Tracking Applications Research Articles

Focusing Area on Advertising: An Eye Tracking Application

This study investigates the impact of eye-tracking technology in advertising, with a focus on gender-based analysis. Marketing strategies have evolved, adapting to technological advancements and shifting consumer behaviors. The current phase, Marketing 5.0, leverages technologies like artificial intelligence, augmented reality, and virtual reality to create data-driven and personalized marketing experiences. Human-computer interaction (HCI) has significantly benefited from these technological developments, particularly through the use of eye-tracking technology. This technology provides valuable insights into where individuals focus their attention on a target object or stimulus, revealing subconscious preferences and behaviors. The research presented in this article aims to determine the effectiveness of call-to-action elements in advertisements and whether there is a gender-based difference in attention to these elements. The study utilizes eye-tracking technology to measure viewer engagement with various components of an advertisement, including the human face, text, and call-to-action prompts. The findings suggest that there are indeed gender-based differences in how viewers interact with these elements, with males focusing more on call-to-action parts and females on the human face. The implications of this research are significant for advertisers and marketers, as understanding these differences can lead to more effective and targeted advertising strategies. By recognizing the distinct ways in which different genders process visual information, campaigns can be tailored to maximize impact and drive desired behavioral outcomes.

Development of Thermoplastic Bi-Component Electrodes for Triboelectric Impact Detection in Smart Textile Applications.

Smart textiles provide a significant technological advancement, but their development must balance traditional textile properties with electronic features. To address this challenge, this study introduces a flexible, electrically conductive composite material that can be fabricated using a continuous bi-component extrusion process, making it ideal for sensor electrodes. The primary aim was to create a composite for the filament's core, combining multi-walled carbon nanotubes (MWCNTs), polypropylene (PP), and thermoplastic elastomer (TPE), optimised for conductivity and flexibility. This blend, suitable for bi-component extrusion processes, exemplifies the role of advanced materials in combining electrical conductivity, mechanical flexibility, and processability, which are essential for wearable technology. The composite optimisation balanced MWCNT (2.5, 5, 7.5, and 10 wt.%) and TPE (0, 25, and 50 wt.%) in a PP matrix. There was a significant decrease in electrical resistivity between 2.5 and 5 wt.% MWCNT, with electrical resistivity ranging from (7.64 ± 4.03)104 to (1.15 ± 0.10)10-1 Ω·m. Combining the composite with 25 wt.% TPE improved the flexibility, while with 50 wt.% TPE decreased tensile strength and hindered the masterbatch pelletising process. The final stage involved laminating the composite filament electrodes, with a 5 wt.% MWCNT/PP/(25 wt.% TPE) core and a TPE sheath, into a textile triboelectric impact detection sensor. This sensor, responding to contact and separation, produced an output voltage of approximately 5 V peak-to-peak per filament and 15 V peak-to-peak with five filaments under a 100 N force over 78.54 cm2. This preliminary study demonstrates an innovative approach to enhance the flexibility of conductive materials for smart textile applications, enabling the development of triboelectric sensor electrodes with potential applications in impact detection, fall monitoring, and motion tracking.

A Low-Cost and Low-Power Consumption-Based Dual-Band Active Antenna for Vehicle Tracking Applications

This paper presents a cost-effective dual-band active antenna solution with low-power requirements, specifically designed for navigation applications utilizing the Indian Constellations (NavIC) and GPS signals. The antenna features a compact single-layer patch with capacitors, designed to operate at the L1 and L5 bands. In this design, the TM10 and TM30 modes are effectively utilized to achieve a dual-band broadside radiation pattern. The locations of the slots, their dimensions, and capacitors are optimized to lower the TM30 mode frequency band along with achieving right-hand circularly polarized (RHCP). The microstrip-printed hybrid resonator diplexer, the RF choke-based bias tee, and the two-stage LNA along with a matching circuit are designed separately and then integrated into a compact 45 mm × 45 mm (0.17λ × 0.17λ) active circuit board size. The routing of DC biasing and RF lines is optimized to minimize cross-talk and ensure that the active circuit remains free from spurious signal generation while enhancing the overall performance in terms of gain and noise figure. The proposed two-stage active circuit exhibits a gain of 44 dB and 42 dB, with corresponding noise figures of 1.67 dB and 1.1 dB at the L1 and L5 band frequencies, respectively. It operates with a power consumption of 85 mW at a supply voltage of 5 V. The integrated active antenna is developed on an FR4 substrate and evaluated with an in-house developed GNSS receiver, demonstrating a measured carrier-to-noise ratio (C/No) exceeding 40 dB-Hz across both frequency bands.

NIR AIE luminogens for primary and metastasis tumor imaging and tracking applications

Modern lifestyle changes, including irregular diets and late-night activities, have contributed to a significant rise in cancer rates, particularly among younger demographics, highlighting the pressing need for early detection and treatment.

Design and development of hexagonal-shaped copper and liquid metamaterial-loaded superstrate patch antenna for 5G, WLAN, tracking and detection applications

Design and development of hexagonal-shaped copper and liquid metamaterial-loaded superstrate patch antenna for 5G, WLAN, tracking and detection applications

Design and Implementation of a Rechargeable Active/Passive RFID Tag System with Integrated Software Interface for Construction Site Equipment Tracking

Radio Frequency Identification (RFID) technology has undergone continuous development since its origin in the 1940s, finding applications across various fields. This research aimed to design and implement a rechargeable active/passive RFID Tag system operating at 433MHz, with a specific focus on its application in construction site equipment tracking. The objectives were to develop an RFID Tag capable of switching between active and passive operation modes, wirelessly recharging the battery system, and demonstrating its effectiveness in managing construction equipment. The designed Tag operates in both active and passive modes, ensuring operation even after battery depletion. Data exchange uses backscattering modulation, allowing transmission without relying on an external power supply. The Tag was implemented using an ESP8266-01 encoder, envelope detection circuit for mode switching, signal amplification unit, impedance-matched antenna, and a rechargeable power supply unit. Testing evaluated data transmission, reception, signal reliability, and distance coverage in both modes. Results showed effective performance up to 50 meters in active mode and 30 meters in passive mode. The system's application in construction site equipment tracking demonstrated significant potential for improving asset utilization, enhancing security, and streamlining inventory management. Additionally, a software interface was developed and tested, showcasing the system's ability to present real-time tracking data in both tabular and graphical formats, further enhancing its practical applicability. This research contributes to advancing RFID technology by introducing a versatile, rechargeable Tag system with practical applications in challenging environments like construction sites, complemented by user-friendly software solutions for effective asset management

California Warehouse Industry Worker Injury Rates in the Occupational Health and Safety Administration's Injury Tracking Application, 2018-2023.

Occupational health and safety surveillance in the US relies primarily on federal and state administrative data sources which all have limitations created by underreporting and different sampling frames. To begin closing data gaps, in 2019 the federal Occupational Health and Safety Administration began requiring many US business establishments to submit injury and illness data to the Injury Tracking Application (ITA). We present an example use of these data by characterizing injuries in the California warehousing industry. Yearly ITA data for 2018-2023 were obtained and cleaned to exclude invalid records, resulting in 3717 records from California General Warehousing and Storage (GWS, NAICS 493110) establishments. Annual rates of total reportable injuries and cases of injuries requiring days of job transfer or restriction (DJTR) were calculated. Total injury rates peaked in 2021, with 5.87 injuries per 100 workers. While total injury rates subsequently declined, 2023 DJTR rates remained elevated over 2018 rates (3.21 vs. 1.84 per 100 workers). The primary analytical limitations were potential bias due to underreporting, and difficulty in identifying warehouse establishments. The ITA data are a valuable addition to existing administrative data sources, with several limitations similar to those in other administrative datasets. The findings of consistently elevated total and DJTR injury rates in California's warehousing industry are consistent with previous research and indicate that outreach and enforcement efforts should be expanded to protect the health and safety of warehouse workers.

Tutorial Booking and Tracking Application Interactivity

Electronic texts can be highly interactive. Forms of interactivity, such as hyperlinks, shortcuts or tags are not only signs to make meaning on the e-page but also space for actions and changing textual situations. Here, e-page interactivity and textual interaction become two different realms and make a gap between the text analysis and multimodal analysis. This case study aims to analyze the interactivity of the online tutorial booking and tracking application called ‘ ASP tutTrak’ by using a social semiotic multimodal framework for text and image analysis. The application is currently used in the Academic Success Center (ASC) of a tertiary level institution in the United Arab Emirates and was designed by the Information and Communication team of the same institution. The theoretical framework of the study presents multimodal social semiotics analysis of sites, signs and images of the ‘ASP tutTrak’ application through three metafunctions of communication (Halliday, 1978, Kress and Van Leeuwen, 1996, 2006). Since the application under consideration is in the academic domain, this study adapted Chou’s (2003) framework for interaction types for learner-interface and learner-content. Analysis of five sample pages indicate that the interactive meaning potentials of the digital text, interface and content are high and the application is interactive.

Advanced Smart Bracelet for Elderly: Combining Temperature Monitoring and GPS Tracking

Indonesia is entering an aging population period, marked by an increase in the number of elderly individuals, accompanied by a rise in dementia cases. This situation leads to higher dependency among the elderly on others for assistance or long-term care. Dementia can cause elderly people to lose their sense of direction, often wandering aimlessly, making them difficult to track. To address this issue, a wearable smart bracelet is proposed to monitor the location and a vital body parameter such as body temperature. The system is equipped with a tracking application that can send an alert if the user is outside a designated area. It automatically sends a warning message to the caregiver's or family member's smartphone when abnormal signs are detected. The bracelet is designed like a wristwatch, to be worn on the wrist. It is small, lightweight, and battery-operated. Temperature and location data can be transmitted in real-time using an internet network to mobile devices. The device can notify when the user is outside the specified area. Test results indicate that the device has high accuracy and reliability in monitoring location and body temperature with accuracy around 98.5%, as well as sending notifications through a Telegram bot when certain thresholds are exceeded. This device can work properly for up to 5 hours on a single battery charge. With this device, it is expected to help monitor and support the care of the elderly so that they can improve their quality of life. This device can also provide an emergency alarm if the elderly are outside the area.

Menstrual cup acceptability and functionality in real-world use: A cross-sectional survey of young people in Australia.

Menstrual cups offer a cost-effective and environmentally sustainable product for many young people. While clinical trials have shown their safety and effectiveness, no studies have investigated their performance in real-world use. To describe the acceptability and functionality (continuation, discomforts, leakage, and adverse events) of menstrual cups and investigate the supportive role of product knowledge. A cross-sectional online survey of 530 people aged 15-24 living in Australia who had ever used a menstrual cup, recruited via a menstrual cycle tracking application. More than half of participants (55%) were still using their first menstrual cup at the time of the survey, 16% had switched to an alternative cup, and 29% had discontinued use. In their first cycle of use, 54% of participants reported leakage and 25% reported pain or discomfort with the cup in place. Many participants (45%) reported being unable to remove the cup on their first attempt, with subsequently 17% requiring help to remove it, and 2% reported displacement of an intra-uterine device during removal. These figures decreased for subsequent cycles. Half of the participants were aware prior to using a cup that different cup models may offer a better fit for different individuals. This knowledge was associated with decreased odds of needing help to remove the cup (adjusted odds ratio 0.57, 95% CI 0.35-0.94) or discontinuing use (adjusted relative risk ratio 0.66, 95% CI 0.44-1.00). Difficulties using menstrual cups are common in real-world use and higher than reported in clinical trials. Improved education provision may support more positive user experiences.

Trajectory Planning With A* Algorithm and Tracking With Multiple Controllers

ABSTRACTThis article consists of mathematical modelling of differential drive mobile robot (DDMR), path planning and tracking application via multiple controller usage. Robot kinematic and dynamic models were constructed and simulated in virtual 2D map environment. A* algorithm was applied to the 2D map problem to acquire optimal pathfinding, efficiency and flexibility in trajectory planning stage. After the reference trajectory is obtained by algorithm, the robot needs to be guided using control strategy that manages the speed control of electric motors. At the first part of the control problem, kinematic based backstepping control (KBBC) were used to increase the efficiency of the control system. After that Sliding Mode Control (SMC) and Proportional‐Integral‐Derivative (PID) control strategies were applied to reduce tracking errors between reference and actual coordinate values and heading angle value. To test the robustness and efficiency of control combinations, two different simulation systems were designed using Matlab‐Simulink Software. First system was designed for the non‐disturbance condition, while the second system included disturbance torque and an additional mass applied condition. Test results showcasing the performance of the controllers are presented in the concluding section, through trajectory tracking and error comparison graphs.

Enhancing Radar Tracking Accuracy Using Combined Hilbert Transform and Proximal Gradient Methods

Accurate radar tracking is crucial in defense, navigation, and surveillance applications, where high precision and resilience to noise are essential. Traditional radar tracking techniques, such as Kalman Filters and Particle Filters, often struggle with performance limitations in noisy and non-linear environments, leading to inaccuracies in target tracking. To address these challenges, we propose a hybrid radar tracking approach combining the Hilbert Transform with the Proximal Gradient Method within a convex optimization framework. This combination leverages the Hilbert Transform's signal enhancement capabilities with the Proximal Gradient Method's optimization strength, improving accuracy and robustness under challenging conditions. Experimental results demonstrate that the proposed method achieves a 23% reduction in Mean Squared Error (MSE) and a 20% increase in tracking accuracy compared to conventional methods, alongside a Signal-to-Noise Ratio (SNR) of approximately 18.3 dB, indicating superior noise resilience. While the hybrid method offers significant improvements, it does involve increased computational complexity and may be sensitive to initial parameter settings, requiring careful tuning for optimal performance. Nevertheless, this method represents a promising advancement over traditional techniques, providing a more accurate and resilient solution for modern radar tracking applications.

Development of Artificial Intelligent-Based Methodology to Prepare Input for Estimating Vehicle Emissions

Machine learning has significantly advanced traffic surveillance and management, with YOLO (You Only Look Once) being a prominent Convolutional Neural Network (CNN) algorithm for vehicle detection. This study utilizes YOLO version 7 (YOLOv7) combined with the Kalman-based SORT (Simple Online and Real-time Tracking) algorithm as one of the models used in our experiments for real-time vehicle identification. We developed the “ISTraffic” dataset. We have also included an overview of existing datasets in the domain of vehicle detection, highlighting their shortcomings: existing vehicle detection datasets often have incomplete annotations and limited diversity, but our “ISTraffic” dataset addresses these issues with detailed and extensive annotations for higher accuracy and robustness. The ISTraffic dataset is meticulously annotated, ensuring high-quality labels for every visible object, including those that are truncated, obscured, or extremely small. With 36,841 annotated examples and an average of 32.7 annotations per image, it offers extensive coverage and dense annotations, making it highly valuable for various object detection and tracking applications. The detailed annotations enhance detection capabilities, enabling the development of more accurate and reliable models for complex environments. This comprehensive dataset is versatile, suitable for applications ranging from autonomous driving to surveillance, and has significantly improved object detection performance, resulting in higher accuracy and robustness in challenging scenarios. Using this dataset, our study achieved significant results with the YOLOv7 model. The model demonstrated high accuracy in detecting various vehicle types, even under challenging conditions. The results highlight the effectiveness of the dataset in training robust vehicle detection models and underscore its potential for future research and development in this field. Our comparative analysis evaluated YOLOv7 against its variants, YOLOv7x and YOLOv7-tiny, using both the “ISTraffic” dataset and the COCO (Common Objects in Context) benchmark. YOLOv7x outperformed others with a mAP@0.5 of 0.87, precision of 0.89, and recall of 0.84, showing a 35% performance improvement over COCO. Performance varied under different conditions, with daytime yielding higher accuracy compared to night-time and rainy weather, where vehicle headlights affected object contours. Despite effective vehicle detection and counting, tracking high-speed vehicles remains a challenge. Additionally, the algorithm’s deep learning estimates of emissions (CO, NO, NO2, NOx, PM2.5, and PM10) were 7.7% to 10.1% lower than ground-truth.

Sliding Mode Control and Its Applications in Robot Trajectory Tracking Control: Review

Sliding Mode Control (SMC), renowned for its robustness and anti-interference capabilities, is extensively applied in the realm of robot trajectory tracking. This paper reviews the application of SMC in robot trajectory tracking, and compares the performance differences between traditional SMC and improved SMC (including integral sliding mode control ISMC and adaptive neural network sliding mode control ANNSMC). Although traditional SMC is widely used in robot trajectory tracking due to its strong robustness, its high-frequency jitter problem cannot be ignored. In contrast, ISMC effectively reduces the system steady-state error, improves control accuracy and alleviates jitter by introducing an integral term. ANNSMC merges the nonlinear mapping capabilities of neural networks with the resilience of SMC, significantly enhancing trajectory tracking precision. This paper seeks to offer readers a comprehensive view of sliding mode control's application in robot trajectory tracking. It delivers a thorough evaluation of the strengths and weaknesses of different methods, serving as a valuable reference for future research.

Strain distribution prediction in UHPC beams using deep learning model

AbstractOver the last three decades, ultra‐high‐performance concrete (UHPC) has emerged as a highly innovative cementitious engineering material. This paper utilizes a distributed fiber optic sensor based on optical frequency domain reflectometry (OFDR) combined with the deep learning model to monitor and predict the strain distribution in UHPC T‐beams subjected to two point bending experiment. These sensors are deployed on the side surfaces of the UHPC T‐beams to capture the strain distribution when subjected to vertical loads, facilitating the determination of crack locations based on strain distribution peaks. While time series modeling is widely used in civil engineering to monitor potential damage using sensor data, its application in tracking and predicting known cracks is less explored. To address this gap, a Long Short‐Term Memory (LSTM) neural network model is developed to forecast the increase in the peak value of the strain distribution prior to structural damage, thus predicting crack initiation. The accuracy of the proposed LSTM model in predicting the UHPC strain distribution was thoroughly investigated. The results demonstrate excellent agreement between the predicted strain distributions and those detected by the ODISI 6000 monitoring system. The root‐mean‐square errors of the model‐predicted strains are generally below 10 με, with an average coefficient of determination (R2) reaching up to 98%, indicating a high degree of accuracy.

Design and Testing of a Tractor Automatic Navigation System Based on Dynamic Path Search and a Fuzzy Stanley Model

Smart agriculture development mainly depends on the intelligence and reliability of autonomous agricultural machinery. Automatic navigation systems (ANSs) play a key role in intelligent agricultural machinery design, as they not only reduce farmers’ workloads but also improve their land utilization rates. In this paper, a tractor ANS based on dynamic path search and a fuzzy Stanley model (FSM) was designed, and its capability for whole-field path tracking was tested. First, the tracking performance of the steering control module was validated after the automatic reconstruction of the tractor platform. Then, a navigation decision system was established based on a unified reference waypoint search framework, where the path generation for whole-field coverage was presented. Finally, the gain coefficient of the Stanley model (SM) was adjusted adaptively according to the tracking error by utilizing the fuzzy logic controller. Subsequently, the developed tractor ANS was tested in the field. The experiment’s results indicate that the FSM outperformed the SM in straight path tracking and whole-field path tracking. When the tractor traveled at a speed of 1 m/s, the maximum lateral tracking error for the straight path was 10 cm, and the average lateral tracking error was 5.2 cm, showing improvements of 16.7% and 10.3% compared to the SM. Whole-field autonomous navigation showed that the maximum lateral tracking error was improved from 34 cm for the SM to 27 cm for the FSM, a reduction of approximately 20.6%, illustrating the superiority of the FSM in the application of whole-field path tracking. As the maximum tracking error of whole-field autonomous navigation appears in the turning stage, where tractors often stop working, the designed ANS satisfies the requirements of a self-driving system for unmanned tractors.

Markerless tracking of tumor and tissues: A motion model approach.

Respiratory motion management is essential in order to achieve high-precision radiotherapy. Markerless motion tracking of tumor can provide a non-invasive way to manage respiratory motion, thereby enhancing treatment accuracy. However, the low contrast in real-time x-ray images for image guidance limits the application of markerless tracking. We present a novel approach based on a motion model to perform markerless tracking of tumor and surrounding tissues even when they have low contrast in real-time x-ray images. A proof-of-concept validation of the method has been performed using digital and physical phantoms at breathing conditions that are significantly different than the planning stage. A motion model is first constructed by performing principal component analysis (PCA) on the planning 4DCT. During treatment, the motion of a surrogate is tracked and used as the input of the motion model, which generates a 3D real-time volume estimation. Such 3D estimation is then projected to 2D to create digitally reconstructed radiographs (DRRs). The relationships between the real-time DRRs, reference DRRs, and reference x-ray images are first established to simulate 2D real-time images from the real-time volume. The registration between the simulated 2D real-time images and real-time x-ray images corrects the initial motion model estimation to ensure the estimated volume matches the real-time condition. In digital phantom, the Dice index of pancreas was improved from 0.74 to 0.78 after correction using real-time DRRs in fully inhaled phase. Validation on lung and pancreas is performed in physical phantom with two motion traces. The surrogate-tumor relationships were intentionally altered to generate large target localization errors due to the differences in body condition between treatment planning stage and during treatment. The real-time correction for the estimated 3D real-time volume was performed using a pair of 2D x-ray images. For the deep breathing motion trace, the tumor localization mean absolute error (MAE) throughout the tracking decreases from around 3mm to less than 1mm after correction. For the shallow breathing motion trace with a 1.7mm baseline shift, the tumor localization MAE throughout the tracking decreases from around 1.5mm to less than 1mm after correction. The method combines the detailed structural information from planning 4DCT and real-time information from real-time x-ray images through a motion model. The matching between the real-time model estimation and 2D real-time images is performed in the same modality so that it can be applied to regions with low contrast in the images. The real-time images successfully corrected the initial motion model estimations in our proof-of-concept validation. This suggests the potential to perform markerless tracking in low-contrast region using a motion model.

Neural networks empowered: a machine learning-enabled, Gyro mmID for enhanced virtual reality and motion tracking applications

Abstract With the emerging developments in millimeter-wave/5G technologies, the potential for wireless Internet of things devices to achieve widespread sensing, precise localization, and high data-rate communication systems becomes increasingly viable. The surge in interest surrounding virtual reality (VR) and augmented reality (AR) technologies is attributed to the vast array of applications they enable, ranging from surgical training to motion capture and daily interactions in VR spaces. To further elevate the user experience, and real-time and accurate orientation detection of the user, the authors proposes the utilization of a frequency-modulated continuous-wave (FMCW) radar system coupled with an ultra-low-power, sticker-like millimeter-wave identification (mmID). The mmID features four backscattering elements, multiplexed in amplitude, frequency, and spatial domains. This design utilizes the training of a supervised learning classification convolutional neural network, enabling accurate real-time three-axis orientation detection of the user. The proposed orientation detection system exhibits exceptional performance, achieving a noteworthy accuracy of 90.58% over three axes at a distance of 8 m. This high accuracy underscores the precision of the orientation detection system, particularly tailored for medium-range VR/AR applications. The integration of the FMCW-based mmID system with machine learning proves to be a promising advancement, contributing to the seamless and immersive interaction within virtual and augmented environments.

A robust and efficient cubature Kalman filter based on the variational Bayesian method and its application in target tracking

Abstract In this paper, we propose a robust cubature Kalman filter (CKF) for nonlinear state-space models with unknown state and measurement noise covariance matrix. We study situations in which sensors are independent of each other. Therefore, the unknown measurement noise variance is modeled as an unknown inverse gamma (IG) distribution. The Gaussian-Student-t-inverse-Wishart (GSTIW) mixture distribution is used to model the one-step prediction distribution. Modeling generates numerous unknown parameters. Therefore, we adopt the statistical linearization method to linearize the observation function and then estimate the state and parameters separately to reduce the computational burden of estimating unknown parameters, significantly improving the algorithm’s efficiency. Finally, using the variational Bayesian method, a novel and efficient robust CKF based on the IG distribution and GSTIW mixture distributions (IG-GSTIW-CKF) is obtained. Simulation and experimental results show that the proposed method has better estimation accuracy than several advanced algorithms when sensors are independent. In addition, the efficiency of the proposed algorithm is significantly higher than that of other CKF-based methods.

Field monitoring of copepodamides using a new application for solid phase adsorption toxin tracking

AbstractChemical signaling is ubiquitous in the marine environment. Plankton rely on chemical signals to find mates, hunt prey, and respond to threats, and these small‐scale interactions can propagate into community‐wide cascades and large‐scale ecological changes. The chemical signaling exchange in the open ocean is poorly understood, and fundamental information about concentrations and spatiotemporal variability is lacking. Passive sampling has been used to monitor a wide range of dissolved chemicals, including anthropogenic pollutants and harmful algal toxins, but it is not generally applied to the study of marine chemical ecology. Here we test the compatibility of two resins commonly used for passive sampling via solid phase adsorption toxin tracking (SPATT), Diaion® HP20 and Sepabeads® SP207, with copepodamides, a group of polar lipid signaling compounds produced by copepods. We developed extraction and analysis methods that align with current SPATT practices for algal toxins and show the first measurements of copepodamides from Monterey Bay in California. In lab trials, mean copepodamide recovery from HP20 resin was approximately 240% greater than SP207. In addition, copepodamides were found to have a mean half‐life of 34 h in seawater. Adsorption to HP20 stabilized dissolved copepodamides, increasing the mean recovery after 168 h from 0.62% in seawater to 65.2% from SPATT. Results suggest that SPATT is a sensitive and effective tool for obtaining integrated copepodamide concentrations, spotlighting a novel method to include information from copepod mesozooplankton in time series and field studies.