Search Results (205)

Visual body-tracking techniques have seen widespread adoption in applications such as motion analysis, human–machine interaction, tele-robotics and extended reality (XR). These systems typically provide 2D landmark coordinates corresponding to key limb positions. However, to construct a meaningful 3D kinematic model for body joint reconstruction, a mapping must be established between these visual landmarks and the underlying joint parameters of individual body parts. This paper presents a method for constructing a 3D kinematic model of the human hand using calibrated 2D landmark-tracking data augmented with depth information. The proposed approach builds a hierarchical model in which the palm serves as the root coordinate frame, and finger landmarks are used to compute both forward and inverse kinematic solutions. Through step-by-step examples, we demonstrate how measured hand landmark coordinates are used to define the palm reference frame and solve for joint angles for each finger. These solutions are then used in a visualization framework to qualitatively assess the accuracy of the reconstructed hand motion. As a future work, the proposed model offers a foundation for model-based hand kinematic estimation and has utility in scenarios involving occlusion or missing data. In such cases, the hierarchical structure and kinematic solutions can be used as generative priors in an optimization framework to estimate unobserved landmark positions and joint configurations. The novelty of this work lies in its model-based approach using real sensor data, without relying on wearable devices or synthetic assumptions. Although current validation is qualitative, the framework provides a foundation for future robust estimation under occlusion or sensor noise. It may also serve as a generative prior for optimization-based methods and be quantitatively compared with joint measurements from wearable motion-capture systems. Full article

Accurate full-body motion reconstruction from sparse sensors is crucial for VR/AR applications but remains challenging due to the under-constrained nature of limited observations and the computational constraints of mobile platforms. This paper presents LGSIK-Poser, a unified and lightweight framework that supports real-time motion reconstruction from heterogeneous sensor configurations, including head-mounted displays, handheld controllers, and up to three optional inertial measurement units, without requiring reconfiguration across scenarios. The model integrates temporally grouped LSTM modeling, anatomically structured graph-based reasoning, and region-specific inverse kinematics refinement to enhance end-effector accuracy and structural consistency. Personalized body shape is estimated using user-specific anthropometric priors within the SMPL model, a widely adopted parametric representation of human shape and pose. Experiments on the AMASS benchmark demonstrate that LGSIK-Poser achieves state-of-the-art accuracy with up to 48% improvement in hand localization, while reducing model size by 60% and latency by 22% compared to HMD-Poser. The system runs at 63.65 FPS with only 3.74 M parameters, highlighting its suitability for real-time immersive applications. Full article

Electroencephalography (EEG) is the only brain imaging method light enough and with the temporal precision to assess electrocortical dynamics during human locomotion. However, head motion during whole-body movements produces artifacts that contaminate the EEG and reduces ICA decomposition quality. We compared commonly used motion artifact removal approaches for reducing the motion artifact from the EEG during running and identifying stimulus-locked ERP components during an adapted flanker task. EEG was recorded from young adults during dynamic jogging and static standing versions of the Flanker task. Motion artifact removal approaches were evaluated based on their ICA’s component dipolarity, power changes at the gait frequency and harmonics, and ability to capture the expected P300 ERP congruency effect. Preprocessing the EEG using either iCanClean with pseudo-reference noise signals or artifact subspace reconstruction (ASR) led to the recovery of more dipolar brain independent components. In our analyses, iCanClean was somewhat more effective than ASR. Power was significantly reduced at the gait frequency after preprocessing with ASR and iCanClean. Finally, preprocessing using ASR and iCanClean also produced ERP components similar in latency to those identified in the standing flanker task. The expected greater P300 amplitude to incongruent flankers was identified when preprocessing using iCanClean. ASR and iCanClean may provide effective preprocessing methods for reducing motion artifacts in human locomotion studies during running. Full article

Due to the challenges in obtaining full-body point clouds and the time-consuming registration of parametric body models, we propose an end-to-end Front Point Cloud Parametric Body Regression Network (FPCR-Net). This network directly regresses the pose and shape parameters of a parametric body model from a single front point cloud of the human body. The network first predicts the label probabilities of corresponding body parts and the back point cloud from the input front point cloud. Then, it extracts equivariant features from both the front and predicted back point clouds, which are concatenated into global point cloud equivariant features. For pose prediction, part-level equivariant feature aggregation is performed using the predicted part label probabilities, and the rotations of each joint in the parametric body model are predicted via a self-attention layer. Shape prediction is achieved by applying mean pooling to part-invariant features and estimating the shape parameters using a self-attention mechanism. Experimental results, both qualitative and quantitative, demonstrate that our method achieves comparable accuracy in reconstructing body models from front point clouds when compared to implicit representation-based methods. Moreover, compared to previous regression-based methods, vertex and joint position errors are reduced by 43.2% and 45.0%, respectively, relative to the baseline. Full article

Occlusion remains a major challenge in person re-identification, as it often leads to incomplete or misleading visual cues. To address this issue, we propose a dual-branch occlusion-aware network (DOAN), which explicitly and implicitly enhances the model’s capability to perceive and handle occlusions. The proposed DOAN framework comprises two synergistic branches. In the first branch, we introduce an Occlusion-Aware Semantic Attention (OASA) module to extract semantic part features, incorporating a parallel channel and spatial attention (PCSA) block to precisely distinguish between pedestrian body regions and occlusion noise. We also generate occlusion-aware parsing labels by combining external human parsing annotations with occluder masks, providing structural supervision to guide the model in focusing on visible regions. In the second branch, we develop an occlusion-aware recovery (OAR) module that reconstructs occluded pedestrians to their original, unoccluded form, enabling the model to recover missing semantic information and enhance occlusion robustness. Extensive experiments on occluded, partial, and holistic benchmark datasets demonstrate that DOAN consistently outperforms existing state-of-the-art methods. Full article

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies. Full article

Studies of prehistoric burials are fundamental for understanding cultural human evolution. Those found in the San Teodoro cave (northeastern Sicily) are significant for the discovery at the turn of the 1930s and 1940s of at least four individuals (ST1–ST4). About 15–16 kyr ago, the bodies of ST1–ST4 were intentionally buried, apparently in a manner original to the context of prehistoric burials, namely by covering them with a continuous layer of red ochre found to connect the graves. Since the earliest excavations, plagued by clandestine digging, there is no material memory of the stratigraphic transition from the burial layer to the subsequent anthropogenic deposit through the red ochre, and nothing certain is known about the orientation of ST3, the presence of grave goods and the ochre cover related to this burial. Moreover, there is no exhaustive knowledge of how much is actually left of the anthropogenic layers described in the old literature. Based on field observations and 3D reconstruction of ST3’s skull position and deposits at the San Teodoro site, we provide insights into anthropological issues, such as the rediscovery of the red ochre vestiges that reasonably covered the ST3 burial, and the burial context of this individual, and shed light on what actually remains of the stratigraphic units described in the 1940s. Full article

Eutrophication has intensified in lacustrine systems across the American continent, which has been primarily driven by human activities such as intensive agriculture, wastewater discharge, and land-use change. This phenomenon adversely affects water quality, biodiversity, and ecosystem functioning. However, studies addressing the historical evolution of trophic states in lakes and reservoirs remain limited—particularly in tropical and subtropical regions. In this context, sedimentary records serve as invaluable archives for reconstructing the environmental history of water bodies. Paleolimnological approaches enable the development of robust chronologies to further analyze physical, geochemical, and biological proxies to infer long-term changes in primary productivity and trophic status. This review synthesizes the main methodologies used in paleolimnological research focused on trophic state reconstruction with particular attention to the utility of proxies such as fossil pigments, diatoms, chironomids, and elemental geochemistry. It further underscores the need to broaden spatial research coverage, fostering interdisciplinary integration and the use of emerging tools such as sedimentary DNA among others. High-resolution temporal records are critical for disentangling natural variability from anthropogenically induced changes, providing essential evidence to inform science-based lake management and restoration strategies under anthropogenic and climate pressures. Full article

Reconstructing accurate human body shapes from clothed images remains a challenge due to occlusion by garments and limitations of the existing methods. Traditional parametric models often require minimal clothing and involve high computational costs. To address these issues, we propose a lightweight algorithm that predicts body shape from clothed RGB images by leveraging pose estimation. Our method simultaneously extracts major joint positions and body features to reconstruct complete 3D body shapes, even in regions hidden by clothing or obscured from view. This approach enables real-time, non-invasive body modeling suitable for practical applications. Full article

Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and important method for human activity recognition. However, most WiFi-based activity recognition methods have limitations, such as using WiFi fingerprints to identify human activities. They either require extensive sample collection and training, are constrained by a fixed environmental layout, or rely on the precise positioning of transmitters (TXs) and receivers (RXs) within the space. If the positions are uncertain, or change, the sensing performance becomes unstable. To address the dependency of current WiFi indoor human activity trajectory reconstruction on the TX-RX position, we propose WiPIHT, a stable system for tracking indoor human activity trajectories using a small number of commercial WiFi devices. This system does not require additional hardware to be carried or locators to be attached, enabling passive, real-time, and accurate tracking and trajectory reconstruction of indoor human activities. WiPIHT is based on an innovative CSI channel analysis method, analyzing its autocorrelation function to extract location-independent real-time movement speed features of the human body. It also incorporates Fresnel zone and motion velocity direction decomposition to extract movement direction change patterns independent of the relative position between the TX-RX and the human body. By combining real-time speed and direction curve features, the system derives the shape of the human movement trajectory. Experiments demonstrate that, compared to existing methods, our system can accurately reconstruct activity trajectory shapes even without knowing the initial positions of the TX or the human body. Additionally, our system shows significant advantages in tracking accuracy, real-time performance, equipment, and cost. Full article

By reconstructing the concept of the “Mandate of Heaven”, the Zisi School grounded the universality of Confucian ethics in the ontological stipulations of Heaven’s Way, bridging the intellectual gap between Confucius’s practical ethics and Mencius’s theory of mind-nature. Central to their framework is the proposition that “Heaven’s mold imparts form to mankind; and imparts inherent pattern to objects”, which constructs a generative chain from the Mandate of Heaven to the nature of objects and human nature. The School posited that the Heavenly Way endows all objects with inherent patterns, while human nature, derived from the Mandate of Heaven, harbors latent moral potential activated through edification. By dialectically reconciling the “differentiation between Heaven and humans” with the “unity of Heaven and humanity”, the Zisi School emphasized both the transcendent authority of the Mandate of Heaven and human moral agency, forming an “immanent yet transcendent” ethical paradigm. However, theoretical limitations persist, including ambiguities in the certainty of innate goodness due to the separation of Heaven and human nature, mind-body dualism that risks formalizing moral practice, and latent fatalism in their concept of mandate. Despite these unresolved tensions, the Zisi School’s metaphysics laid the groundwork for Mencius’s theory of innate goodness, Xunzi’s legalist emphasis on ritual, and Song-Ming Neo-Confucian discourses on “Heaven’s inherent pattern”. As a pivotal transitional phase in Pre-Qin Confucianism, the Zisi School highlights the interplay between metaphysical grounding and pragmatic adaptability, underscoring the enduring dynamism of Confucian ethics. Full article

Accurately estimating 3D human pose and body shape from a single monocular image remains challenging, especially under poor lighting or occlusions. Traditional RGB-based methods struggle in such conditions, whereas single-pixel imaging (SPI) in the Near-Infrared (NIR) spectrum offers a robust alternative. NIR penetrates clothing and adapts to illumination changes, enhancing body shape and pose estimation. This work explores an SPI camera (850–1550 nm) with Time-of-Flight (TOF) technology for human detection in low-light conditions. SPI-derived point clouds are processed using a Vision Transformer (ViT) to align poses with a predefined SMPL-X model. A self-supervised PointNet++ network estimates global rotation, translation, body shape, and pose, enabling precise 3D human mesh reconstruction. Laboratory experiments simulating night-time conditions validate NIR-SPI’s potential for real-world applications, including human detection in rescue missions. Full article

This paper examines the philosophy of “Dao follows nature” (道法自然) and investigates how Daoist clothing transforms abstract cosmological concepts into a “wearable interface for spiritual practice” through the use of materials, colors, and patterns. By integrating symbol system analysis, material culture theory, and the philosophy of body practice, this study uncovers three layers of symbolic mechanisms inherent in Daoist attire. First, the materials embody the tension between “nature and humanity”, with the intentional imperfections in craftsmanship serving as a critique of technological alienation. Second, the color coding disrupts the static structure of the Five Elements system by dynamically shifting between sacred and taboo properties during rituals while simultaneously reconstructing symbolic meanings through negotiation with secular power. Third, the patterns (such as star constellations and Bagua) employ directional arrangements to transform the human body into a miniature cosmos, with dynamic designs offering a visual path for spiritual practice. This paper introduces the concept of a “dynamic practice interface”, emphasizing that the meaning of Daoist clothing is generated through the interaction of historical power, individual experience, and cosmological imagination. This research fills a critical gap in the symbolic system of Daoist art and provides a new paradigm for sustainable design and body aesthetics, framed from the perspective of “reaching the Dao through objects”. Full article

Time-of-flight (ToF) 3D cameras can obtain a real-time point cloud of human respiratory motion in medical robot scenes. Through this point cloud, real-time displacement information can be provided for the medical robot to avoid the robot injuring the human body during the operation due to the positioning deviation. However, multi-camera deployments face a conflict between spatial coverage and measurement accuracy due to the limitations of different types of ToF modulation. To address this, we design a multi-camera acquisition system incorporating different modulation schemes and propose a multi-view voxelized point cloud fusion algorithm utilizing a two-dimensional voxel block index table. Our algorithm first constructs a voxelized scene from multi-view depth maps. Then, the two-dimensional voxel block index table estimates and reconstructs overlapping regions across views. Experimental results demonstrate that fusing multi-view point clouds from low-precision 3D cameras achieves accuracy comparable to high-precision systems while maintaining the extensive spatial coverage of multi-view configurations. Full article

The digitization of cultural and intellectual heritage is expanding the research scope and methodologies of the scientific discipline of Humanities. Culturally diverse epigraphic systems reveal a range of methodological impediments on the way to their integration into digital epigraphic data preservation systems—EAGLE and FAIR ontologies predominantly based on Greco-Roman cultural categorization. We suggest an interdisciplinary approach—drawing from Heritage Studies, Cultural Epistemology, and Social Semiotics—to ensure the comprehensive encoding, preservation, and accessibility of at-risk cultural artifacts. Heritage Studies emphasize inscriptions as material reflections of historical memory. Cultural Epistemology helps us to understand how different knowledge systems influence data categorization, while semiotic analysis reveals how inscriptions function within their social and symbolic contexts. Together, these methods guide the integration of culturally specific information into broader digital infrastructures. The case of Ukrainian epigraphy illustrates how this approach can be applied to ensure that local traditions are accurately represented and not flattened by standardized international systems. We argue that the same methodology can also support the digitization of other non-Greco-Roman heritage. FAIR Ontology and EAGLE vocabularies prioritize standardization and interoperability, introducing text mining, GIS mapping, and digital visualization to trace patterns across the vast body of texts from different historical periods. Standardizing valuable elements of cultural categorization and reconstructing and integrating lost or underrepresented cultural narratives will expand the capacity of the above systems and will foster greater inclusivity in Humanities research. Ukrainian epigraphic classification systems offer a unique, granular approach to inscription studies as a worthwhile contribution to the broader cognitive and epistemological horizons of the Humanities. Through a balanced use of specificity and interoperability principles, this study attempts to contribute to epigraphic metalanguage by challenging the monocentric ontologies, questioning cultural biases in digital categorization, and promoting open access to diverse sources of knowledge production. Full article