Smart Security System leveraging IoT technology for smart home applications. This research focuses on exploring and evaluating various artificial intelligence (AI) and Internet of Things (IoT) options, particularly in video processing and smart home security. The system is structured around key components: IoT technology elements, software management of IoT interactions, AI-driven video processing, and user information delivery methods. Each component's selection is based on a comparative analysis of alternative approaches, emphasizing the advantages of the chosen solutions. This study provides an in-depth discussion of the theoretical framework and implementation strategies used to integrate these technologies into the security system. Results from the system's deployment and testing are analyzed, highlighting the system's performance and the challenges faced during integration. This study also addresses how these challenges were mitigated through specific adaptations. Finally, potential future enhancements are suggested to further improve the system, including recommendations on how these upgrades could advance the functionality and effectiveness of AI-based Smart Security Systems in smart home applications. [ABSTRACT FROM AUTHOR]

This article introduces a technique for detecting four human movements using micro-doppler radar and intelligent algorithms. Micro-doppler radar exhibits the capability to detect and measure object movements with intricate detail, even capturing complex or non-rigid motions, while accurately identifying direction, velocity, and motion patterns. The application of intelligent algorithms enhances detection efficiency and reduces false alarms by discerning subtle movement patterns, thereby facilitating more accurate detection and a deeper understanding of observed object dynamics. A continuous wave radar setup was implemented utilizing a spectrum analyzer and radio frequency (RF) generator capturing signals in a spectrogram centered at 2,395 MHz. Six models were assessed for image classification: VGG-16, VGG-19, MobileNet, MobileNet V2, Xception, and Inception V3. A dataset comprising 500 images depicting four movements-running, walking, arm raising, and jumping-was curated. Our findings reveal that the most optimal architecture in terms of training time, accuracy, and loss is VGG-16, achieving an accuracy of 96%. Furthermore, precision values of 96%, 100%, and 98% were obtained for the movements of walking, running, and arm raising, respectively. Notably, VGG-16 exhibited a training loss of 4.191E-04, attributed to the utilization of the Adam optimizer with a learning rate of 0.001 over 15 epochs and a batch size of 32. [ABSTRACT FROM AUTHOR]

Eyes are highly efficient sensors created by nature, capable of perceiving external light signals with remarkable precision. The signals received by the eyes undergo intricate processing in the visual cortex, resulting in the phenomenon of vision. Among the various aspects of visual processing, the detection of motion details holds paramount significance for the survival and navigation of organisms. Extensive research has been conducted over the years to comprehend the complex mechanisms underlying motion direction detection in the visual system. In our previous work, we developed the HRC-based artificial visual system for motion direction detection. However, the model was designed solely for processing images with a single input channel and may not directly apply to colored images. In this paper, we present a novel approach to motion direction detection that supports colored images by integrating photoreceptors with different spectral sensitivities. The experiment demonstrates that incorporating color information enhances motion vision capabilities, aligning with biological theories. In this research, we expanded our model to include colored images. The process of constructing the motion direction detecting model for grayscale and colored images follows a similar trajectory. Our initial step involved constructing the core detector, which employs the HRC model to detect motion in a single direction. According to the HRC model, direction-selective neurons receive signals from two separate photoreceptors to detect motion direction. Subsequently, we developed a contrast-response system that receives input from the same photoreceptors and inhibits motion-direction-detecting neurons based on the contrast information of the input signals. Furthermore, we extended the model to two-dimensional planes to detect eight movement directions. In the two-dimensional model, the contrast-response system receives input from a number of surrounding photoreceptors and outputs an inhibitory signal to the motion-direction-detecting neurons based on the contrast information from the photoreceptors. Finally, we constructed a global motion-direction-detecting model. To demonstrate the practicality of the model, a comprehensive comparison was conducted with four deep learning models, including two types of convolutional neural networks, EfficientNetB0 and ResNet-50. The results of the comparison reveal that the proposed model outperforms the deep learning models in terms of accuracy and noise immunity. [ABSTRACT FROM AUTHOR]

Copyright of Japanese Journal of JSCE / Doboku Gakkai Ronbunshu is the property of Japan Society of Civil Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the improvement of economic level, people’s demand for sports activities is increasing, especially for on-net opposability sports such as tennis. However, learning tennis techniques is often difficult for beginners and requires a lot of repeated practice to master. Traditional teaching methods are inefficient and difficult to quantify the correctness of actions. In view of this research, a tennis sports assistance technology based on dynamic time warping algorithm is developed. By collecting athletes’ motion data and using dynamic time warping algorithm for motion similarity analysis, personalized technical improvement suggestions are provided for athletes. This technology combines components such as normalization, support vector machine, joint detection, sparse matrix, and second-order stepping mode to improve algorithm performance and reduce computational complexity. The experiment outcomes indicate that this method can validly raise the training effect of tennis players, with an accuracy rate of 95.66%, a calculation time of 0.32 seconds, a variance of 0.88, and an average absolute error of 4.22. Compared with the experimental group that does not use normalization, support vector mechanism node detection, sparse matrix, and second-order stepping mode, there is a significant improvement in performance. Therefore, technology significantly improves the scientific and targeted nature of tennis training through advanced algorithms and data processing techniques. This technology not only provides real-time and accurate feedback to help athletes improve their technical movements, but also enhances training productivity and precision, which is important for promoting the popularization and development of tennis.

Drosophila can rapidly and precisely detect changes in light in their surroundings and achieve acute perception of motion information with high energy efficiency and adaptivity owing to the cooperation of “ON” channel and the “OFF” channel in its visual system. Optical controlled bidirectional synaptic behavior of neuromorphic device is important for modeling parallel processing channels of Drosophila's visual system. In this study, an ambipolar transistor utilizing a bilayer architecture composed of p‐type pentacene and n‐type C60 as semiconductors is developed, with near‐infrared (NIR) PbS quantum dots serving as the charge‐trapping layer. This design enables a gate‐tunable positive and negative photoresponse, driven by photogating and photovoltaic effects at visible and NIR wavelengths. When regulated by a negative gate voltage, the device exhibits a suppressed photocurrent relaxation time exceeding 1000 s, demonstrating stable long‐term inhibitory characteristics. Consequently, high‐contrast excitatory and inhibitory synapses facilitate orientation and motion detection. Identification accuracies of up to 94.8% for motion direction and 98.1% for dynamic gestures are achieved. Practical applications such as intelligent monitoring and human–computer interaction stand to benefit significantly from these findings. [ABSTRACT FROM AUTHOR]

Natural visual scenes are dominated by spatiotemporal image dynamics, but how the visual system integrates “movie” information over time is unclear. We characterized optic tectal neuronal receptive fields using sparse noise stimuli and reverse correlation analysis. Neurons recognized movies of ~200-600 ms durations with defined start and stop stimuli. Movie durations from start to stop responses were tuned by sensory experience though a hierarchical algorithm. Neurons encoded families of image sequences following trigonometric functions. Spike sequence and information flow suggest that repetitive circuit motifs underlie movie detection. Principles of frog topographic retinotectal plasticity and cortical simple cells are employed in machine learning networks for static image recognition, suggesting that discoveries of principles of movie encoding in the brain, such as how image sequences and duration are encoded, may benefit movie recognition technology. We built and trained a machine learning network that mimicked neural principles of visual system movie encoders. The network, named MovieNet, outperformed current machine learning image recognition networks in classifying natural movie scenes, while reducing data size and steps to complete the classification task. This study reveals how movie sequences and time are encoded in the brain and demonstrates that brain-based movie processing principles enable efficient machine learning. [ABSTRACT FROM AUTHOR]

Surface Electromyographic (sEMG) signals are a promising approach to hand and finger gesture recognition. Most of the sEMG-based hand gesture recognition has developed based on the whole hand gesture, full wavelength, and all extracted features. However, further improvement of the recognition accuracy and reducing time complexity with effective feature extraction methods are still challenges for sEMG gesture recognition. Surface Electromyographic (sEMG) signals hold potential for hand and finger gesture recognition. While many sEMG-based hand gesture recognition methods rely on whole-hand gestures, full wavelength, and all extracted features, challenges remain in enhancing recognition accuracy, reducing time complexity, and effectively extracting features. In our study, we introduced a novel method for sEMG-based hand gesture recognition, emphasizing improving recognition accuracy and time efficiency. Our method integrates segmentation, effective feature extraction, and a potential feature selection approach to address these challenges. We captured the sEMG signal's significant motion from multiple channels using a sliding window and the MAD approach. From each channel, we extracted eighteen TD and FD features, yielding 144 features for the MA21 dataset and 360 for the UC8 dataset. We employed the LR algorithm for feature selection, enhancing our system's efficiency. Four ML classifiers, namely ETC, RF, SVM, and KNN, were tested on both segmented and full wavelength sEMG signals. The ETC outperformed, achieving peak accuracies of 97.33% (segmented) and 97.26% (full wavelength) for MA21 and 99.88% and 99.70% for UC8. Our model surpassed existing methods by over 5% in accuracy, highlighting its efficiency and superior capability. [ABSTRACT FROM AUTHOR]

Functional near-infrared spectroscopy (fNIRS) is beneficial for studying brain activity in naturalistic settings due to its tolerance for movement. However, residual motion artifacts still compromise fNIRS data quality and might lead to spurious results. Although some motion artifact correction algorithms have been proposed in the literature, their development and accurate evaluation have been challenged by the lack of ground truth information. This is because ground truth information is time- and labor-intensive to manually annotate. This work investigates the feasibility and reliability of a deep learning computer vision (CV) approach for automated detection and annotation of head movements from video recordings. Fifteen participants performed controlled head movements across three main rotational axes (head up/down, head left/right, bend left/right) at two speeds (fast and slow), and in different ways (half, complete, repeated movement). Sessions were video recorded and head movement information was obtained using a CV approach. A 1-dimensional UNet model (1D-UNet) that detects head movements from head orientation signals extracted via a pre-trained model (SynergyNet) was implemented. Movements were manually annotated as a ground truth for model evaluation. The model's performance was evaluated using the Jaccard index. The model showed comparable performance between the training and test sets (J train = 0.954; J test = 0.865). Moreover, it demonstrated good and consistent performance at annotating movement across movement axes and speeds. However, performance varied by movement type, with the best results being obtained for repeated (J test = 0.941), followed by complete (J test = 0.872), and then half movements (J test = 0.826). This study suggests that the proposed CV approach provides accurate ground truth movement information. Future research can rely on this CV approach to evaluate and improve fNIRS motion artifact correction algorithms. [ABSTRACT FROM AUTHOR]

A flexible pressure sensor, capable of effectively detecting forces exerted on soft or deformable surfaces, has demonstrated broad application in diverse fields, including human motion tracking, health monitoring, electronic skin, and artificial intelligence systems. However, the design of convenient sensors with high sensitivity and excellent stability is still a great challenge. Herein, we present a multi-scale 3D graphene pressure sensor composed of two types of 3D graphene foam. The sensor exhibits a high sensitivity of 0.42 kPa−1 within the low-pressure range of 0–390 Pa and 0.012 kPa−1 within the higher-pressure range of 0.4 to 42 kPa, a rapid response time of 62 ms, and exceptional repeatability and stability exceeding 10,000 cycles. These characteristics empower the sensor to realize the sensation of a drop of water, the speed of airflow, and human movements. [ABSTRACT FROM AUTHOR]

Significance: Motion artifacts are a notorious challenge in the functional near-infrared spectroscopy (fNIRS) field. However, little is known about how to deal with them in resting-state data. Aim: We assessed the impact of motion artifact correction approaches on assessing functional connectivity, using semi-simulated datasets with different percentages and types of motion artifact contamination. Approach: Thirty-five healthy adults underwent a 15-min resting-state acquisition. Semi-simulated datasets were generated by adding spike-like and/or baseline-shift motion artifacts to the real dataset. Fifteen pipelines, employing various correction approaches, were applied to each dataset, and the group correlation matrix was computed. Three metrics were used to test the performance of each approach. Results: When motion artifact contamination was low, various correction approaches were effective. However, with increased contamination, only a few pipelines were reliable. For datasets mostly free of baseline-shift artifacts, discarding contaminated frames after pre-processing was optimal. Conversely, when both spike and baseline-shift artifacts were present, discarding contaminated frames before pre-processing yielded the best results. Conclusions: This study emphasizes the need for customized motion correction approaches as the effectiveness varies with the specific type and amount of motion artifacts present. [ABSTRACT FROM AUTHOR]

Organic retinomorphic sensors offer the advantage of in-sensor processing to filter out redundant static backgrounds and are well suited for motion detection. To improve this promising structure, here, the key role of interfacial energetics in promoting charge accumulation to raise the inherent photoresponse of the light-sensitive capacitor is studied. Specifically, incorporating appropriate interfacial layers around the photoactive layer is crucial to extend the carrier lifetime, as confirmed by intensity-modulated photovoltage spectroscopy. Compared to its photodiode counterpart, the retinomorphic sensor shows better detectivity and response speed due to the additional insulating layer, which reduces the dark current and the RC time constant. Lastly, three retinomorphic sensors are integrated into a line array to demonstrate the detection of movement speed and direction, showing the potential of retinomorphic designs for efficient motion tracking.

Traditional object detection methods usually underperform when locating tiny or small drones against complex backgrounds, since the appearance features of the targets and the backgrounds are highly similar. To address this, inspired by the magnocellular motion processing mechanisms, we proposed to utilize the spatial–temporal characteristics of the flying drones based on spiking neural networks, thereby developing the Magno-Spiking Neural Network (MG-SNN) for drone detection. The MG-SNN can learn to identify potential regions of moving targets through motion saliency estimation and subsequently integrates the information into the popular object detection algorithms to design the retinal-inspired spiking neural network module for drone motion extraction and object detection architecture, which integrates motion and spatial features before object detection to enhance detection accuracy. To design and train the MG-SNN, we propose a new backpropagation method called Dynamic Threshold Multi-frame Spike Time Sequence (DT-MSTS), and establish a dataset for the training and validation of MG-SNN, effectively extracting and updating visual motion features. Experimental results in terms of drone detection performance indicate that the incorporation of MG-SNN significantly improves the accuracy of low-altitude drone detection tasks compared to popular small object detection algorithms, acting as a cheap plug-and-play module in detecting small flying targets against complex backgrounds.

Purpose: Widening the availability of fetal MRI with fully automatic real‐time planning of radiological brain planes on 0.55T MRI. Methods: Deep learning‐based detection of key brain landmarks on a whole‐uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single‐shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted. Results: Prospective automatic planning was performed in real‐time without latency in all subjects. The landmark detection accuracy was 4.2 ±$$ \pm $$ 2.6 mm for the fetal eyes and 6.5 ±$$ \pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning. Conclusions: Real‐time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers. [ABSTRACT FROM AUTHOR]

Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The motor imagery (MI) problem is an important research area in this field. Accurate solutions to this problem will greatly affect real-world applications. Most of the proposed methods are based on raw signal processing techniques. Known as prior knowledge, the structural-functional information and interregional connections can improve signal processing accuracy. It is possible to correctly perceive the generated signals by considering the brain structure (i.e. anatomical units), the source of signals, and the structural-functional dependence of different brain regions (i.e. effective connection) that are the semantic generators of signals. This study employed electroencephalograph (EEG) signals based on the activity of brain regions (cortex) and effective connections between brain regions based on dynamic causal modeling to solve the MI problem. EEG signals, as well as effective connections between brain regions to improve the interpretability of MI action, were fed into the architecture of Graph Convolutional Neural Network (GCN). The proposed model allowed GCN to extract more discriminative features. The results indicated that the proposed method was successful in developing a model with a MI detection accuracy of 93.73%. [ABSTRACT FROM AUTHOR]

A method for detecting motion in a frame and identifying a large object is described in this article. The operation of the method is illustrated by an example from the maritime transport industry. The example shows the solution of the task of monitoring the position of an autonomous marine large-tonnage ship relative to its berth when performing loading, unloading, and mooring operations. This paper incudes description of the structure of a measuring complex which includes optical meters. The operating principle of the complex is based on the method of detecting motion in a frame and identifying a large object. A diagram of the algorithm for detecting motion in a frame and identifying a large object is presented in this paper. The performance of the software implementation of the algorithm for detecting motion in a frame and identifying a large object is assessed in this article. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As an excellent material for flexible sensors, conductive hydrogels have broad prospects in the field of wearable devices. In this study, a series of PVA/EG conductive hydrogels with different mass ratios of PVA were prepared by mixing the hydrogel base polyvinyl alcohol (PVA) with ethylene glycol (EG). The structures and morphologies of 10%, 20%, and 30% PVA/EG hydrogels were characterized, among which, the 20% PVA/EG hydrogel exhibited higher relative background intensity, crystalline content, and more surface pores. The sensing properties of the 20% PVA/EG conductive hydrogel, including sensitivity, linearity, response time, stability, and temperature reliability, were tested using a universal materials testing machine. The results showed that the sensitivity coefficient of the conductive hydrogel reached up to 0.74, the linear correlation coefficient reached 0.987, the response time was as low as 80 ms, and the relative resistance change remained relatively constant during 50 cycles of 15% stretching. Moreover, the hydrogel sample continued to function normally at -20 °C. Finally, the conductive hydrogel samples were attached to different parts of the body, such as fingers, spine, and feet, for motion experiments, validating the feasibility of the conductive hydrogel in various human motion monitoring scenarios. [ABSTRACT FROM AUTHOR]

Background: Motion induced image artifacts have been the focus of many investigations for x‐ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post‐processing algorithms to minimize motion artifacts. Purpose: Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image‐space spatial‐ and temporal‐consistency metric (CM) to detect and characterize the local motion. Method: In a non‐rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region‐of‐interests for consistency‐metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion. Results: Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in‐plane motion, cross‐plane motion, cone‐beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images. Conclusions: In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view‐by‐view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact. [ABSTRACT FROM AUTHOR]

Recent years have seen a sharp rise in crime, raising concerns about neighborhood safety among the general public. A number of security technologies, such as digital door locks, closed-circuit televisions (cctvs), and alarm systems, have been developed and implemented. Even though most buildings have cctv installed, manually monitoring the footage takes a lot of manpower. The hog (histogram of oriented gradients) algorithm is widely used for object detection in computer vision. In particular, it shows good performance in face recognition and motion detection. In addition to identifying the user, the proposed system allows for mobile engagement, allowing the user to see the video, set off the alarm, and even receive notifications when an unknown person approaches the residence. The system proposed in the paper seeks to improve the performance of the current surveillance system by applying additional functions. The system was implemented to recognize people through real-time monitoring and send warnings if they are not registered users. Additionally, the system can be used to quickly report to the police. [ABSTRACT FROM AUTHOR]

This paper presents a prototype for an intelligent, self-contained theft detection system designed for small-scale applications. Utilizing a Raspberry Pi 3 as the core processing unit, the system employs a motion-detecting camera to monitor a defined area, recording and securely archiving video data on a cloud server upon detecting movement. This cloud-based repository supports real-time analysis, ensuring that data remains available for future reference. Battery-powered configuration enhances the system’s portability, making it adaptable across various environments, such as healthcare for patient monitoring or wildlife tracking for behavioural studies. The design aligns with IoT principles, featuring autonomous operation and cloud connectivity, offering a scalable, flexible solution capable of integration into larger IoT ecosystems for diverse surveillance applications.

The potential applications of wearable sensors in health monitoring, micro-environment detection, and advanced soft electronic noses have attracted widespread attention. However, due to the inherent limitations of traditional organic flexible substrates, simultaneously achieving excellent flexibility, high sensitivity, robustness, and breathability remains a significant challenge. Herein, using the electrospinning method, a flexible TPU substrate film was prepared, and Bi2S3/Ti3C2Tx nanocomposite materials were synthesized through a simple one-step hydrothermal method. Au nanoparticles were attached to the surface of the composite material using sodium borohydride, and finally, the surface impregnation method was used to successfully prepare the Au-Bi2S3/Ti3C2Tx-TPU flexible electrospun mat. The as-prepared sensor demonstrated an outstanding response of 154 % to 100 ppm NH3 at room temperature, coupled with high selectivity, repeatability, and long-term stability. The strain properties of the sensor were also evaluated, showcasing its potential for human motion and physiological feature detection, such as breathing, swallowing, and limb movements. The study’s findings indicated that the Au-Bi2S3/Ti3C2Tx-TPU electrospun mat sensor has promising applications in flexible electronics, wearable environmental monitoring devices, and human healthy monitoring.

Series of motion detection based on background subtraction there is an image segmentation stage. Thresholding is a common technique used for the segmentation process. There are two types that can be used in thresholding techniques namely local and global. This research intends to implement local adaptive wolf thresholding as the threshold value of the background subtraction method to detect motion objects. The proposed method consists of the reading frame, background and foreground initialization of each frame, preprocessing, background subtraction, wolf thresholding, providing a bounding box, and running frame sequentially. Based on MSE and PSNR obtained on four videos, it has shown that wolf thresholding has succeeded in outperforming of global threshold.

Accurately capturing human movements is a crucial element of health status monitoring and a necessary precondition for realizing future virtual reality/augmented reality applications. Flexible motion sensors with exceptional sensitivity are capable of detecting physical activities by converting them into resistance fluctuations. Silver nanowires (AgNWs) have become a preferred choice for the development of various types of sensors due to their outstanding electrical conductivity, transparency, and flexibility within polymer composites. Herein, we present the design and fabrication of a flexible strain sensor based on silver nanowires. Suitable substrate materials were selected, and the sensor's sensitivity and fatigue properties were characterized and tested, with the sensor maintaining reliability after 5000 deformation cycles. Different sensors were prepared by controlling the concentration of silver nanowires to achieve the collection of motion signals from various parts of the human body. Additionally, we explored potential applications of these sensors in fields such as health monitoring and virtual reality. In summary, this work integrated the acquisition of different human motion signals, demonstrating great potential for future multifunctional wearable electronic devices. [ABSTRACT FROM AUTHOR]

Fire poses a significant threat to daily life, causing both economic and social harm. To mitigate these damages, early detection of fire and smoke is crucial, and this paper introduces a model employing vision-based techniques. The proposed model utilizes image processing and convolutional neural networks to detect fire and smoke, providing insights into their intensity and any changes in a video. The model comprises two units for fire and smoke detection, each employing image preprocessing techniques, including rule-based color detection and motion detection, along with CNN. The calculated percentages of fire and smoke in the processed images offer detailed information about the severity of the hazards in a specific area. The model detects whether the intensity of fire and smoke is increasing, decreasing or constant. [ABSTRACT FROM AUTHOR]

Summary: With its low power consumption and small size, the memristor has shown great potential for improving data storage density and computing efficiency. Compared to the dual‐port memristor, greater attention should be paid to researching gate‐tunable memristor for image processing to improve the processing speed and reduce hardware resource consumption. Developing gate‐tunable memristor emulators is highly attractive given the immaturity of current fabrication of the gate‐tunable memristor. This work proposes a digital gate‐tunable memristor emulator based on Raspberry Pi, which addresses the non‐reconfigurability and inflexibility issues of the analog emulators. The proposed emulator can match the behavior of different memristor devices by regulating the gate voltage parameter. Additionally, it can operate at a maximum frequency of 500 MHz. To test the functionality of the proposed emulator, a digital implementation of the memristive circuit for motion detection is designed and verified experimentally. Experiments demonstrate that when moving object detection is performed on a 640 × 350 pixel video stream, low power consumption of 53 mW and a delay of 3.52 μs can be achieved. [ABSTRACT FROM AUTHOR]

In order to improve the sensitivity, responsiveness and stability of flexible pressure sensor based on fabric, a variable honeycomb weave fabric with a dome-shaped micro-surface structure was woven by introducing micro-structures in the woven structure design into cotton fabric. Variable honeycomb fabric, carbon black(CB)and polyvinyl alcohol(PVA)were used as raw materials, different sensor fabrics were prepared by immersion method. The relationship between the mass fraction of carbon black and the sensor resistance was tested, the structural morphology and mechanical sensing performance of the prepared sensor were characterized to explore its application in human motion monitoring. The results showed that the prepared sensor had high sensitivity and small hysteresis when the mass fraction of carbon black was 5% and the sensor resistance was decreased to 20 kΩ/cm. Under different pressures and compression rates, the sensor could maintain better stability, repeatability and durability. It is considered that the prepared sensor can be applied to human motion monitoring, including finger pressing, knuckle, wrist and elbow bending movements. [ABSTRACT FROM AUTHOR]

Copyright of Foundry Technology (1000-8365) is the property of Foundry Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of the flexible electronics field, flexible pressure sensors are widely used inwearable devices, medical monitoring and other fields. However, it is still a huge challenge to design a sensor with highsensitivity, low cost and a simple manufacturing process. In this work, we report a flexible strain sensor based on discarded mask straps (MS) with a unique three-dimensional (3D) twist-like network structure. Multi-walled carbon nanotubes (MWCNTs)/carbon nanoparticles (CN)/MS composites were prepared by a simple dip-drying method and encapsulated using a medical Polyurethane (PU) film. It is founded that the fabricated MWCNTs/CN/MS/PU flexible strain sensor (MCMP-FSS) exhibits a high gauge factor (GF) of 13,928 and advanced performance with a large sensing range of 0%–72%. Moreover, good water-repellent harmony and various types of stimuli over 1000 cycles were obtained for the MCMP-FSS. Benefiting from the unique 3D twist-like network structure and the MWCNTs/CN synergistic conductive network, MCMP-FSS also has broad application prospects in the fields of human motion detection, encrypted communication, and natural human-robot interaction. [ABSTRACT FROM AUTHOR]

Abstract Objective To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning. Methods We collected the video recordings of ICU patients and cut them into 30-second (30-s) and 2-second (2-s) segments. All of the segments were annotated with the status of agitation as “Attention” and “Non-attention”. After transforming the video segments into movement quantification, we constructed the models of agitation classifiers with Threshold, Random Forest, and LSTM and evaluated their performances. Results The video recording segmentation yielded 427 30-s and 6405 2-s segments from 61 patients for model construction. The LSTM model achieved remarkable accuracy (ACC 0.92, AUC 0.91), outperforming other methods. Conclusion Our study proposes an advanced monitoring system combining LSTM and image processing to ensure mild patient sedation in ICU care. LSTM proves to be the optimal choice for accurate monitoring. Future efforts should prioritize expanding data collection and enhancing system integration for practical application.

A flexible pressure sensor, capable of effectively detecting forces exerted on soft or deformable surfaces, has demonstrated broad application in diverse fields, including human motion tracking, health monitoring, electronic skin, and artificial intelligence systems. However, the design of convenient sensors with high sensitivity and excellent stability is still a great challenge. Herein, we present a multi-scale 3D graphene pressure sensor composed of two types of 3D graphene foam. The sensor exhibits a high sensitivity of 0.42 kPa−1 within the low-pressure range of 0–390 Pa and 0.012 kPa−1 within the higher-pressure range of 0.4 to 42 kPa, a rapid response time of 62 ms, and exceptional repeatability and stability exceeding 10,000 cycles. These characteristics empower the sensor to realize the sensation of a drop of water, the speed of airflow, and human movements.