Capture Device Research Articles

A MACHINE LEARNING APPROACH TO EYE BLINK DETECTION IN LOW-LIGHT VIDEOS

Inadequate lighting conditions can harm the accuracy of blink detection systems, which play a crucial role in fatigue detection technology, transportation and security applications. While some video capture devices are now equipped with flashlight technology to enhance lighting, users occasionally need to remember to activate this feature, resulting in slightly darker videos. Consequently, there is a pressing need to improve the performance of blink detection systems to detect eye accurately blinks in low light videos. This research proposes developing a machine learning-based blink detection system to see flashes in low-light videos. The Confusion matrix was conducted to evaluate the effectiveness of the proposed blink detection system. These tests involved 31 videos ranging from 5 to 10 seconds in duration. Involving male and female test subjects aged between 20 and 22. The accuracy of the proposed blink detection system was measured using the confusion matrix method. The results indicate that by leveraging a machine learning approach, the blink detection system achieved a remarkable accuracy of 100% in detecting blinks within low-light videos. However, this research necessitates further development to account for more complex and diverse real-life situations. Future studies could focus on developing more sophisticated algorithms and expanding the test subjects to improve the performance of the blink detection system in low light conditions. Such advancements would contribute to the practical application of the system in a broader range of scenarios, ultimately enhancing its effectiveness in fatigue detection technology.

Research on Compliant Controller of Underwater Mechanical Capture System Based on Impedance Theory

Due to the requirement of the exploitation of marine resources, the execution of specific underwater tasks by onboard manipulators has become one of the key research fields in domestic and all over the world. Based on the underwater capture system designed for the UUV recycling task, which consists of an underwater manipulator and a mechanical capture device, this paper first constructs the kinematics and dynamics model of the capture system through theoretical analysis such as theoretical mechanics and theory of mechanism. Then, combined with the requirements of the recycling task, through the theoretical basis of fluid mechanics such as Morrison equation, dynamic of the capture system in underwater environment is analysed, with a compliant controller designed for the capture system based on impedance theory in order to reduce the impact of underwater environment in the capture task. Moreover, as the capture system modelled in the Adams dynamics simulation platform, it is verified that the designed compliant controller can reduce the underwater environmental impact through simulation experiments in the Adams dynamic platform.

Human Action Recognition Using Key-Frame Attention-Based LSTM Networks

Human action recognition is a classical problem in computer vision and machine learning, and the task of effectively and efficiently recognising human actions is a concern for researchers. In this paper, we propose a key-frame-based approach to human action recognition. First, we designed a key-frame attention-based LSTM network (KF-LSTM) using the attention mechanism, which can be combined with LSTM to effectively recognise human action sequences by assigning different weight scale values to give more attention to key frames. In addition, we designed a new key-frame extraction method by combining an automatic segmentation model based on the autoregressive moving average (ARMA) algorithm and the K-means clustering algorithm. This method effectively avoids the possibility of inter-frame confusion in the temporal sequence of key frames of different actions and ensures that the subsequent human action recognition task proceeds smoothly. The dataset used in the experiments was acquired with an IMU sensor-based motion capture device, and we separately extracted the motion features of each joint using a manual method and then performed collective inference.

Deep Learning-Based Image Classification for Major Mosquito Species Inhabiting Korea.

Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In this study, we developed an automatic image analysis method to identify mosquito species using a deep learning-based object detection technique. Color and fluorescence images of live mosquitoes were acquired using a mosquito capture device and were used to develop a deep learning-based object detection model. Among the deep learning-based object identification models, the combination of a swine transformer and a faster region-convolutional neural network model demonstrated the best performance, with a 91.7% F1-score. This indicates that the proposed automatic identification method can be rapidly applied for efficient analysis of species and populations of vector-borne mosquitoes with reduced labor in the field.

Connection Between Sleep and Psychological Well-Being in U.S. Army Soldiers.

The goal of this exploratory study was to examine the relationships between sleep consistency and workplace resilience among soldiers stationed in a challenging Arctic environment. A total of 862 soldiers (67 females) on an Army base in Anchorage, AK, were provided WHOOP 3.0, a validated sleep biometric capture device and were surveyed at onboarding and at the conclusion of the study. Soldiers joined the study from early January to early March 2021 and completed the study in July 2021 (650 soldiers completed the onboarding survey and 210 completed the exit survey, with 151 soldiers completing both). Three comparative analyses were conducted. First, soldiers' sleep and cardiac metrics were compared against the general WHOOP population and a WHOOP sample living in AK. Second, seasonal trends (summer versus winter) in soldiers' sleep metrics (time in bed, hours of sleep, wake duration during sleep, time of sleep onset/offset, and disturbances) were analyzed, and these seasonal trends were compared with the general WHOOP population and the WHOOP sample living in AK. Third, soldiers' exertion, sleep duration, and sleep consistency were correlated with their self-reported psychological functioning. All analyses were conducted with parametric and non-parametric statistics. This study was approved by The University of Queensland Human Research Ethics Committee (Brisbane, Australia) Institutional Review Board. Because of the exploratory nature of the study, the critical significance value was set at P < .001. Results revealed that: (1) Arctic soldiers had poorer sleep consistency and sleep duration than the general WHOOP sample and the Alaskan WHOOP sample, (2) Arctic soldiers showed a decrease in sleep consistency and sleep duration in the summer compared to that in the winter, (3) Arctic soldiers were less able to control their bedroom environment in the summer than in the winter, and (4) sleep consistency but not sleep duration correlated positively with self-report measures of workplace resilience and healthy social networks and negatively with homesickness. The study highlights the relationship between seasonality, sleep consistency, and psychological well-being. The results indicate the potential importance of sleep consistency in psychological functioning, suggesting that future work should manipulate factors known to increase sleep consistency to assess whether improved sleep consistency can enhance the well-being of soldiers. Such efforts would be of particular value in an Arctic environment, where seasonality effects are large and sleep consistency is difficult to maintain.

Semi-Passive RFID Electronic Devices With On-Chip Sensor Fusion Capabilities for Motion Capture and Biomechanical Analysis

This paper presents the development and testing of a novel electronic device for Wireless Motion Capture (W-MoCap), a technology that allows the reconstruction of movements of objects and body parts with many potential applications in various contexts. The device integrates UHF RFID technology with sensors for low-power backscattering communication. It consists of a Battery Assisted Passive UHF RFID chip, an Inertial Measurement Unit, an ultra-low power microcontroller, and a custom-designed edge-fed body-tolerant antenna operating at 866 MHz. The proper matching between the RFID chip and antenna is ensured through a well-designed L-match unbalanced network, and the separation of RF and DC signals is achieved with a meandered microstrip quarter-wavelength transformer, choke inductor, and decoupling capacitor. The designed and realized RFID W-MoCap Sensor-Tag has been thoroughly evaluated in terms of current consumption, front-end sensitivity, and sensing accuracy. Finally, five prototypes have been applied to specific segments of a human subject and successfully tested in a practical scenario for real time reconstruction of human movements.

Comparative Evaluation of Commercial Protein A Membranes for the Rapid Purification of Antibodies.

Protein A chromatography is ubiquitous to antibody purification. The high specificity of Protein A for binding the Fc-region of antibodies and related products enables unmatched clearance of process impurities like host cell proteins, DNA, and virus particles. A recent development is the commercialization of research-scale Protein A membrane chromatography products that can perform capture step purification with short residence times (RT) on the order of seconds. This study investigates process-relevant performance and physical properties of four Protein A membranes: Purilogics Purexa™ PrA, Gore® Protein Capture Device, Cytiva HiTrap™ Fibro PrismA, and Sartorius Sartobind® Protein A. Performance metrics include dynamic binding capacity, equilibrium binding capacity, regeneration-reuse, impurity clearance, and elution volumes. Physical properties include permeability, pore diameter, specific surface area, and dead volume. Key results indicate that all membranes except the Gore® Protein Capture Device operate with flow rate-independent binding capacities; the Purilogics Purexa™ PrA and Cytiva HiTrap Fibro™ PrismA have binding capacities on par with resins, with orders of magnitude faster throughput; and dead volume and hydrodynamics play major roles in elution behavior. Results from this study will enable bioprocess scientists to understand the ways that Protein A membranes can fit into their antibody process development strategies.

The spectrum of eating environments encountered in free living adults documented using a passive capture food intake wearable device.

The aim of this feasibility and proof-of-concept study was to examine the use of a novel wearable device for automatic food intake detection to capture the full range of free-living eating environments of adults with overweight and obesity. In this paper, we document eating environments of individuals that have not been thoroughly described previously in nutrition software as current practices rely on participant self-report and methods with limited eating environment options. Data from 25 participants and 116 total days (7 men, 18 women, Mage = 44 ± 12 years, BMI 34.3 ± 5.2 kg/mm2), who wore the passive capture device for at least 7 consecutive days (≥12h waking hours/d) were analyzed. Data were analyzed at the participant level and stratified amongst meal type into breakfast, lunch, dinner, and snack categories. Out of 116 days, 68.1% included breakfast, 71.5% included lunch, 82.8% included dinner, and 86.2% included at least one snack. The most prevalent eating environment among all eating occasions was at home and with one or more screens in use (breakfast: 48.1%, lunch: 42.2%, dinner: 50%, and snacks: 55%), eating alone (breakfast: 75.9%, lunch: 89.2%, dinner: 74.3%, snacks: 74.3%), in the dining room (breakfast: 36.7%, lunch: 30.1%, dinner: 45.8%) or living room (snacks: 28.0%), and in multiple locations (breakfast: 44.3%, lunch: 28.8%, dinner: 44.8%, snacks: 41.3%). Results suggest a passive capture device can provide accurate detection of food intake in multiple eating environments. To our knowledge, this is the first study to classify eating occasions in multiple eating environments and may be a useful tool for future behavioral research studies to accurately codify eating environments.

Study of the Power Generation Performance of Impact Piezoelectric Energy Capture Devices.

In order to solve the problem of conventional energy shortages, a non-resonant impact piezoelectric energy capture device using a (polyvinylidene fluoride) piezoelectric film at low frequency is proposed, and related theoretical analysis and experimental studies are conducted. The device has a simple internal structure, is green and easy to miniaturize, and is capable of harvesting energy at low frequencies to supply energy to micro and small electronic devices. First, to verify the feasibility of the device, the structure of the experimental device is modeled and dynamically analyzed. Then the modal, stress-strain, and output voltage of the piezoelectric film are simulated and analyzed using COMSOL Multiphysics simulation software. Finally, the experimental prototype is built according to the model, and the experimental platform is constructed to test the relevant performance. The experimental results show that the output power produced by the capturer varies within a certain range when the capturer is excited externally. With an external excitation force of 30 N, a piezoelectric film bending amplitude of 60°, and a piezoelectric film size of 45 × 80 mm, the resulting output power voltage is 21.69 V, the output current is 0.07 mA, and the output power is 1.5176 mW. This experiment verifies the feasibility of the energy capturer and provides a new idea for powering electronic components.

Generalization of the shortest path approach for superpixel segmentation of omnidirectional images

With the growing use of image capture devices using wide angles and the need for fast and accurate image analysis in computer vision, there is a demand for dedicated under-representation methods. Most decomposition methods segment an image into a small number of irregular homogeneous regions, called superpixels. Nevertheless, these approaches are generally designed to process natural 2D planar images, i.e., captured with a 90o angle view without distortion. In this work, we present SphSPS, a new general decomposition method (for Spherical Shortest Path-based Superpixels)11Available code at: https://github.com/rgiraud/sphsps, that is dedicated to wide 360o omnidirectional or spherical images. The produced superpixels respect both the geometry of the 3D spherical acquisition space, and the boundaries of image objects. To fastly extract relevant clustering features, we generalize the shortest path approach between a pixel and a superpixel center. We demonstrate that considering the geometry of the acquisition space to compute the shortest path enables to jointly improve the segmentation accuracy and the shape regularity of superpixels. To evaluate this regularity property, we propose a generalization of a standard 2D regularity metric to the spherical space, addressing the limitations of the only existing spherical compactness measure. Finally, SphSPS is validated on reference 360o images from the PSD (Panorama Segmentation Dataset) and also on synthetic road omnidirectional images. Our method significantly outperforms both planar and spherical state-of-the-art approaches in terms of segmentation accuracy, robustness to noise and regularity, providing a very interesting tool for superpixel-based applications on 360o images.

Low Carbon Optimal Scheduling of Integrated Energy Systems Based on Carbon Capture Devices

Carbon pollution has gradually become a serious environmental problem in recent years. The low-carbon optimization dispatch of the Integrated Energy Systems (IES) can realize the complementary and energy-use substitution of multiple energy sources and solve the problem of high carbon emission. In this paper, we first construct a system model and a carbon emission model. Then, we introduce carbon capture equipment into the system and construct a system optimization model with equipment constraints for each unit. Finally, we solve the proposed model with a complex solver in Matlab to verify the accuracy of the proposed model and the effectiveness of the method. The results show that the strategy can effectively reduce the carbon emissions of the IES.

Conductive Nanofibers-Enhanced Microfluidic Device for the Efficient Capture and Electrical Stimulation-Triggered Rapid Release of Circulating Tumor Cells

The effective detection and release of circulating tumor cells (CTCs) are of great significance for cancer diagnosis and monitoring. The microfluidic technique has proved to be a promising method for CTCs isolation and subsequent analysis. However, complex micro-geometries or nanostructures were often constructed and functionalized to improve the capture efficiency, which limited the scale-up for high-throughput production and larger-scale clinical applications. Thus, we designed a simple conductive nanofiber chip (CNF-Chip)-embedded microfluidic device with a herringbone microchannel to achieve the efficient and specific capture and electrical stimulation-triggered rapid release of CTCs. Here, the most used epithelial cell adhesion molecule (EpCAM) was selected as the representative biomarker, and the EpCAM-positive cancer cells were mainly studied. Under the effects of the nanointerface formed by the nanofibers with a rough surface and the herringbone-based high-throughput microfluidic mixing, the local topographic interaction between target cells and nanofibrous substrate in the microfluidic was synergistically enhanced, and the capture efficiency for CTCs was further improved (more than 85%). After capture, the sensitive and rapid release of CTCs (release efficiency above 97%) could be conveniently achieved through the cleavage of the gold-sulfur bond by applying a low voltage (-1.2 V). The device was successfully used for the effective isolation of CTCs in clinical blood samples from cancer patients, indicating the great potential of this CNF-Chip-embedded microfluidic device in clinical applications.

Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities.

Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has a role in preventing WMSDs by contributing to improvements in exposure and risk assessment and potentially improved effectiveness in work technique training. Given the versatile potential for wearables, this article aims to provide an overview of their application related to the prevention of WMSDs of the trunk and upper limbs and discusses challenges for the technology to support prevention measures and future opportunities, including future research needs. The relevant literature was identified from a screening of recent systematic literature reviews and overviews, and more recent studies were identified by a literature search using the Web of Science platform. Wearable technology enables continuous measurements of multiple body segments of superior accuracy and precision compared to observational tools. The technology also enables real-time visualization of exposures, automatic analyses, and real-time feedback to the user. While miniaturization and improved usability and wearability can expand the use also to more occupational settings and increase use among occupational safety and health practitioners, several fundamental challenges remain to be resolved. The future opportunities of increased usage of wearable motion capture devices for the prevention of work-related musculoskeletal disorders may require more international collaborations for creating common standards for measurements, analyses, and exposure metrics, which can be related to epidemiologically based risk categories for work-related musculoskeletal disorders.

Tele-ultrasound imaging using smartphones and single-board PCs

BACKGROUND: Mobile devices are widely available and their computational performance increases. Nonetheless, medicine should not be an exception: single-board computers and mobile phones are crucial aides in telehealth.&#x0D; AIM: To explore tele-ultrasound scope using smartphones and single-board computers&#x0D; MATERIALS AND METHODS: This study focused on capturing ultrasound videos using external video recording devices connected via USB. Raspberry Pi single-board computers and Android smartphones have been used as platforms to host a tele-ultrasound server. Used software: VLC, Motion, and USB camera. A remote expert assessment was performed with mobile devices using the following software: VLC acted as a VLC server, Google Chrome for OS Windows 7 and OS Android was used in the remaining scenarios, and Chromium browser was installed on the Raspberry Pi computer.&#x0D; OUTCOMES: The UTV007 chip-based video capture device produces better images than the AMT630A-based device. The optimum video resolution was 720576 and 25 frames per second. VLC and OBS studios are considered the most suitable for a raspberry-based ultrasound system owing to low equipment and bandwidth requirements (0.640.17 Mbps for VLC; 0.5 Mbps for OBS studio). For Android phone OS, the ultrasound system was set with the USB camera software, although it required a faster network connection speed (5.20.3 Mbps).&#x0D; CONCLUSION: The use of devices based on single-board computers and smartphones implements a low-cost tele-ultrasound system, which potentially improves the quality of studies performed through distance learning and consulting doctors. These solutions can be used in remote regions for field medicine tasks and other possible areas of m-health.

Viewpoint-Controllable Telepresence: A Robotic-Arm-Based Mixed-Reality Telecollaboration System.

In mixed-reality (MR) telecollaboration, the local environment is remotely presented to a remote user wearing a virtual reality (VR) head-mounted display (HMD) via a video capture device. However, remote users frequently face challenges in naturally and actively manipulating their viewpoints. In this paper, we propose a telepresence system with viewpoint control, which involves a robotic arm equipped with a stereo camera in the local environment. This system enables remote users to actively and flexibly observe the local environment by moving their heads to manipulate the robotic arm. Additionally, to solve the problem of the limited field of view of the stereo camera and limited movement range of the robotic arm, we propose a 3D reconstruction method combined with a stereo video field-of-view enhancement technique to guide remote users to move within the movement range of the robotic arm and provide them with a larger range of local environment perception. Finally, a mixed-reality telecollaboration prototype was built, and two user studies were conducted to evaluate the overall system. User study A evaluated the interaction efficiency, system usability, workload, copresence, and user satisfaction of our system from the remote user's perspective, and the results showed that our system can effectively improve the interaction efficiency while achieving a better user experience than two traditional view-sharing techniques based on 360 video and based on the local user's first-person view. User study B evaluated our MR telecollaboration system prototype from both the remote-user side and the local-user side as a whole, providing directions and suggestions for the subsequent design and improvement of our mixed-reality telecollaboration system.

A new kinematic dataset of lower limbs action for balance testing

Balance is a common performance but nevertheless an essential part of performance analysis investigations in ski. Many skier pay attention to the training of balance ability in training. Inertial Measurement Unit, as a kind of Multiplex-type human motion capture system, is widely used because of its humanized human-computer interaction design, low energy consumption and more freedom provided by the environment. The purpose of this research is to use sensor to establish a kinematics dataset of balance test tasks extracted from skis to help quantify skier’ balance ability. Perception Neuron Studio motion capture device is used in present. The dataset contains a total of 20 participants’ data (half male) of the motion and sensor data, which is collected at a 100 Hz sampling frequency. To our knowledge, this dataset is the only one that uses a BOSU ball in the balance test. We hope that this dataset will contribute to multiple fields of cross-technology integration in physical training and functional testing, including big-data analysis, sports equipment design and sports biomechanical analysis.

Abstract 2296: Engineering and evaluation of a novel circulating tumor cell capture assay for urothelial carcinoma

Abstract Background: Circulating tumor cells (CTCs) are promising real-time, noninvasive biomarkers to assess therapy response and to monitor disease. However clinical applications of CTCs in urothelial carcinomas (UC) remain limited. There is a need for sensitive and disease-specific CTC assay for urothelial cancer. As CTCs are very rare in blood compared to blood cells and may vary due to tumor heterogeneity, detection and isolation of them solely based on one biomarker (e.g., EpCAM) would be insufficient. To achieve better results and increase clinical relevance, we designed a CTC capture device that was functionalized with multiple antibodies specific to UC. Methods: We engineered a novel UC-CTC capture device using nanotechnology and biomimetic cell rolling. To capture UC cells, we examined 4 biomarkers: EpCAM, Nectin-4, Trop-2, and FGFR3, that are highly expressed in UC. We engineered CTC assays using single and multi-biomarker capture surfaces. The capture sensitivity and selectivity of the device were investigated using four bladder cancer cell lines as well as a leukocyte model, HL-60, as negative control. CTCs are defined as CK+/DAPI+/CD45- cells. The capture sensitivity was compared between the different surfaces with each of the different antibodies and the final UC-CTC chip with optimized antibody mix was chosen to detect CTCs from UC patients. Results: We examined the CTC capture using 4 UC cell lines: UM-UC-1, T-24, HT1376, RT4. The devices that utilized single antibodies were much less sensitive (capture efficiencies less than 82%). In comparison, combination antibody capture improved sensitivity to &amp;gt;90%. Negative control using the HL-60 cells showed minimal non-specific binding, &amp;lt;0.3%. We were also able to validate the CTC assay using patient samples and demonstrated high sensitivity. For example, we captured ~302 CTCs/mL in a patient who had early-stage T2 muscle invasive UC. Conclusion: We developed a novel CTC capture device using four UC-specific capture antibodies for the detection and isolation of CTCs in UC. Compared to the configuration of EpCAM alone, the mixture of multiple antibodies improved the capture efficiency up to 1.4-fold for in vitro bladder cancer cell spiking assays. High specificity was also achieved, with low levels of non-specific leukocyte detection. High sensitivity and specificity suggest that this platform has potential impact to detect CTCs from patients with UC. Citation Format: Qinhan Zhou, Michael Poellmann, Nikhila Reddy Sultanpuram, Damla Gunenc, Krista English, Luke Chen, Runze Zhao, Seungpyo Hong, Andrew Wang, Tian Zhang. Engineering and evaluation of a novel circulating tumor cell capture assay for urothelial carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2296.

Multi-criteria assessment of a new energy-saving and environmentally-friendly scheme for natural gas-fired power plants

Recently, natural gas-fired power plants have received wide attention for their environmental advantages. However, high-temperature waste gas causes a quantity of heat loss, and a large amount of carbon dioxide emissions exacerbate the greenhouse effect. In order to efficiently utilize the waste heat of flue gas and reduce the carbon dioxide emissions from power plants, a cascade system which combines waste heat recovery technologies, including the supercritical carbon dioxide (SCO2) cycle and trilateral organic cycle (TOC), with carbon dioxide capture device is proposed. After validating the accuracy of thermodynamic model, the optimum working fluid for the trilateral organic cycle is selected and some main factors affecting the thermodynamic performance are analyzed in detail. Finally, to test the feasibility of the design, this paper optimizes the economy performance of the cascade system and the amount of captured CO2. The results show that the maximum net output power, carbon dioxide capture and payback period of the system are 157.1 MW, 96.49 ton/h and 4.125 years. Moreover, compared with original power plant design, the net plant efficiency of the cascade system has a significantly increasing of 23.6%. Hence, the new scheme proposed in this paper is a feasible method to achieve the aim of energy conservation and emission reduction.

Design of Wave Energy Capture Structure and Mooring System with Additional Mass

There is huge amounts of wave energy in ocean, and how to convert its unstable energy into stable and sustainable output energy is a major problem that needs to be solved urgently. The power production performance of a wave energy capture device is closely related to its anti-wave performance, so the design of the mooring system is very important for it, In this paper, a truss-type three-sided wave energy capture device is used, and the mooring system with additional mass is used for research. The study found that the mooring system with additional mass blocks could significantly restrain the platform’s surge displacement under wind and wave loads.

인도적 들개 포획 방법 및 도구의 활용

Feral dogs are not friendly to people and are very difficult to capture, unlike stray dogs in Korea. In addition, various problems caused by feral dogs are on the rise, so capture and adoption are urgently needed, but it is difficult to capture. The use of the COLLARUM® Canine Capture Device is considered to be a very useful live capture tool in the domestic situation where it is difficult to capture feral dogs. This capture tool meets international humanitarian capture standards and is already widely used in Australia and other places where feral dogs cause ecological problems, so its effectiveness and safety have been verified. It is easy to install and move, so it is possible to install many of them in places where it is difficult to use a capture frame, such as a mountain area, which is the main wild dog habitat in Korea. As described above, COLLARUM® can be installed in almost any place, including mountainous areas, so it is possible to safely capture feral dogs as well as stray dogs, enabling effective management of abandoned animals. Therefore, it is considered to be the equipment that should be most actively considered for domestic introduction and local government distribution in order to manage the population of wild dogs and increase the capture rate.