Movement Patterns Research Articles

Modeling rehabilitation dataset to implement effective AI assistive systems

The demand for automated systems monitoring and supporting patients in their home-based recovery programs is substantial. While emerging technologies have been proposed as potential solutions to enhance at-home patient care, limited systems are in place due to their challenges in offering real-time monitoring and corrective feedback. Most proposed methodologies provide an overall measure or score for the executed movement. The proposed work involves the adaptation of an existing published dataset to cater to the needs of a system capable of remotely assisting patients, effectively acting as a virtual physical therapist able to provide corrective feedback. A dataset containing a set of three simple exercises for shoulder rehabilitation was processed. Each movement was meticulously annotated for temporal and categorical motion domains to monitor exercise execution in terms of the range of motion completeness and to evaluate compensatory movement patterns. This work carries substantial significance by offering a standardized and easily accessible model for human movement data, thus fostering the advancement of digital assistive systems designed to support home-based rehabilitation programs.

GL-STGCNN: Enhancing Multi-Ship Trajectory Prediction with MPC Correction

In addressing the challenges of trajectory prediction in multi-ship interaction scenarios and aiming to improve the accuracy of multi-ship trajectory prediction, this paper proposes a multi-ship trajectory prediction model, GL-STGCNN. The GL-STGCNN model employs a ship interaction adjacency matrix extraction module to obtain a more reasonable ship interaction adjacency matrix. Additionally, after obtaining the distribution of predicted trajectories using the model, a model predictive control trajectory correction method is introduced to enhance the accuracy and reasonability of the predicted trajectories. Through quantitative analysis of different datasets, it was observed that GL-STGCNN outperforms previous prediction models with a 31.8% improvement in the average displacement error metric and a 16.8% improvement in the final displacement error metric. Furthermore, trajectory correction through model predictive control shows a performance boost of 44.5% based on the initial predicted trajectory distribution. While GL-STGCNN excels in multi-ship interaction trajectory prediction by reasonably modeling ship interaction adjacency matrices and employing trajectory correction, its performance may vary in different datasets and ship motion patterns. Future work could focus on adapting the model’s ship interaction adjacency matrix modeling to diverse environmental scenarios for enhanced performance.

Preparation of Non Locomotor Basic Movement Assessment Instruments for Class III Moderate Mentally Retarded Children at SLB Negeri Luragung Kuningan

Assessment is an access used to determine the abilities, difficulties and needs of a child which is then used as a reference to develop an appropriate learning program. A teacher, especially a teacher who handles children with special needs, is required to be able to compile assessment instruments that are suitable for the children he handles. The purpose of this study was to develop a non-locomotor basic motion assessment instrument for class III moderate mental retardation child at SDLB SLB Negeri Luragung. Non-locomotor basic movements are used in learning activities usually in warming up or cooling down during sports that contain motion patterns such as bending, twisting, swinging and bending. This study used a qualitative approach and descriptive method. The data in this study were obtained from observations, interviews, FGDs, and validation. The results of observations made by research to three grade III moderate mental retardation child show that children experience obstacles in gross motor non-locomotor basic movements such as in sports lessons during warm-ups the child looks stiff and less able to maintain body balance so as not to fall, while in daily activities maintaining body balance while doing activities in place is very necessary. Therefore, researchers are interested in compiling a non-locomotor basic motion assessment instrument for grade III moderate disability children to find out more about what the child's difficulties are in basic non-locomotor movements, then the results of the assessment instruments that have been prepared are validated to determine readability and applicability.

Metropolitan spatial structure based on commuting pattern: a comparison between Jakarta and Surabaya metropolitans

ABSTRACT Different urban structures can present different problems. Therefore, it is important to organize the spatial structure of the city to ensure the movement and welfare of city residents. Metropolitan Jakarta and Surabaya are the two largest metropolitan areas in Indonesia and play a very important role as centres of national social and economic activities. This study aims to identify urban spatial structures based on commuter movement patterns in both metropolitan areas. This is done by calculating the spatial interaction and visualization of the Desire Line Map. The results of the analysis show that Jakarta and Surabaya Metropolitan Areas still tend to be monocentric because the attraction of their movements is still dominated by their respective cores.

An Enhanced Study on Gold Price Prognosis using Machine Learning

Machine learning has emerged as a prominent research area for predicting gold prices, utilizing historical data and algorithms. The field aims to uncover patterns, trends, and connections among various factors that influence gold prices, including economic indicators, geopolitical events, and supply and demand dynamics. By employing machine learning algorithms, predictive models can be constructed to provide valuable insights into potential patterns in gold price movements. This enables traders, investors, and other stakeholders to make informed decisions when it comes to gold investments. In our study, we delve into the realm of data science and machine learning techniques to forecast gold prices. We meticulously analyze historical gold price data, develop sophisticated forecasting models, and rigorously evaluate their performance. Through this process, we are able to identify meaningful patterns and correlations that significantly contribute to the prediction of future gold prices. One of the key aspects of our study is the assessment of the reliability and accuracy of various machine learning models specifically designed for gold price prediction. We examine different algorithms and approaches, comparing their effectiveness in capturing the underlying patterns in gold price movements. This evaluation provides us with important findings and insights, enabling us to determine the most suitable models for accurate gold price forecasting. However, it is crucial to acknowledge the limitations inherent in our study. The forecasting of gold prices is a complex task influenced by a multitude of factors, some of which may be unpredictable or subject to sudden changes. Therefore, our models may not capture all the nuances and intricacies of gold price dynamics. To address these limitations, we propose recommendations for future research, such as exploring novel data sources, incorporating additional variables, or improving the models' adaptability to changing market conditions. Machine learning plays a pivotal role in the field of gold price prediction. By leveraging historical data and employing sophisticated algorithms, we can uncover valuable insights and patterns that assist in forecasting future gold prices. Our study aims to contribute to this growing body of research by developing reliable models and providing important insights for traders, investors, and other stakeholders in the gold market

Effect of graphene coating on flow boiling in a minichannel at sub-atmospheric pressures

Flow boiling in a mini-channel with graphene-coated sintered porous copper surface was investigated at sub-atmospheric pressures by analyzing bubble motion and flow patterns. A method of surface modification based on graphene coating was presented for thermal management systems operating at lower temperatures. The experiments were carried out at mass fluxes of 60 kg.m–2.s–1 and 120 kg.m–2.s–1 and sub-atmospheric pressures of 48 kPa and 68 kPa in a mini-channel with dimensions of 50 mm × 15 mm × 2 mm using HFE-7100 coolant as the working fluid. At sub-atmospheric pressures, the bubble nucleation site density decreases, and the bubble diameter increases while the bubble release frequency decreases. The existence of porous surfaces and HFE-7100 coolant with lower surface tension leads to the inception of boiling at lower wall superheats. The graphene coating promotes microlayer evaporation and consequently increases the nucleation site density by enhancing the thermal conductivity and wicking rate. Additionally, it provides stable annular flow at high heat fluxes, whereas intermittent-annular flow is observed for the untreated surface under the same condition. This enhancement results in a more pronounced increase in the boiling heat transfer for the graphene-coated surface. As a result, this study provides the high potential of graphene coatings for phase change heat transfer enhancement at sub-atmospheric pressures.

Integrating multiple vehicle drivetrains into an energy system simulation model for Japan

To reduce the impact of climate change, the Japanese economy has set mitigation goals that include the decarbonisation of the energy sector and the electrification of transport. As a result, zero-emission vehicles could change the electricity demand curve and need to be integrated into energy system models to estimate their impact and any opportunities or challenges they represent to grid stability. While previous studies have integrated single vehicle technologies in the simulation of country-level energy grids, the present study improves on available models by integrating a country-level energy system model with a transmission grid, while considering two different drivetrains and improving on the diversity of the vehicle movement patterns considered. The simulation model results highlight that the electricity demand of each drivetrain is distinct, with a midday peak for battery electric vehicles and less pronounced morning and afternoon peaks for fuel cell electric vehicles. An important conclusion is that the infrastructure setup and associated use rules can be expected to significantly impact transport demand curves, indicating the need to further investigate how policy changes can impact on the overall configuration of the energy mix.

Real-time sports injury monitoring system based on the deep learning algorithm

In response to the low real-time performance and accuracy of traditional sports injury monitoring, this article conducts research on a real-time injury monitoring system using the SVM model as an example. Video detection is performed to capture human movements, followed by human joint detection. Polynomial fitting analysis is used to extract joint motion patterns, and the average of training data is calculated as a reference point. The raw data is then normalized to adjust position and direction, and dimensionality reduction is achieved through singular value decomposition to enhance processing efficiency and model training speed. A support vector machine classifier is used to classify and identify the processed data. The experimental section monitors sports injuries and investigates the accuracy of the system’s monitoring. Compared to mainstream models such as Random Forest and Naive Bayes, the SVM utilized demonstrates good performance in accuracy, sensitivity, and specificity, reaching 94.2%, 92.5%, and 96.0% respectively.

Validation of Inertial-Measurement-Unit-Based Ex Vivo Knee Kinematics during a Loaded Squat before and after Reference-Frame-Orientation Optimisation

Recently, inertial measurement units have been gaining popularity as a potential alternative to optical motion capture systems in the analysis of joint kinematics. In a previous study, the accuracy of knee joint angles calculated from inertial data and an extended Kalman filter and smoother algorithm was tested using ground truth data originating from a joint simulator guided by fluoroscopy-based signals. Although high levels of accuracy were achieved, the experimental setup leveraged multiple iterations of the same movement pattern and an absence of soft tissue artefacts. Here, the algorithm is tested against an optical marker-based system in a more challenging setting, with single iterations of a loaded squat cycle simulated on seven cadaveric specimens on a force-controlled knee rig. Prior to the optimisation of local coordinate systems using the REference FRame Alignment MEthod (REFRAME) to account for the effect of differences in local reference frame orientation, root-mean-square errors between the kinematic signals of the inertial and optical systems were as high as 3.8° ± 3.5° for flexion/extension, 20.4° ± 10.0° for abduction/adduction and 8.6° ± 5.7° for external/internal rotation. After REFRAME implementation, however, average root-mean-square errors decreased to 0.9° ± 0.4° and to 1.5° ± 0.7° for abduction/adduction and for external/internal rotation, respectively, with a slight increase to 4.2° ± 3.6° for flexion/extension. While these results demonstrate promising potential in the approach’s ability to estimate knee joint angles during a single loaded squat cycle, they highlight the limiting effects that a reduced number of iterations and the lack of a reliable consistent reference pose inflicts on the sensor fusion algorithm’s performance. They similarly stress the importance of adapting underlying assumptions and correctly tuning filter parameters to ensure satisfactory performance. More importantly, our findings emphasise the notable impact that properly aligning reference-frame orientations before comparing joint kinematics can have on results and the conclusions derived from them.

Autonomous Full 3D Coverage Using an Aerial Vehicle, Performing Localization, Path Planning, and Navigation Towards Indoors Inventorying for the Logistics Domain

Over the last years, a rapid evolution of unmanned aerial vehicle (UAV) usage in various applications has been observed. Their use in indoor environments requires a precise perception of the surrounding area, immediate response to its changes, and, consequently, a robust position estimation. This paper provides an implementation of navigation algorithms for solving the problem of fast, reliable, and low-cost inventorying in the logistics industry. The drone localization is achieved with a particle filter algorithm that uses an array of distance sensors and an inertial measurement unit (IMU) sensor. Navigation is based on a proportional–integral–derivative (PID) position controller that ensures an obstacle-free path within the known 3D map. As for the full 3D coverage, an extraction of the targets and then their final succession towards optimal coverage is performed. Finally, a series of experiments are carried out to examine the robustness of the positioning system using different motion patterns and velocities. At the same time, various ways of traversing the environment are examined by using different configurations of the sensor that is used to perform the area coverage.

An Evidence-Based Discussion of Fetal Pain and Stress.

The concept of fetal pain results from procedures conducted without anesthesia in preterm newborns and fetuses, which indicate that it is possible to examine fetal pain based on stress hormone, metabolic, and behavioral changes. Anatomical and physiological data suggest that fetuses become capable of processing nociceptive stimuli around midgestation, although the associated changes in fetal brain development remain unclear. What constitutes fetal pain remains controversial in the light of the definition of pain adopted by the International Association for the Study of Pain (IASP), which posits pain as an "unpleasant sensory and emotional experience." Here, we examine the notion that human fetuses cannot "experience" pain and potential implications of this claim. We highlight the key scientific evidence related to fetal pain, including clinical studies on pain in fetuses and preterm newborns. We argue that consistent patterns of stress hormones, metabolic changes, body movements, hemodynamic changes, and pain-related facial expressions in fetuses exposed to invasive procedures overcome the need for subjective proof of pain as articulated in the IASP definition. No study to date has conclusively proven the absence of fetal pain beyond the age of viability. Based on the current evidence, we propose that all fetuses receive anesthesia regardless of the invasive procedures being performed to guarantee the least possible pain and physiological, behavioral, or hormonal responses without exposing the mother or her baby to unnecessary complications.

Predictive Analyses of Traffic Level in the City of Barcelona: From ARIMA to eXtreme Gradient Boosting

This study delves into the intricate dynamics of urban mobility, a pivotal aspect for policymakers, businesses, and communities alike. By deciphering patterns of movement within a city, stakeholders can craft targeted interventions to mitigate traffic congestion peaks, optimizing both resource allocation and individual travel routes. Focused on Barcelona, Spain, this paper draws on data sourced from the city council’s open data service. Through a blend of exploratory analysis, visualization techniques, and modeling methodologies—including time series analysis and the eXtreme Gradient Boosting (XGBoost) algorithm—the research endeavors to forecast traffic conditions. Additionally, a study of variable importance is carried out, and Shapley Additive Explanations are applied to enhance the interpretability of model outputs. Findings underscore the limitations of traditional forecasting methods in capturing the nuanced spatial and temporal dependencies present in traffic flows, particularly over medium- to long-term horizons. However, the XGBoost model demonstrates robust performance, with the area under ROC curves consistently exceeding 80%, indicating its efficacy in handling non-linear traffic data variables.

Understanding Human Cognition Through Computational Modeling.

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

Survival, Nest Site Affiliation and Post-Fledging Movements of Danish White-Tailed Eagles (Haliaeetus albicilla)

The early life movement patterns of long-lived, large raptors, such as the white-tailed eagle (Haliaeetus albicilla), remain largely unexplored. In this study, we have tracked 22 individuals of white-tailed eagles hatched in Denmark to investigate key parameters, including survival rates, causes of death, nest site fidelity, geographical distribution, and dispersal behaviors. Our analyses are based on approximately 340,000 GPS/GSM telemetry positions from the 22 individuals. We found that survival rates were lower in the first year of life, especially among females, but increased in subsequent years. The primary causes of death were wind turbine collisions and avian influenza. Movement analyses revealed that juvenile eagles frequented nest sites in August and September, with females showing earlier exploratory flights and dispersal. Both males and females spent most of their first calendar year near their nest sites. In the second calendar year, a significant proportion of their time was spent at well-known white-tailed eagle resting areas in Denmark, as well as visiting neighboring countries. Notably, one individual set a new distance record for a Danish GPS-tagged white-tailed eagle, venturing at least 1750 km away from its nest site to visit Sweden, Finland, Russia, and Norway. Our results, indicating that juvenile white-tailed eagles continue to utilize nest sites into September, suggest an extension of the recommended time period for nest protection zones provided by BirdLife Denmark (DOF).

Improved motion correction in brain MRI using 3D radial trajectory and projection moment analysis.

To develop a generalized rigid body motion correction method in 3D radial brain MRI to deal with continuous motion pattern through projection moment analysis. An assumption was made that the multichannel coil moves with the head, which was achieved by using a flexible head coil. A two-step motion correction scheme was proposed to directly extract the motion parameters from the acquired k-space data using the analysis of center-of-mass with high noise robustness, which were used for retrospective motion correction. A recursive least-squares model was introduced to recursively estimate the motion parameters for every single spoke, which used the smoothness of motion and resulted in high temporal resolution and low computational cost. Five volunteers were scanned at 3 T using a 3D radial multidimensional golden-means trajectory with instructed motion patterns. The performance was tested through both simulation and in vivo experiments. Quantitative image quality metrics were calculated for comparison. The proposed method showed good accuracy and precision in both translation and rotation estimation. A better result was achieved using the proposed two-step correction compared to traditional one-step correction without significantly increasing computation time. Retrospective correction showed substantial improvements in image quality among all scans, even for stationary scans. The proposed method provides an easy, robust, and time-efficient tool for motion correction in brain MRI, which may benefit clinical diagnosis of uncooperative patients as well as scientific MRI researches.

GRAFTS COMPARISON BETWEEN QUADRICEPS TENDON AUTOGRAFT VS. ALLOGRAFT FOR ANTERIOR CRUCIATE LIGAMENT REVISIOON RECONSTRUCTION: A LITERATURE REVIEW

Introduction: Anterior cruciate ligament (ACL) reconstruction is one of the most common orthopaedic surgeries performed on active people in the world. One of the most important surgical decisions is graft type for use in the reconstruction. Recently, the quadriceps tendon has gained popularity for use as a graft source for ACL reconstruction. The graft choice is broadly between an autograft or allograft. This review aims to understand the current concepts in graft comparison between quadriceps tendon autograft vs. allograft for anterior cruciate ligament revision reconstruction. Methods: This literature review study was reported following the preferred reporting items for systematic reviews and adhered to a structured review protocol. The author searched the PubMed, ScienceDirect, and British Medical Journal databases. The authors comparing selected publications reporting patient outcomes with all types of reviews or descriptions of ACL revision reconstruction and its graft being used or other related subjects. Published in English is included. The author then formulates a synthesis to support the previously determined research objectives. Results: ACL reconstruction with partial thickness soft tissue quadriceps tendon graft is a reliable option. Where there are differences in the failure rates of ACL reconstructions between allografts and autografts, these can mostly be explained by irradiated grafts. Giving sufficient radiation to achieve sterility will likely weaken grafts and make them more likely to fail; therefore, irradiating grafts is not recommended. The evidence shows no significant differences in clinical effectiveness between autografts and non-irradiated allografts. Failure rates with both grafts are now low. Discussion: The primary goals of rehabilitation after ACL reconstruction do not vary considerably based on graft type and emphasize: (1) restoration of the full range of motion, (2) normalization of quadriceps strength, (3) improvement of overall lower extremity muscle strength, and (4) re-training of movement patterns to return to pre-injury sports participation while decreasing injury risk. Achieving full motion and quadriceps strength contributes to a rapid return to normal weight bearing, gait, and activities of daily living. In the setting of revision ACL reconstruction, there are various considerations regarding graft choice due to tunnel size or position, previous usage of other grafts, and reconstructions in the other leg, which may mean that allografts would be preferred to autografts. The other population not covered sufficiently in the literature is the elite sprinting athlete, where autograft choices may be influenced by the effect of graft harvesting on their sport. However, in most cases, allograft ACL reconstruction with non-irradiated grafts is as safe but more expensive than autograft ACL reconstruction, which is preferred as it is more cost-effective. Conclusion: There is little difference in the results of ACL reconstruction with autografts or non-irradiated allografts, with any advantage being with autografts. The cost is higher with allografts. So if autografts are available, allografts are not cost-effective.

GazeSwitch: Automatic Eye-Head Mode Switching for Optimised Hands-Free Pointing

This paper contributes GazeSwitch, an ML-based technique that optimises the real-time switching between eye and head modes for fast and precise hands-free pointing. GazeSwitch reduces false positives from natural head movements and efficiently detects head gestures for input, resulting in an effective hands-free and adaptive technique for interaction. We conducted two user studies to evaluate its performance and user experience. Comparative analyses with baseline switching techniques, Eye+Head Pinpointing (manual) and BimodalGaze (threshold-based) revealed several trade-offs. We found that GazeSwitch provides a natural and effortless experience but trades off control and stability compared to manual mode switching, and requires less head movement compared to BimodalGaze. This work demonstrates the effectiveness of machine learning approach to learn and adapt to patterns in head movement, allowing us to better leverage the synergistic relation between eye and head input modalities for interaction in mixed and extended reality.

Low signs of territorial behavior in the Eurasian otter during low-water conditions in a Mediterranean river

The Eurasian otter Lutra lutra is a territorial semi-aquatic carnivore usually found at low densities in rivers, coastal areas, and wetlands. Its diet is based on prey associated with aquatic environments. Mediterranean rivers are highly seasonal, and suffer reduced flow during the summer, resulting in isolated river sections (pools) that sometimes can be left with a minimal amount of water, leading to concentrations of food for otters. To our knowledge, this process, which was known to field naturalists, has not been accurately described, nor have otter densities been estimated under these conditions. In this study, we describe the population size and movements of an aggregation of otters in an isolated pool in the Guadiana River in the Tablas de Daimiel National Park (central Spain), which progressively dried out during the spring–summer of 2022, in a context of low connectivity due to the absence of circulating water in the Guadiana and Gigüela rivers. Using non-invasive genetic sampling of 120 spraints collected along 79.4 km of sampling transects and spatial capture-recapture methods, we estimated the otter density at 1.71 individuals/km of river channel length (4.21 individuals/km2) in a progressively drying river pool, up to five times higher than previously described in the Iberian Peninsula. The movement patterns obtained with the spatial capture-recapture model are not quite different from those described in low density, which seems to indicate a wide home range overlap, with low signs of territoriality.

The use of accelerometer bracelets to evaluate arm motor function over a stroke rehabilitation period – an explorative observational study

BackgroundAssessments of arm motor function are usually based on clinical examinations or self-reported rating scales. Wrist-worn accelerometers can be a good complement to measure movement patterns after stroke. Currently there is limited knowledge of how accelerometry correlate to clinically used scales. The purpose of this study was therefore to evaluate the relationship between intermittent measurements of wrist-worn accelerometers and the patient’s progression of arm motor function assessed by routine clinical outcome measures during a rehabilitation period.MethodsPatients enrolled in in-hospital rehabilitation following a stroke were invited. Included patients were asked to wear wrist accelerometers for 24 h at the start (T1) and end (T2) of their rehabilitation period. On both occasions arm motor function was assessed by the modified Motor Assessment Scale (M_MAS) and the Motor Activity Log (MAL). The recorded accelerometry was compared to M_MAS and MAL.Results20 patients were included, of which 18 completed all measurements and were therefore included in the final analysis. The resulting Spearman’s rank correlation coefficient showed a strong positive correlation between measured wrist acceleration in the affected arm and M-MAS and MAL values at T1, 0.94 (p < 0.05) for M_MAS and 0.74 (p < 0.05) for the MAL values, and a slightly weaker positive correlation at T2, 0.57 (p < 0.05) for M_MAS and 0.46 − 0.45 (p = 0.06) for the MAL values. However, no correlation was seen for the difference between the two sessions.ConclusionsThe results confirm that the wrist acceleration can differentiate between the affected and non-affected arm, and that there is a positive correlation between accelerometry and clinical measures. Many of the patients did not change their M-MAS or MAL scores during the rehabilitation period, which may explain why no correlation was seen for the difference between measurements during the rehabilitation period. Further studies should include continuous accelerometry throughout the rehabilitation period to reduce the impact of day-to-day variability.

Migratory movements of fin whales from the Gulf of St. Lawrence challenge our understanding of the Northwest Atlantic stock structure

Fin whales, Balenoptera physalus, are capital breeders, having the potential to separate breeding and feeding both spatially and temporally. Fin whales occur throughout the Northwest Atlantic, but stock structure and seasonal movements remain unclear. By deploying satellite transmitters on 28 individuals, we examine movement patterns within and beyond the Gulf of St. Lawrence (GSL), Canada, and challenge the current understanding of stock structure. Eight individuals left the GSL in autumn, with five tags persisting into January. Migration patterns of these whales showed considerable variation in timing and trajectory, with movements extending south to 24°N, and thus beyond the assumed distribution limit of the species in the Northwest Atlantic. A rapid return to the Scotian Shelf or Gulf of Maine was observed from several whales after incursions in southern waters, suggesting that fin whales in the Northwest Atlantic may not have a common winter destination that fits the definition of a breeding ground. Area-restricted search (ARS) behavior dominated fin whale activities during summer (92%) and fall (72%), with persistence into the winter (56%); ARS occurred at multiple locations in the GSL, Scotian Shelf and Shelf edge, and near seamounts of the North Atlantic, having characteristics consistent with foraging areas.