Capture Device Research Articles

Portable and Rapid Fluorescence Turn-On Detection of Total Pepsin in Saliva Based on Strong Electrostatic Interactions.

Salivary pepsin has been proposed as a promising diagnostic marker for gastroesophageal reflux disease (GERD). However, the activity of human pepsin is strongly influenced by pH, and the acidic condition (pH ∼ 2) is optimal, which is a contradiction for the pepsin detection kit based on its catalytic activity. Thus, its accurate quantification in saliva (neutral pH) by readily rapid tools with simplicity and low cost is still challenging. Herein, a convenient fluorescence assay has been developed for the rapid detection of pepsin at neutral pH based on its electrostatic interaction with SYBR Green (SG) rather than the bioactivity. At neutral pH, the positively charged SG fluorophore can be effectively adsorbed by the negatively charged pepsin due to the low isoelectric point (pI) and large molecular size of pepsin. Thus, the molecular rotation of SG is limited, and its fluorescence intensity is significantly increased. The strategy was further confirmed to have the same fluorescence response as that of normally active and inactivated pepsin. Due to the unique pI of pepsin, the fluorescence strategy is highly selective for pepsin compared to other proteins. On the basis of this strategy, a smartphone-based fluorescence capture device integrated with a programmed Python program was fabricated for both color recognition and the accurate detection of pepsin within 3 min. Under the optimal conditions, this turn-on sensor allowed for the on-site analysis of pepsin with a detection limit of 0.2 μg/mL. Moreover, this strategy was successfully used to assess salivary pepsin to aid in the noninvasive diagnosis of GERD.

A Physical Fatigue Evaluation Method for Automotive Manual Assembly: An Experiment of Cerebral Oxygenation with ARE Platform

Due to the complexity of the automobile manufacturing process, some flexible and delicate assembly work relies on manual operations. However, high-frequency and high-load repetitive operations make assembly workers prone to physical fatigue. This study proposes a method for evaluating human physical fatigue for the manual assembly of automobiles with methods: NIOSH (National Institute for Occupational Safety and Health), OWAS (Ovako Working Posture Analysis System) and RULA (Rapid Upper Limb Assessment). The cerebral oxygenation signal is selected as an objective physiological index reflecting the human fatigue level to verify the proposed physical fatigue evaluation method. Taking auto seat assembly and automobile manual assembly as an example, 18 group experiments were carried out with the ARE platform (Augmented Reality-based Ergonomic Platform). Furthermore, predictions of metabolic energy expenditure were performed for experiments in Tecnomatix Jack. Finally, it is concluded that the proposed physical fatigue evaluation method can reflect the human physical fatigue level and is more accurate than the evaluation of metabolic energy consumption in Tecnomatix Jack because of the immersion that comes with the AR devices and the precision that comes with motion capture devices.

Modified polyethyleneimine as additive for enhanced direct air capture via membrane contactors

This paper reports the preparation and characterization of asymmetric membranes for direct air capture (DAC) in polysulfone membrane contactors. In the previous studies it was found that blending the polysulfone with modified Lupasol G20 improved the polysulfone membrane performance; however, the hydrophobicity and stability of the additive require improvement for the application in gas/liquid system. In order to achieve this objective, in the current study, Lupasol G20 was grafted with phenyl isocyanate via urea linkages in a solvent-free and eco-friendly method. Membranes with different amount of the additive were prepared by phase inversion and characterized by several techniques in terms of morphology, porosity, wettability, and stability in aqueous KOH solution. Finally, CO2 solubility and mass transfer coefficient were determined and correlated with membranes’ performance in the direct air capture device. In general, it was found that the presence of the additive greatly improved the characteristics of the membranes in terms of CO2 capture efficiency and robustness, outperforming the results obtained previously. The best results for were obtained with 5% additive content reaching 0.18 [mol/m2s].

Unraveling the potential of breath and sweat VOC capture devices for human disease detection: a systematic-like review of canine olfaction and GC-MS analysis.

The development of disease screening methods using biomedical detection dogs relies on the collection and analysis of body odors, particularly volatile organic compounds (VOCs) present in body fluids. To capture and analyze odors produced by the human body, numerous protocols and materials are used in forensics or medical studies. This paper provides an overview of sampling devices used to collect VOCs from sweat and exhaled air, for medical diagnostic purposes using canine olfaction and/or Gas Chromatography-Mass spectrometry (GC-MS). Canine olfaction and GC-MS are regarded as complementary tools, holding immense promise for detecting cancers and infectious diseases. However, existing literature lacks guidelines for selecting materials suitable for both canine olfaction and GC-MS. Hence, this review aims to address this gap and pave the way for efficient body odor sampling materials. The first section of the paper describes the materials utilized in training sniffing dogs, while the second section delves into the details of sampling devices and extraction techniques employed for exhaled air and sweat analysis using GC-MS. Finally, the paper proposes the development of an ideal sampling device tailored for detection purposes in the field of odorology. By bridging the knowledge gap, this study seeks to advance disease detection methodologies, harnessing the unique abilities of both dogs and GC-MS analysis in biomedical research.

Dynamic evolution of locomotor performance independent of changes in extended phenotype use in spiders.

Many animals use self-built structures (extended phenotypes) to enhance body functions, such as thermoregulation, prey capture or defence. Yet, it is unclear whether the evolution of animal constructions supplements or substitutes body functions-with disparate feedbacks on trait evolution. Here, using brown spiders (Araneae: marronoid clade), we explored if the evolutionary loss and gain of silken webs as extended prey capture devices correlates with alterations in traits known to play an important role in predatory strikes-locomotor performance (sprint speed) and leg spination (expression of capture spines on front legs). We found that in this group high locomotor performance, with running speeds of over 100 body lengths per second, evolved repeatedly-both in web-building and cursorial spiders. There was no correlation with running speed, and leg spination only poorly correlated, relative to the use of extended phenotypes, indicating that web use does not reduce selective pressures on body functions involved in prey capture and defence per se. Consequently, extended prey capture devices serve as supplements rather than substitutions to body traits and may only be beneficial in conjunction with certain life-history traits, possibly explaining the rare evolution and repeated loss of trapping strategies in predatory animals.

Risks that induce bicycle accidents: Measurement and analysis of bicyclist behavior while going straight and turning right using a bicycle simulator

In 2021, 69 % of those who died in Japan while riding a bicycle were of an older age. Preventing such accidents is an important issue because older bicyclists are more likely to be fatally injured in an accident. We developed a bicycle simulator that can safely simulate dangerous traffic environments by combining VR technology, motion capture technology, and various sensors to investigate the driving behavior of bicyclists who are involved in accidents. The simulator measures operating data such as floor reaction force, wheel rotation angle, brake lever displacement, position and posture of motion capture devices, and position and posture of the bicycle and vehicles at 60 Hz. We projected images of the virtual traffic environment to reflect the driving behavior in real-time onto three screens installed at the front, right side, and rear of the bicycle using three projectors. The constructed simulator was implemented for a “crossing inspection” where a rider checks for safety in front and behind while riding down a straight road with one lane and crossing this roadway and compared this to a “tracing inspection” where a model route is traced using a previously measured safe route taken by a younger rider. We measured and analyzed the bicyclist behaviors of nine younger and six older participants during two types of inspections as a usability study. In the crossing inspection, the crash rate for the older riders in the near lane was 5.3 times higher than that for the young. Riders collided with vehicles exhibited minimal rearward checking angles before entering a roadway, extended durations from the end of the last rearward check to roadway entry, and considerable standard deviations during road crossings. Moreover, in the tracing inspection, younger participants demonstrated superior trajectory passage rates and maintained closer proximity to the model, whereas older participants manifested diminished trajectory passage rates and remained at more extended distances from the model. Notably, a declining trajectory passage rate corresponded to an increase in the standard deviation of the crossing route and the time taken to enter the roadway.

Distributed low-carbon economic dispatch of integrated power and transportation system

Electric vehicles (EVs) are widely recognized as promising solutions to reduce carbon emissions from the transportation sector. The rapid proliferation of EVs intensifies the interdependency between the power distribution system (PDS) and transportation system (TS). This paper proposes a distributed low-carbon economic dispatch scheme for the integrated power and transportation system (IPTS). Carbon emissions from the TS are quantified by a carbon dioxide calculation model, and the emissions from the PDS are determined by the electricity generation of fossil-fueled generators and the carbon reduction of carbon capture and storage (CCS) devices. An integrated model reformulation method is employed to transform the original non-convex model into a tractable one. Then a distributed solution method based on the spectral penalty parameter based adaptive alternating direction method of multipliers (SPPA-ADMM) is developed to protect the privacy of the PDS and TS. The reformulated model is solved iteratively, and the penalty parameter of the SPPA-ADMM is adjusted adaptively to improve the convergence performance of the method. Numerical results on three test systems demonstrate the effectiveness of the proposed scheme in achieving low-carbon economic dispatch of IPTS. Moreover, case studies validate the superiority of the SPPA-ADMM based distributed solution method in computation performance.

An improved memetic algorithm with Q-learning for low carbon economic scheduling of cogeneration system

A low-carbon economic scheduling method based on a Q-learning-based multiobjective memetic algorithm (Q-MOMA) is proposed to improve the economy of cogeneration system scheduling and reduce carbon emission. First, the model incorporates a carbon capture device, a heat storage device, and a demand response mechanism to enhance the system’s flexibility and wind power consumption. In addition, the Q-MOMA algorithm combines global and local search and uses a Q-learning algorithm to dynamically adjust the crossover and mutation probabilities to improve the algorithm’s searchability. Finally, the fuzzy membership function method is used to make a multiobjective decision, which balances the economy and low carbon of the system, and a compromise scheduling scheme is given. The effectiveness of the proposed model and solution method is verified through the simulation calculation of the improved system and compared with the simulation results of various optimization algorithms. The simulation results show that the proposed model can improve the wind power consumption space and the system’s economy and reduce carbon emissions. The Q-MOMA algorithm has a relatively better optimization ability in the low-carbon economic scheduling of the cogeneration system.

Multi‐stage stochastic dual dynamic programming to low‐carbon economic dispatch for power systems with flexible carbon capture and storage devices

AbstractThe carbon capture and storage (CCS) technique gains much attention due to its role in reducing CO2 emissions. By introducing flexible CCS devices into conventional power plants, the low‐carbon economic operation of the power system can be achieved. However, the uncertainty of renewable energy makes it hard to obtain an optimal operation policy. First, the detailed model of CCS consisting of bypass venting stacks, solvent storage tanks, absorber, and stripper is described. Then, a low‐carbon economic dispatch model of the power system with CCS is proposed to minimize the total operation and renewable energy abandonment penalty costs. Next, the problem is reformulated as multi‐stage stochastic programming, and the stochastic dual dynamic programming (SDDP) method is applied to relieve the computation burden. To control the probability of stopping the algorithm prematurely, a new stopping criterion based on a two‐sided hypothesis test is proposed. Finally, the effectiveness of the proposed model and the new stopping criterion is demonstrated by case studies on a practical power system.

Can manipulating control‐display ratio dynamically really work in changing pseudo‐haptic weight?

AbstractDue to the limited hardware available in virtual reality, such as VR controllers or simple motion capture devices, users will need more tactile feedback, like the weight of virtual objects. Pseudo‐tactile feedback, such as the control display (C/D) ratio manipulation method, is considered a standard method to simulate weight perception. In past studies, this method was basically used in a static environment, and the C/D ratio was usually determined initially. Can this method still work if the C/D ratio changes in dynamic usage scenarios? In a series of experiments, we tried to answer this question. We proved that the dynamic change of C/D ratio could simulate the weight change and improve the sense of embodiment through another hand redirection method.

Max Feature Map CNN with Support Vector Guided Softmax for Face Recognition

Face recognition has made significant progress because of advances in deep convolutional neural networks (CNNs) in addressing face verification in large amounts of data variation. When image data comes from different sources and devices, the identifiability of other classes and the presence of profile face data can lead to inaccurate and ambiguous classification because other classes lack discriminatory power. Furthermore, using a complex architecture with many deep convolutional layers can become very slow in the training process due to a huge amount of Random Access Memory (RAM) usage during the reverse pass of backpropagation. In this paper, we design a light CNN architecture that addresses these challenges. Specifically, we implemented Max-feature-map (MFM) into each convolutional layer to improve the accuracy and efficiency of the CNN. The strength of the support vector-guided SoftMax (SV-SoftMax) is also used in the proposed method to emphasize misclassified points and adaptively guide feature learning. Experimental results show that the 9-Layers CNN with MFM layer and SV-SoftMax outperform VGG-19 with 96.22% validation accuracy and the second rank below FaceNet tested on the same dataset with fewer parameters. Moreover, the model performed well on data that is obtained from various capture devices such as webcam, CCTVs, phone cameras, and DSLR cameras. The implications of this research could extend to scenarios requiring face recognition technology implementation with light size, such as surveillance and authentication systems

Development and Testing of a Data Capture Device for Use With Clinical Incentive Spirometers: Testing and Usability Study.

The incentive spirometer is a basic and common medical device from which electronic health care data cannot be directly collected. As a result, despite numerous studies investigating clinical use, there remains little consensus on optimal device use and sparse evidence supporting its intended benefits such as prevention of postoperative respiratory complications. The aim of the study is to develop and test an add-on hardware device for data capture of the incentive spirometer. An add-on device was designed, built, and tested using reflective optical sensors to identify the real-time location of the volume piston and flow bobbin of a common incentive spirometer. Investigators manually tested sensor level accuracies and triggering range calibrations using a digital flowmeter. A valid breath classification algorithm was created and tested to determine valid from invalid breath attempts. To assess real-time use, a video game was developed using the incentive spirometer and add-on device as a controller using the Apple iPad. In user testing, sensor locations were captured at an accuracy of 99% (SD 1.4%) for volume and 100% accuracy for flow. Median and average volumes were within 7.5% (SD 6%) of target volume sensor levels, and maximum sensor triggering values seldom exceeded intended sensor levels, showing a good correlation to placement on 2 similar but distinct incentive spirometer designs. The breath classification algorithm displayed a 100% sensitivity and a 99% specificity on user testing, and the device operated as a video game controller in real time without noticeable interference or delay. An effective and reusable add-on device for the incentive spirometer was created to allow the collection of previously inaccessible incentive spirometer data and demonstrate Internet-of-Things use on a common hospital device. This design showed high sensor accuracies and the ability to use data in real-time applications, showing promise in the ability to capture currently inaccessible clinical data. Further use of this device could facilitate improved research into the incentive spirometer to improve adoption, incentivize adherence, and investigate the clinical effectiveness to help guide clinical care.

Методика определения вероятности захвата асимметричных деталей формы тел вращения в дисковых бункерных загрузочно-ориентирующих устройствах

Modern equipment in the product assembly should be the automatically loaded reliable systems, where the hopper loading-orientation device is the main component. Recently, there appeared a lot of asymmetric parts, including those with implicit asymmetry. In this connection, it becomes necessary to design such capture devices that are optimal in all the parameters. However, the probabilistic operation principle and the device ability to operate only in capturing a specific part with certain geometric parameters significantly complicate their design and require solution to the complex multi-level tasks. The most important task is to determine its performance, namely the probability of capturing the parts in the hopper loading-orientation device. The paper presents a detailed methodology making it possible to determine the probability of capturing the parts in the bunker loading-orientation devices of various types that implement different methods in capturing and orienting parts with the revolution body shape of a wide range, both with implicit and explicit asymmetry, for constructing the performance mathematical model. To build a mathematical model of the capture probability, probabilities of finding the part in the favorable position for capturing it with its differing orientations, limiting circumferential speed of the capturing bodies of the hopper loading-orientation device and absence of interference from the interlocking parts were determined. Dependencies are provided that are making it possible to graphically determine the indicated parameters of the mathematical model

A novel approach to the investigation and quantification of the stop/start process for pedestrian traffic using motion capture devices

Characterising the stop/start walking process of individuals in a crowded and congested space is an important consideration in modelling pedestrian movement. However, the reaction of pedestrians to speed changes, especially to the person in front, has not been fully characterised nor quantified for full adoption in computer models. This study, therefore, explored the different phases of the stop/start process through a series of novel experiments conducted at University College Dublin (UCD) in which individual movements were captured precisely using motion capture equipment. The overall aim of the study was to develop a novel methodology (inspired from vehicle traffic flow) to break down and quantify the components of the stop/start walking process, i.e., the perception-reaction time, slow-down time, and start-up time of individuals walking in a single-file. These times, together with the total stopping distance and inter-person distances of each follower to their leader at the beginning and the end of each phase were quantified successfully and their inter-relationships were explored. The results showed the mean perception-reaction time, slow-down time, and start-up time delay were 0.48, 0.58, and 0.39 s, respectively. Where applicable, normal, lognormal, Weibull, gamma, and log-logistic probability distributions were fitted to the data to determine the best fit. The novel methodology developed in this study can be used in the future to investigate pedestrians’ behaviour in response to any changes in leaders’ speed, i.e. quantify the reaction of individuals in different phases. The results of this study can inform the representation of the stop/start process in microscopic pedestrian models.

Microplastics and Tire Wear Particles in Urban Stormwater: Abundance, Characteristics, and Potential Mitigation Strategies.

Stormwater has been identified as a pathway for microplastics (MPs), including tire wear particles (TWPs), into aquatic habitats. Our knowledge of the abundance of MPs in urban stormwater and potential strategies to control MPs in stormwater is still limited. In this study, stormwater samples were collected from microlitter capture devices (inlet and outlet) during rain events. Sediment samples were collected from the material captured in the device and from the inlet and outlet of a constructed stormwater wetland. MP (>25 μm) concentration in stormwater varied across different locations ranging from 3.8 to 59 MPs/L in raw and 1.8 to 32 MPs/L in treated stormwater, demonstrating a decrease after passage through the device (35-88% removal). TWPs comprised ∼95% of all particles, followed by polypropylene (PP) and poly(ethylene terephthalate) (PET). The concentration of TWPs ranged from 2.5 to 58 TWPs/L and 1450 to 4740 TWPs/kg in stormwater and sediment, respectively. A higher abundance of MPs was found in the sediment at the inlet of the constructed wetland compared to the outlet, indicating a potential role of wetlands in removing MPs from stormwater. These findings suggest that both constructed wetlands and microlitter capture devices can mitigate the transport of MPs from stormwater to the receiving waterways.

Bimanual Asymmetric Coordination in a Three-Dimensional Zooming Task Based on Leap Motion: Implications for Upper Limb Rehabilitation

This paper explores the practicability of bimanual asymmetric interaction and the factors affecting it when manipulating virtual three-dimensional objects through Leap, a novel hand motion capture device. This research has the potential to aid in the rehabilitation of people with upper limb function impairments. The participants were asked to enlarge a virtual three-dimensional box device to a predefined specified scale. Three influencing factors were addressed, i.e., task difficulty, task allocation and interactive mode. The results indicate that all factors have significant effects on movement time. However, analysis of variance tests shows that there are significant effects on the error rate and spatial patterns due to task difficulty and interactive mode, while no significant effects are found for task allocation. Additionally, task allocation is observed to have significant effects on the time of each hand from zooming phase onset to peak velocity. The action of the nondominant hand is coarser than that of the dominant hand. Interestingly, the velocities of both hands synchronized as the task difficulty level increased, even though the limbs moved at quite different speeds in the initial stage. This research provides insights into how one hand coordinates with the other in terms of the temporal aspects of movement kinematics and thus can help in designing rehabilitative devices that interact with the healthy hand.

Paper-assisted ratiometric fluorescent sensors for on-site sensing of sulfide based on the target-induced inner filter effect

Dissolved sulfide tends to species transformation and loss upon leaving the matrix, thus the development of a practical on-site determination of sulfide is crucial for environmental monitoring and human health. In this work, a novel paper-based ratiometric fluorescence sensor was developed for the field analysis of sulfide, which system was constructed by the inner filter effect (IFE) of CdS quantum dots (QDs) toward carbon dots (C-dots). Instead of an aqueous phase system, the conversion of sulfide to its hydride would induce the in-situ formation of CdS QDs on the paper, which acted as an energy acceptor to quench the emission of C-dots, leading to a variation of ratiometric fluorescence from blue to yellow with the increasing concentration of sulfide. Moreover, we proposed a smartphone-based fluorescence capture device integrated with a programmed Python program, accomplishing both color recognition and accurate detection of sulfide. Under the optimal condition, this ratiometric fluorescence sensor allowed for the on-site analysis of sulfide with a limit of detection of 0.05 μM. The accuracy of the sensor was validated via the successful field analysis of environmental water samples with satisfactory recoveries. Compared to other fluorescence methods used for sulfide analysis, this developed system retains the advantages of label-free, low-cost, ease of operation, and miniaturization, showing great potential for the measurement of sulfide on-site, as well as environmental monitoring.

Motion capture device reveals a quick learning curve in vascular anastomosis training.

Surgical procedures are often evaluated subjectively, and an objective evaluation has been considered difficult to make and rarely reported, especially in open surgery, where the range of motion is wide. This study evaluated the effectiveness of surgical suturing training as an educational tool using the Leap Motion Controller (LMC), which can capture hand movements and reproduce them as data comprising parametric elements. We developed an off-the-job training system (Off-JT) in our department, mainly using prosthetic grafts and various anastomotic methodologies with graded difficulty levels. We recruited 50 medical students (novice group) and 6 vascular surgeons (expert group) for the study. We evaluated four parameters for intraoperative skills: suturing time, slope of the roll, smoothness, and rate of excess motion. All 4 parameters distinguished the skill of the novice group at 1 and 10h off-JT. After 10h of off-JT, all 4 parameters of the novices were comparable to those of the expert group. Our education system using the LMC is relatively inexpensive and easy to set up, with a free application for analyses, serving as an effective and ubiquitous educational tool for young surgeons.

System for classification of human gaits using markerless motion capture sensor

PurposeHuman gait analysis is based on a significant part of the musculoskeletal, nervous and respiratory systems. Gait analysis is widely adopted to help patients increase community involvement and independent living.Design/methodology/approachThis paper presents a system for the classification of abnormal human gaits using a Markerless 3D Motion Capture device. This study aims at examining and estimating the spatiotemporal and kinematic parameters obtained by 3D gait analysis in diverse groups of gait-impaired subjects and compares the parameters with that of healthy participants to interpret the gait patterns.FindingsThe classification is based on mathematical models that distinguish between normal and abnormal gait patterns depending on the deviations in the gait parameters. The difference between the gait measures of the control and each disease group was examined using 95% limits of agreement by the Bland and Altman method. The scatter plots demonstrated gait variability in Parkinsonian and ataxia gait and knee joint angle variation in hemiplegic gait when compared with those of healthy controls. To prove the validity of the Kinect camera, significant correlations were detected between Kinect- and inertial-based gait tests.Originality/valueThe various techniques used for gait assessments are often high in price and have existing limitations like the hindrance of components. The results suggest that the Kinect-based gait assessment techniques can be used as a low-cost, less-intrusive alternative to expensive infrastructure gait lab tests in the clinical environment.

Validation of Estimators for Weight-Bearing and Shoulder Joint Loads Using Instrumented Crutches.

This research paper aimed to validate two methods for measuring loads during walking with instrumented crutches: one method to estimate partial weight-bearing on the lower limbs and another to estimate shoulder joint reactions. Currently, gait laboratories, instrumented with high-end measurement systems, are used to extract kinematic and kinetic data, but such facilities are expensive and not accessible to all patients. The proposed method uses instrumented crutches to measure ground reaction forces and does not require any motion capture devices or force platforms. The load on the lower limbs is estimated by subtracting the forces measured by the crutches from the subject's total weight. Since the model does not consider inertia contribution in dynamic conditions, the estimation improves with low walking cadence when walking with the two-point contralateral and the three-point partial weight-bearing patterns considered for the validation tests. The shoulder joint reactions are estimated using linear regression, providing accurate values for the forces but less accurate torque estimates. The crutches data are acquired and processed in real-time, allowing for immediate feedback, and the system can be used outdoors in real-world walking conditions. The validation of this method could lead to better monitoring of partial weight-bearing and shoulder joint reactions, which could improve patient outcomes and reduce complications.