Motion Tracking Research Articles

469 Motion management and tracking

469 Motion management and tracking

EM-Gait: Gait recognition using motion excitation and feature embedding self-attention

Gait recognition, which can realize long-distance and contactless identification, is an important biometric technology. Recent gait recognition methods focus on learning the pattern of human movement or appearance during walking, and construct the corresponding spatio-temporal representations. However, different individuals have their own laws of movement patterns, simple spatial–temporal features are difficult to describe changes in motion of human parts, especially when confounding variables such as clothing and carrying are included, thus distinguishability of features is reduced. To this end, we propose the Embedding and Motion (EM) block and Fine Feature Extractor (FFE) to capture the motion mode of walking and enhance the difference of local motion rules. The EM block consists of a Motion Excitation (ME) module to capture the changes of temporal motion and an Embedding Self-attention (ES) module to enhance the expression of motion rules. Specifically, without introducing additional parameters, ME module learns the difference information between frames and intervals to obtain the dynamic change representation of walking for frame sequences with uncertain length. By contrast, ES module divides the feature map hierarchically based on element values, blurring the difference of elements to highlight the motion track. Furthermore, we present the FFE, which independently learns the spatio-temporal representations of human body according to different horizontal parts of individuals. Benefiting from EM block and our proposed motion branch, our method innovatively combines motion change information, significantly improving the performance of the model under cross appearance conditions. On the popular dataset CASIA-B, our proposed EM-Gait is better than the existing single-modal gait recognition methods.

Relationship between glenohumeral horizontal abduction angle and scapular internal rotation angle at maximum shoulder external rotation during baseball pitching

BackgroundIncreased scapulothoracic (ST) internal rotation at maximum external rotation (MER) during baseball pitching can decrease glenohumeral (GH) horizontal adduction, leading to excessive GH horizontal abduction. However, few studies have examined this direct relationship, and none have investigated the ST internal rotation angle at stride foot contact (FC) and MER. This study investigated the relationships between GH horizontal adduction and ST internal rotation angles at MER and between ST internal rotation angles at FC and MER. MethodsWe recruited 15 asymptomatic collegiate baseball pitchers and assessed the ST internal rotation angle at FC and MER and GH horizontal adduction angle at MER during pitching using an optical motion tracking system. Pearson’s correlation coefficients determined the relationships between GH horizontal adduction and ST internal rotation angles at MER and between ST internal rotation angles at FC and MER. ResultsThe GH horizontal adduction angle at MER was significantly related to the ST internal rotation angle at MER (r = -0.58, p = 0.022). The ST internal rotation angle at FC was significantly related to the ST internal rotation angle at MER (r = 0.53, p = 0.045). ConclusionsThe GH horizontal adduction angle at MER is associated with the ST internal rotation angle at MER in asymptomatic collegiate baseball pitchers and the ST internal rotation angle at FC is related to the ST internal rotation angle at MER. Thus, the scapula and humerus should be controlled from FC to MER during pitching to reduce internal impingement risk in asymptomatic baseball pitchers.

Biomechanical costs influence decisions made during ongoing actions.

Accurate interaction with the environment relies on the integration of external information about the spatial layout of potential actions and knowledge of their costs and benefits. Previous studies have shown that when given a choice between voluntary reaching movements, humans tend to prefer actions with lower biomechanical costs. However, these studies primarily focused on decisions made before the onset of movement ("decide-then-act" scenarios), and it is not known to what extent their conclusions generalize to many real-life situations, in which decisions occur during ongoing actions ("decide-while-acting"). For example, one recent study found that biomechanical costs did not influence decisions to switch from a continuous manual tracking movement to a point-to-point movement, suggesting that biomechanical costs may be disregarded in decide-while-acting scenarios. To better understand this surprising result, we designed an experiment in which participants were faced with the decision between continuing to track a target moving along a straight path or changing paths to track a new target that gradually moved along a direction that deviated from the initial one. We manipulated tracking direction, angular deviation rate, and side of deviation, allowing us to compare scenarios where biomechanical costs favored either continuing or changing the path. Crucially, here the choice was always between two continuous tracking actions. Our results show that in this situation decisions clearly took biomechanical costs into account. Thus we conclude that biomechanics are not disregarded during decide-while-acting scenarios but rather that cost comparisons can only be made between similar types of actions.NEW & NOTEWORTHY In this study, we aim to shed light on how biomechanical factors influence decisions made during ongoing actions. Previous work suggested that decisions made during actions disregard biomechanical costs, in contrast to decisions made before movement. Our results challenge that proposal and suggest instead that the effect of biomechanical factors is dependent on the types of actions being compared (e.g., continuous tracking vs. point-to-point reaching). These findings contribute to our understanding of the dynamic interplay between biomechanical considerations and action choices during ongoing interactions with the environment.

Tracking ablation and movement of icebergs with time-lapse photography at an alpine proglacial lake in Austria

ABSTRACT Little is known about the evolution and dynamics of icebergs in alpine lakes. We analyzed the movement and ablation patterns of icebergs at an ice-contact lake at Pasterze Glacier, Austria, using time-lapse images. Iceberg evolution was quantified for two timescales and related to meteorological as well as glacier ablation data from the adjacent glacier tongue. On a multiyear scale, ablation and movement of one iceberg (IB1) was monitored during a twenty-five-month period. On a single-day scale, the movement paths of eighty-four icebergs were tracked over 16 hours. Results for IB1 revealed an average iceberg ablation of 72 mm d−1 from June to September and no winter ablation. Iceberg ablation rates rose over time, explained by a rising surface area-to-volume ratio. Monitoring lake-wide iceberg movement for one day shows that a persistent katabatic glacier wind and a valley wind are the main influences on horizontal iceberg movement. Iceberg velocity is roughly 0.6 percent of the wind velocity. The existence of a wind-driven current on the lake surface is proposed. Sudden changes in movement rates, which are not explained by wind data, suggest that iceberg grounding is common. This study provides insight into iceberg melt rates in the absence of wave erosion.

Mathematical analysis of human motion vision capture image processing based on artificial intelligence

In order to understand the mathematical analysis of human motion vision capture image processing, a mathematical analysis of human motion vision capture image processing based on artificial intelligence is proposed. This paper firstly introduces the research progress, classification and several commonly used vision-based human motion tracking methods of motion capture technology. Secondly, the process and framework of capturing human motion video by using ordinary cameras and marking nodes are proposed, and the automatic tracking algorithm based on Camshift and Kalman filter is adopted. It verifies the effectiveness of the system, changes the traditional way of motion capture, and makes the process of capture more convenient when the capture effect meets the requirements. Finally, the performance of the human motion capture data retrieval algorithm based on video is evaluated comprehensively. It is compared with the latest literature in this field. In terms of time efficiency, for each online retrieval of data set, the proposed algorithm takes 0.056 s, while the methods of other scholars take an average of 1.5 s. Meanwhile, experiments are also conducted on public databases, proving the universality and scalability of the proposed algorithm. The algorithm proposed in this paper has greater advantages than the most advanced method of the same type, which verifies the effectiveness of the algorithm proposed in this paper.

Sensorimotor adaptation impedes perturbation detection in grasping.

Humans achieve skilled actions by continuously correcting for motor errors or perceptual misjudgments, a process called sensorimotor adaptation. This can occur with the actor both detecting (explicitly) and not detecting the error (implicitly). We investigated how the magnitude of a perturbation and the corresponding error signal each contribute to the detection of a size perturbation during interaction with real-world objects. Participants grasped cuboids of different lengths in a mirror-setup allowing us to present different sizes for seen and felt cuboids, respectively. Visuo-haptic size mismatches (perturbations) were introduced either abruptly or followed a sinusoidal schedule. These schedules dissociated the error signal from the visuo-haptic mismatch: Participants could fully adapt their grip and reduce the error when a perturbation was introduced abruptly and then stayed the same, but not with a constantly changing sinusoidal perturbation. We compared participants' performance in a two-alternative forced choice (2AFC) task where participants judged these mismatches, and modelled error-correction in grasping movements by looking at changes in maximum grip apertures, measured using motion tracking. We found similar mismatch-detection performance with sinusoidal perturbation schedules and the first trial after an abrupt change, but decreasing performance over further trials for the latter. This is consistent with the idea that reduced error signals following adaptation make it harder to detect perturbations. Error-correction parameters indicated stronger error-correction in abruptly introduced perturbations. However, we saw no correlation between error-correction and overall mismatch-detection performance. This emphasizes the distinct contributions of the perturbation magnitude and the error signal in helping participants detect sensory perturbations.

Winter Tracking Data Suggest that Migratory Seabirds Transport Per- and Polyfluoroalkyl Substances to Their Arctic Nesting Site.

Seabirds are often considered sentinel species of marine ecosystems, and their blood and eggs utilized to monitor local environmental contaminations. Most seabirds breeding in the Arctic are migratory and thus are exposed to geographically distinct sources of contamination throughout the year, including per- and polyfluoroalkyl substances (PFAS). Despite the abundance and high toxicity of PFAS, little is known about whether blood concentrations at breeding sites reliably reflect local contamination or exposure in distant wintering areas. We tested this by combining movement tracking data and PFAS analysis (nine compounds) from the blood of prelaying black-legged kittiwakes (Rissa tridactyla) nesting in Arctic Norway (Svalbard). PFAS burden before egg laying varied with the latitude of the wintering area and was negatively associated with time upon return of individuals at the Arctic nesting site. Kittiwakes (n = 64) wintering farther south carried lighter burdens of shorter-chain perfluoroalkyl carboxylates (PFCAs, C9-C12) and heavier burdens of longer chain PFCAs (C13-C14) and perfluorooctanesulfonic acid compared to those wintering farther north. Thus, blood concentrations prior to egg laying still reflected the uptake during the previous wintering stage, suggesting that migratory seabirds can act as biovectors of PFAS to Arctic nesting sites.

Ant and termite collective behavior: Group‐level similarity arising from individual‐level diversity

AbstractAnimal collective behavior produces group‐level patterns that emerge from individual‐level interactions among members. Such sophisticated behavioral coordination can be observed across various taxa, indicating evolutionary convergence. Among the most striking examples are two social insect lineages: ants and termites. Ants and termites evolved socially independently, inhabit terrestrial and subterranean environments, and face a similar collective task of maintaining their colonies with many individuals. This results in the convergent evolution of collective phenomena, such as constructing complex nest structures or movement coordination based on chemical and tactile cues. However, although the striking similarity attracts the most attention, little research has focused on how ants and termites differ in collective behaviors at individual and group levels. Here, we review the similarities and distinctions of collective behaviors in ants and termites. Even with similarities in group‐level patterns, rich diversity exists in mechanisms of behavioral coordination and the functions of collective patterns. Comparative analysis of collective behavior is challenging, but recent advances in automatic movement tracking methodologies have greatly expanded the potential for generating further insights. Finally, we conclude that comparative collective behavioral analysis can enhance our ability to understand biodiversity and provide alternative solutions for collective problem‐solving in many fields.

Revealing multi-scale spatial synergy of mega-city region from a human mobility perspective

ABSTRACT Spatial synergy is strengthened integration and connection between cities in a mega-city region, transcending administrative boundaries. The central flow theory suggests that the mega-city regions are formed by the interconnected flows of people across cities, making the spatial synergy can be measured by assessing the aggregation and intensity of flows and interactions between cities and regions. Human mobility data, such as mobile phone data and social media check-ins, enable the tracking of human movements, thus facilitating the transition of central flow theory from theoretical constructs to empirical research. To this end, this study presents an alternative data-driven framework to reveal the multi-scale spatial synergy of mega-city regions from a human mobility perspective. It uncovers homogeneously spatial communities with high inter-city integration using community detection. Strong internal spatial connections of 2.13 billion mobility are filtered using network backbone extraction. An experiment in the Pearl River Delta (PRD), China, demonstrates a multi-scale and multi-core hierarchical spatial synergy in the PRD region. The detailed findings are as follows: (1) Three cities attract the majority of human mobility. Mobility distance is short in urban centers and long in suburban areas. (2) The spatial integration pattern shows the detected communities reveal the hierarchical integration pattern with three main integrated regions: Guangzhou-Foshan-Zhaoqing, Shenzhen-Dongguan-Huizhou, and Zhuhai-Zhongshan-Jiangmen. (3) The spatial connection pattern illustrates the close ties of 9 cities and three core cities, including Guangzhou, Shenzhen, and Foshan. These results provide a human-centric understanding of urban synergy and deeper insights into central flow theory, which supports cooperative development in mega-city regions.

Wearable Sensors for Motion and Electrophysiological Signal Tracking in XR

Wearable Sensors for Motion and Electrophysiological Signal Tracking in XR

The impact of face masks on face-to-face neural tracking of speech: Auditory and visual obstacles

Face masks provide fundamental protection against the transmission of respiratory viruses but hamper communication. We estimated auditory and visual obstacles generated by face masks on communication by measuring the neural tracking of speech. To this end, we recorded the EEG while participants were exposed to naturalistic audio-visual speech, embedded in 5-talker noise, in three contexts: (i) no-mask (audio-visual information was fully available), (ii) virtual mask (occluded lips, but intact audio), and (iii) real mask (occluded lips and degraded audio). Neural tracking of lip movements and of the sound envelope of speech was measured through backward modeling, that is, by reconstructing stimulus properties from neural activity. Behaviorally, face masks increased perceived listening difficulty and phonological errors in speech content retrieval. At the neural level, we observed that the occlusion of the mouth abolished lip tracking and dampened neural tracking of the speech envelope at the earliest processing stages. By contrast, degraded acoustic information related to face mask filtering altered neural tracking of speech envelope at later processing stages. Finally, a consistent link emerged between the increment of perceived listening difficulty and the drop in reconstruction performance of speech envelope when attending to a speaker wearing a face mask. Results clearly dissociated the visual and auditory impact of face masks on the neural tracking of speech. While the visual obstacle related to face masks hampered the ability to predict and integrate audio-visual speech, the auditory filter generated by face masks impacted neural processing stages typically associated with auditory selective attention. The link between perceived difficulty and neural tracking drop also provides evidence of the impact of face masks on the metacognitive levels subtending face-to-face communication.

On Challenges of Monocular Pose Estimation for Endoluminal Navigation

Accurate endoscope navigation is vital for precise sampling and drug delivery during endoluminal procedures. Current systems rely on sensorized endoscopes for real-time bronchoscope tracking, but they are costly and require specialised settings. Camera pose estimation offers an alternative for endoscope motion tracking and navigation. In this study, we assess state-of-the-art monocular pose estimation methods for endoluminal navigation. We analyze multiple datasets and diverse feature tracking frameworks, including SIFT, ORB, BRISK, FREAK, AKAZE, SuperPoint, LoFTR, and EndoSfMLearner. Our findings reveal that, while modern deep learning-based feature detectors like SuperPoint and LoFTR yield denser features compared to traditional methods, their overall quality and reliability remain suboptimal. This insight is crucial for understanding the limitations of current monocular odometry in endoluminal environments. We also emphasize the challenges in generalizing learned frameworks developed for other odometry applications to endoscopic applications, stressing the need for more publicly available datasets. To fill this gap, we introduce a clinical bronchoscopy dataset with ground truth camera pose data. This dataset serves as a realistic benchmark for improving feature-based odometry in surgical navigation and encourages tailored framework development for learned pose estimation in endoluminal interventions.

Comparing and characterizing scapular muscle activation ratios in males and females during execution of common functional movements.

The shoulder complex relies on scapular movement controlled by periscapular muscles for optimal arm function. However, minimal research has explored scapular muscle activation ratios during functional tasks, nor how they might be influenced by biological sex. This investigation aims to characterize how sex impacts scapular muscle activation ratios during functional tasks. Twenty participants (ten females, ten males) were assessed with surface electromyography (EMG) and motion tracking during seven functional tasks. Activation ratios were calculated from normalized EMG for the three trapezius muscles and serratus anterior. Scapular angles were calculated using a YXZ Euler sequence. Two-way mixed methods ANOVAs (p<.05) were used to assess the effects of sex and humeral elevation level on ratios and angles. Sex-based differences were present in the Tie Apron task, with males exhibiting higher upper trapezius/lower trapezius and upper trapezius/middle trapezius ratios than females. Males also demonstrated decreased internal rotation in this task. Other tasks showcased significant sex-based differences in scapular upward rotation but not in activation ratios. Humeral elevation generally demonstrated an inverse relationship with scapular muscle activation ratios. This study highlights sex-based differences in scapular muscle activation ratios during specific functional tasks, emphasizing the need to consider sex in analyses of shoulder movements. Normative activation ratios for functional tasks were provided, offering a foundation for future comparisons with non-normative groups. Further research is warranted to confirm and explore additional influencing factors, advancing our understanding of shoulder activation and movement in diverse populations.

Enhancing Radar Image Classification with Autoencoder-CNN Hybrid System

The tracking, analysis, and classification of human movements can be crucial, particularly in areas such as elderly care, healthcare, and infant care. Typically, such tracking is done remotely with cameras. However, radar systems have emerged as significant methods and tools for these tasks due to their advantages such as privacy, wireless operation, and the ability to work through walls. By converting reflected radar signals from targets into images, human activities can be classified using powerful classification tools like deep learning. In this study, range-Doppler images of behind-the-wall human movements obtained with a radar system consisting of one transmitter and four receiver antennas were classified. Since the data collected from the four receiver antennas are in different positions, the collected reflection signals also differ. The signals collected with the range-time matrix content were divided into positive and negative parts, resulting in eight images from the four antennas. Instead of using all the data in CNN training, the images were first subjected to a reconstruction process with an autoencoder to reduce differences. As a result, it was observed that reconstructing the images with an autoencoder before classification with CNN increased the classification success. In conclusion, it was observed that the classification success of radar images can be increased by using a hybrid system with an autoencoder to reconstruct the images before classification with CNN.

Dynamic Programming-Based Track-before-Detect Algorithm for Weak Maneuvering Targets in Range–Doppler Plane

This paper focuses on detecting and tracking maneuvering weak targets in the range–Doppler (RD) plane with the track-before-detect (TBD) algorithm based on dynamic programming (DP). Traditional DP-TBD algorithms integrate target detection and tracking in their framework while searching the paths provided by a predefined model of the kinematic properties within the constraints allowed. However, both the approximate motion model used in the RD plane and the model mismatch caused when the target undergoes a maneuver can degrade the TBD performance sharply. To address these issues, this paper accurately describes the evolution of the RD equation based on Constant Acceleration (CA) and Coordinated Turn (CT) motion models with the process noise in the Cartesian coordinate system, and it also employs a recursive method to estimate the parameters in the equations for efficient energy accumulation and path searches. Facing the situation that targets energy accumulation during the DP iteration process will lead to an expansion of the target energy accumulation process. This paper designs a more efficient Optimization Function (OF) to inhibit the expansion effect, improve the resolution of the nearby targets, and increase the detection probability of the weak targets simultaneously. In addition, to search the trajectory more efficiently and accurately, we improved the process of DP multi-frame accumulation, thus reducing the computation amount of large-scale searches. Finally, the effectiveness of the proposed method for CA and CT motion target detection and tracking is verified by many of the simulation experiments that were conducted in this paper.

Digital-assisted multidisciplinary treatment for complex occlusal rehabilitation: an 18-month follow-up case report

BackgroundThis case report highlights the importance of standardized and fully digital sequential treatment in complex occlusal rehabilitation cases. To fully resolve the patient’s dental needs, such cases often require multidisciplinary interventions including periodontal therapy, endodontic treatment, anterior esthetics, implant restoration, and prosthetic rehabilitation. A fully digital workflow (including facial scanners, intraoral scanners, jaw motion tracking systems, virtual articulators, and computer-aided design software) streamlined the complex treatment, enhancing workflow simplicity, efficiency, visibility, and precision.Case presentationThe patient presented with decreased chewing efficiency of the upper and lower prostheses, along with unsatisfactory esthetic appearance of the anterior teeth. After physical examination and radiological assessment, this complex occlusal rehabilitation case required periodontal therapy, anterior esthetic enhancement, implant restoration, and fixed prosthetic rehabilitation. Therefore, a fully digital workflow was adopted. Full-crown prostheses were placed on teeth 13, 23, and 34; a fixed bridge encompassed positions 32 to 42, and single implant crowns were placed on teeth 35 and 36. Implant-supported fixed bridges were constructed for teeth 12 to 22 and 44 to 46, anchored by implants at teeth 12, 22, 44, and 46. All definitive prostheses were fabricated from zirconia ceramics, chosen for their durability and esthetic characteristics. Finally, restorations with satisfactory esthetic and functional characteristics were seated, preserving the tooth and its supporting structures. During treatment and follow-up, the T-scan occlusal analysis system was utilized to continuously monitor and guide the adjustment of occlusal distribution across the patient’s dental arches. After 18 months, the patient remains satisfied with the definitive restorations.ConclusionsThis report is intended to help dentists understand and implement standardized and fully digital workflows during the management of complex occlusal rehabilitation cases; it may also facilitate harmonious integration of esthetic and functional characteristics.

Neural mechanisms for secondary suppression of emotional distractors: Evidence from concurrent electroencephalography-magnetoencephalography data.

Distractor suppression allows us to remain on-task in the presence of distractions by filtering task-irrelevant information from ongoing cognitive processing and responding. Electrophysiological studies have revealed that this key feature of selective attention is a dynamic process that involves at least two distinct stages of processing. Two important aspects of these processing stages remain unclear: Whether the processing of emotional distractors at an earlier stage is automatic, as reflected in the N2/early posterior negativity (EPN) component; and what functional-anatomical brain systems are recruited in each stage. The present study addresses these issues by measuring brain activity with concurrent electroencephalography-magnetoencephalography (MEG) recordings while participants performed a combined rapid serial visual presentation and motion tracking task. Event-related potentials (ERP) showed significant effects of attentional capture and attentional modulation during two time windows marked by the N2/EPN and P3b ERP components. Source reconstruction of concurrent MEG measurements revealed activation of the left visual association cortex and anterior cingulate cortex during the N2/EPN time window, activation of the insula during the early phase of the P3b and anterior cingulate cortex activity during the later phase of the P3b. The findings provide novel evidence establishing a connection between the increased N2 response to negative pictures and the activation of the cingulate gyrus, which facilitates the suppression of distractions during demanding cognitive tasks. In addition, distinct activation patterns were observed in the insula and anterior cingulate cortex during the P3b time window, indicating that attentional control mediated by the anterior cingulate cortex operates to suppress the processing of distracting emotional stimuli. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Stretchable Iontronic Tactile Sensing Fabric.

The iontronic tactile sensing modality has garnered significant attention due to its exceptional sensitivity, immunity to noise, and versatility in materials. Recently, various formats of iontronic tactile sensors have been developed, including droplets, polymer films, paper, ionic gels, and fabrics. However, the stretchability of the current iontronic pressure sensing fabric is inadequate, hindered by the limited stretchiness of the ionic functional fabric. Incorporating a stretchable tactile sensing implement could enhance the wear comfortability by preventing relative movement and ensuring intimate contact between the sensor and the skin. The research focuses on the development of a stretchable iontronic pressure sensing (SIPS) fabric for monitoring diverse aspects of body health and movement in wearable applications. The tactile sensing structure is generated at the iontronic interface between highly stretchable ionic and conductive fabrics. In particular, the ionic fabric is prepared by coating a layer of polyurethane/ionic liquid gel onto a Spandex fabric. To showcase its remarkable sensitivity, stretchability, and ability to detect diverse body information, several application scenarios have been demonstrated including an elastic wristband for precise pulse wave detection, a flexible belt with multitactile sensing channels for respiration and motion tracking purposes, and a stretchable fabric cuff equipped with a high-resolution sensing array comprising 32 × 32 units for accurate gesture recognition.

Tracking movements of meagre (Argyrosomus regius) during the spawning season: Preliminary indications of off estuary spawning.

Understanding the spawning behavior of meagre (Argyrosomus regius) is crucial for fisheries management and conservation. Meagre forms large spawning aggregations in estuaries, yet details of its spawning grounds remain elusive. We tagged 41 individuals and monitored their movements throughout several spawning seasons. Detections inside estuaries were limited. Fish detected inside the Tejo estuary during the spawning season performed regular movements toward coastal areas, suggesting potential spawning outside estuaries. Our findings underscore the significance of understanding meagre's migratory patterns for effective fisheries management and conservation strategies, supporting the establishment of spatial and seasonal closures during the spawning season.