Characteristics Of Human Motion Research Articles

Injury prevention and rehabilitation strategies in physical education: A machine learning-based approach using biomechanical characteristics

The field of sports biomechanics employs concepts from physics, biology, and engineering to investigate the mechanical characteristics of human motion and how they affect the body’s anatomy and functions. With the development of technology, sports biomechanics has emerged as a crucial component of sports medicine, training, and rehabilitation. To reduce the risk of injury and enhance athletic performance, sports biomechanics examines motions in sports in great length. The purpose of the study is to establish injury prevention and rehabilitation strategies for physical education (PE) teaching based on biomechanical characteristics. The early warning mode of sports injuries is recognized using advanced deep learning (DL) techniques, specifically resilient convolutional neural networks (RCNN). Biomechanical data from wearable sensors is used in this study to find trends related to sports-related injuries. A questionnaire survey of 228 students from various colleges was conducted. Individualized rehabilitation strategies will be provided to injured participants, taking into account their unique biomechanical deficiencies. These programs will be created in conjunction with physical therapists, and they will be updated in response to the patient’s progress toward recovery. The study found that their sports injuries were acute and chronic. This research demonstrated the treatment, prevention, and rehabilitation strategies of injuries in sports. The study emphasizes that biomechanical analysis is crucial for improving PE programs, which will eventually enhance students’ performance and overall health.

An Efficient Attentive-GRU Structure for Uncertainty Modelling of Crowdsourced Human Trajectories Under Building-obscured Urban Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of human mobility analysis and urban navigation, that can affect the performance of human behaviour modelling and location information acquisition. Existing uncertainty modelling algorithms towards the human movement trajectory are subjected to random and highly dynamic human motion characteristics and sampling and observation errors of Global Navigation Satellite System (GNSS) signals caused by the occlusion of buildings in urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of uncertainty modelling. To fill in this gap, this paper proposes an efficient attentive-GRU structure for uncertainty modelling of crowdsourced human trajectories under building-obscured urban scenes, that takes into account both temporal correlation and spatial correlation of human-originated GNSS trajectories and related motion features. A period of human motion data is modelled instead of only one or adjacent location points to avoid interference factors caused by the obstruction of urban buildings, and time-varying measurement and sampling errors are further estimated and combined with comprehensive human motion features to improve the accuracy of final uncertainty modelling. Comprehensive experiments indicate that compared with existing uncertainty modelling methods including physical models and deep-learning models, the proposed attentive-GRU structure realizes much better performance under different accuracy indexes.

An Enhanced Seamless Localization Framework Using Spatial-temporal Uncertainty Predictor Under Obscured Indoor and Outdoor Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of urban navigation, that can affect the performance of indoor and outdoor location information acquisition, especially under obscured scenarios. Existing multi-source fusion-based seamless positioning algorithms are subjected to random and highly dynamic human motion characteristics and changeable observation errors caused by the dynamic occlusions of human and buildings under urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of final multi-source fusion structure. To fill in this gap, this paper proposes an enhanced seamless localization framework using spatial-temporal uncertainty predictor under obscured indoor and outdoor scenes (ESL-STUP), that takes into account both temporal correlation and spatial correlation of trajectories provided by Wi-Fi, GNSS, and sensor-originated motion information. An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. Comprehensive experiments indicate that compared with existing multi-source fusion-based seamless positioning structure, the proposed ESL-STUP realizes much better performance under different scenes.

Fabrication of TPU-Supported Au-Bi2S3/Ti3C2Tx electrospun mat: Towards flexible and Multi-functional sensors for human motion and NH3 detection at room temperature

The potential applications of wearable sensors in health monitoring, micro-environment detection, and advanced soft electronic noses have attracted widespread attention. However, due to the inherent limitations of traditional organic flexible substrates, simultaneously achieving excellent flexibility, high sensitivity, robustness, and breathability remains a significant challenge. Herein, using the electrospinning method, a flexible TPU substrate film was prepared, and Bi2S3/Ti3C2Tx nanocomposite materials were synthesized through a simple one-step hydrothermal method. Au nanoparticles were attached to the surface of the composite material using sodium borohydride, and finally, the surface impregnation method was used to successfully prepare the Au-Bi2S3/Ti3C2Tx-TPU flexible electrospun mat. The as-prepared sensor demonstrated an outstanding response of 154 % to 100 ppm NH3 at room temperature, coupled with high selectivity, repeatability, and long-term stability. The strain properties of the sensor were also evaluated, showcasing its potential for human motion and physiological feature detection, such as breathing, swallowing, and limb movements. The study’s findings indicated that the Au-Bi2S3/Ti3C2Tx-TPU electrospun mat sensor has promising applications in flexible electronics, wearable environmental monitoring devices, and human healthy monitoring.

Kinesiology-Inspired Assessment of Intrusion Risk Based on Human Motion Features

Kinesiology-Inspired Assessment of Intrusion Risk Based on Human Motion Features

Finite element analysis of a three-dimensional cervical spine model with muscles based on CT scan data

Background The incidence of cervical spondylosis is increasing, gradually affecting people’s normal lives. Establishing a finite element model of the cervical spine is one of the methods for studying cervical spondylosis. MRI (Magnetic Resonance Imaging) still has certain difficulties in transitioning from human imaging to establishing muscle models suitable for finite element analysis. Medical software provides specific morphologies and can generate muscle finite element models. Additionally, there is little research on the static analysis of cervical spine finite element models with solid muscle. Purpose A new method is proposed for establishing a finite element model of the cervical spine based on CT (Computed Tomography) data and medical software, and the model’s effectiveness is validated. Human movement characteristics based on the force distribution in various parts are analyzed and predicted. Methods The muscle model is reconstructed in medical software and a three-dimensional finite element model of the entire cervical spine (C0–C7) is established by combining muscle models with CT vertebral data models. 1.5 Nm of load is applied to the finite element model to simulate the cervical spine movement. Results The finite element model was successfully established, and effectiveness was verified. Stress variations in various parts under six movements were obtained. The effectiveness of the model was basically verified. Conclusion The finite element model of the cervical spine for mechanical analysis can be successfully established by using medical software and CT data. In daily life, the C2–3, C3–4, C4–C5 intervertebral discs, rectus capitis posterior major, longus colli, and obliquus capitis inferior are more prone to injury.

Tactile shape discrimination for moving stimuli

In this study, we explored spatial-temporal dependencies and their impact on the tactile perception of moving objects. Building on previous research linking visual perception and human movement, we examined if an imputed motion mechanism operates within the tactile modality. We focused on how biological coherence between space and time, characteristic of human movement, influences tactile perception. An experiment was designed wherein participants were stimulated on their right palm with tactile patterns, either ambiguous (incongruent conditions) or non-ambiguous (congruent conditions) relative to a biological motion law (two-thirds power law) and asked to report perceived shape and associated confidence. Our findings reveal that introducing ambiguous tactile patterns (1) significantly diminishes tactile discrimination performance, implying motor features of shape recognition in vision are also observed in the tactile modality, and (2) undermines participants’ response confidence, uncovering the accessibility degree of information determining the tactile percept’s conscious representation. Analysis based on the Hierarchical Drift Diffusion Model unveiled the sensitivity of the evidence accumulation process to the stimulus’s informational ambiguity and provides insight into tactile perception as predictive dynamics for reducing uncertainty. These discoveries deepen our understanding of tactile perception mechanisms and underscore the criticality of predictions in sensory information processing.

A Generative Model to Embed Human Expressivity into Robot Motions.

This paper presents a model for generating expressive robot motions based on human expressive movements. The proposed data-driven approach combines variational autoencoders and a generative adversarial network framework to extract the essential features of human expressive motion and generate expressive robot motion accordingly. The primary objective was to transfer the underlying expressive features from human to robot motion. The input to the model consists of the robot task defined by the robot's linear velocities and angular velocities and the expressive data defined by the movement of a human body part, represented by the acceleration and angular velocity. The experimental results show that the model can effectively recognize and transfer expressive cues to the robot, producing new movements that incorporate the expressive qualities derived from the human input. Furthermore, the generated motions exhibited variability with different human inputs, highlighting the ability of the model to produce diverse outputs.

Mobility difference index: a quantitative method for detecting human mobility difference

ABSTRACT Differences in human mobility reflect temporal variations and spatial differences in urban spaces, including regional functions, physical environments, and geographical sentiments. Accurately quantifying these differences is critical for understanding and managing cities. However, existing measurement methods overlook the spatial distribution of population movement, which limits the ability to compare spatial differences in human mobility. Separate treatment of the spatial distribution, flux, and distance of human movement increases the complexity and uncertainty of understanding geographic phenomena. Therefore, we propose a flow-based location measure, termed the mobility difference index (MDI), that fuses multidimensional movement characteristics to quantify temporal variations and spatial differences in human mobility. The method quantifies the differences in human mobility by calculating the minimum transformation cost between two sets of origin-destination flows based on optimal transport theory. Simulation experiments confirmed the advantage of the MDI in perceiving the multidimensional characteristics of human movement, particularly regarding spatial distribution. We examined mobile signaling and positioning data from Wuhan and found that the proposed MDI could effectively identify the spatial and temporal dependencies of variations in human mobility and heterogeneous effects of spatial semantics and distance on spatial differences in human mobility.

Retracted: A Table Tennis Motion Correction System Based on Human Motion Feature Recognition

Retracted: A Table Tennis Motion Correction System Based on Human Motion Feature Recognition

Hand-held rolling magnetic-spring energy harvester: Design, analysis, and experimental verification

Aiming at the random and low-frequency characteristics of human motion, a hand-held rolling magnetic-spring energy harvester is proposed to scavenge energy from handshaking. The harvester applies the rolling movement of a magnetically suspended magnet and possesses the merit of low damping. The dynamic model is established and the open-circuit voltages are predicted by combining Faraday's law of electromagnetic induction and finite element analysis. By calculating magnetic flux through coils, the effect of the coil’s position on energy harvesting efficiency is studied through numerical and experimental approaches. Experimental results under harmonic excitations indicate that the coil positioned directly below the stable equilibrium position of the moving magnet has the best output capability, and a maximum open-circuit voltage of 1.33 V and a half-power bandwidth of 1.61 Hz is achieved at 0.3 g. Under handshaking excitation, an instantaneous peak power of 20.15 mW is achieved, along with a maximum average power of 2.22 mW. The corresponding volume and mass power densities are respectively 26.04 μW/cm3 and 23.28 μW/g. Furthermore, handshaking excitation in 5 s to charge a capacitor of 470 μF could enable the watch, time display, timer, and hygrothermograph to operate for 1100 s, 513 s, 86 s, and 35 s respectively, demonstrating the brilliant application foreground of the proposed harvester.

Automatic Evaluation of Functional Movement Screening Based on Attention Mechanism and Score Distribution Prediction

Functional movement screening (FMS) is a crucial testing method that evaluates fundamental movement patterns in the human body and identifies functional limitations. However, due to the inherent complexity of human movements, the automated assessment of FMS poses significant challenges. Prior methodologies have struggled to effectively capture and model critical human features in video data. To address this challenge, this paper introduces an automatic assessment approach for FMS by leveraging deep learning techniques. The proposed method harnesses an I3D network to extract spatiotemporal video features across various scales and levels. Additionally, an attention mechanism (AM) module is incorporated to enable the network to focus more on human movement characteristics, enhancing its sensitivity to diverse location features. Furthermore, the multilayer perceptron (MLP) module is employed to effectively discern intricate patterns and features within the input data, facilitating its classification into multiple categories. Experimental evaluations conducted on publicly available datasets demonstrate that the proposed approach achieves state-of-the-art performance levels. Notably, in comparison to existing state-of-the-art (SOTA) methods, this approach exhibits a marked improvement in accuracy. These results corroborate the efficacy of the I3D-AM-MLP framework, indicating its significance in extracting advanced human movement feature expressions and automating the assessment of functional movement screening.

LSTM-MLP BASED UNCERTAINTY MODELLING APPROACH FOR COMPLEX HUMAN INDOOR TRAJECTORY

Abstract. Modelling the movement uncertainty of human indoor trajectory consist of an essential part in promoting the performance of smart city related applications. At this stage, the existing uncertainty modelling algorithms usually take the constant sampling error or measurement error into consideration and cannot adapt well to the changeable human motion modes and complex handheld modes of smartphones. To fill this gap, this paper applied the Long Short-Term Memory (LSTM) network for continuous prediction of uncertainty error of human indoor trajectory with complex motion modes and detected indoor landmark points. The human motion information including handheld modes, walking speed, and heading information in extracted and fused with detected landmark points for reconstruction of human indoor trajectory under large-scale areas using Gradient Descent (GD) algorithm. In addition, the hybrid LSTM and Multilayer Perceptron (MLP) network is adopted for uncertainty error prediction, by considering both sampling error and measurement error in a specific time period, and the reconstructed trajectory with human motion features are modelled as the input vector for model training with the ground-truth uncertainty error as reference. Comprehensive experiments on real-world collected dataset indicate that the proposed LSTM-assisted uncertainty modelling algorithm has robust outperformance in uncertainty error prediction and uncertainty region definition compared with traditional uncertainty modelling approaches.

Human Movement Recognition Based on 3D Point Cloud Spatiotemporal Information from Millimeter-Wave Radar

Human movement recognition is the use of perceptual technology to collect some of the limb or body movements presented. This practice involves the use of wireless signals, processing, and classification to identify some of the regular movements of the human body. It has a wide range of application prospects, including in intelligent pensions, remote health monitoring, and child supervision. Among the traditional human movement recognition methods, the widely used ones are video image-based recognition technology and Wi-Fi-based recognition technology. However, in some dim and imperfect weather environments, it is not easy to maintain a high performance and recognition rate for human movement recognition using video images. There is the problem of a low recognition degree for Wi-Fi recognition of human movement in the case of a complex environment. Most of the previous research on human movement recognition is based on LiDAR perception technology. LiDAR scanning using a three-dimensional static point cloud can only present the point cloud characteristics of static objects; it struggles to reflect all the characteristics of moving objects. In addition, due to its consideration of privacy and security issues, the dynamic millimeter-wave radar point cloud used in the previous study on the existing problems of human body movement recognition performance is better, with the recognition of human movement characteristics in non-line-of-sight situations as well as better protection of people’s privacy. In this paper, we propose a human motion feature recognition system (PNHM) based on spatiotemporal information of the 3D point cloud of millimeter-wave radar, design a neural network based on the network PointNet++ in order to effectively recognize human motion features, and study four human motions based on the threshold method. The data set of the four movements of the human body at two angles in two experimental environments was constructed. This paper compares four standard mainstream 3D point cloud human action recognition models for the system. The experimental results show that the recognition accuracy of the human body’s when walking upright can reach 94%, the recognition accuracy when moving from squatting to standing can reach 84%, that when moving from standing to sitting can reach 87%, and the recognition accuracy of falling can reach 93%.

DanceTrend: An Integration Framework of Video-Based Body Action Recognition and Color Space Features for Dance Popularity Prediction

Background: With the rise of user-generated content (UGC) platforms, we are witnessing an unprecedented surge in data. Among various content types, dance videos have emerged as a potent medium for artistic and emotional expression in the Web 2.0 era. Such videos have increasingly become a significant means for users to captivate audiences and amplify their online influence. Given this, predicting the popularity of dance videos on UGC platforms has drawn significant attention. Methods: This study postulates that body movement features play a pivotal role in determining the future popularity of dance videos. To test this hypothesis, we design a robust prediction framework DanceTrend to integrate the body movement features with color space information for dance popularity prediction. We utilize the jazz dance videos from the comprehensive AIST++ street dance dataset and segment each dance routine video into individual movements. AlphaPose was chosen as the human posture detection algorithm to help us obtain human motion features from the videos. Then, the ST-GCN (Spatial Temporal Graph Convolutional Network) is harnessed to train the movement classification models. These pre-trained ST-GCN models are applied to extract body movement features from our curated Bilibili dance video dataset. Alongside these body movement features, we integrate color space attributes and user metadata for the final dance popularity prediction task. Results: The experimental results endorse our initial hypothesis that the body movement features significantly influence the future popularity of dance videos. A comprehensive evaluation of various feature fusion strategies and diverse classifiers discern that a pre–post fusion hybrid strategy coupled with the XGBoost classifier yields the most optimal outcomes for our dataset.

Automatic Tracking Method for 3D Human Motion Pose Using Contrastive Learning

Automatic tracking of three-dimensional (3D) human motion pose has the potential to provide corresponding technical support in various fields. However, existing methods for tracking human motion pose suffer from significant errors, long tracking times and suboptimal tracking results. To address these issues, an automatic tracking method for 3D human motion pose using contrastive learning is proposed. By using the feature parameters of 3D human motion poses, threshold variation parameters of 3D human motion poses are computed. The golden section is introduced to transform the threshold variation parameters and extract the features of 3D human motion poses by comparing the feature parameters with the threshold of parameter variation. Under the supervision of contrastive learning, a constraint loss is added to the local–global deep supervision module of contrastive learning to extract local parameters of 3D human motion poses, combined with their local features. After normalizing the 3D human motion pose images, frame differences of the background image are calculated. By constructing an automatic tracking model for 3D human motion poses, automatic tracking of 3D human motion poses is achieved. Experimental results demonstrate that the highest tracking lag is 9%, there is no deviation in node tracking, the pixel contrast is maintained above 90% and only 6 sub-blocks have detail loss. This indicates that the proposed method effectively tracks 3D human motion poses, tracks all the nodes, achieves high accuracy in automatic tracking and produces good tracking results.

Interpretable Multi-Channel Capsule Network for Human Motion Recognition

Recently, capsule networks have emerged as a novel neural network architecture for human motion recognition owing to their enhanced interpretability compared to traditional deep learning networks. However, the characteristic features of human motion are often distributed across distinct spatial dimensions and existing capsule networks struggle to independently extract and combine features across multiple spatial dimensions. In this paper, we propose a new multi-channel capsule network architecture that extracts feature capsules in different spatial dimensions, generates a multi-channel capsule chain with independent routing within each channel, and culminates in the aggregation of information from capsules in different channels to activate categories. The proposed structure endows the network with the capability to independently cluster interpretable features within different channels; aggregates features across channels during classification, thereby enhancing classification accuracy and robustness; and also presents the potential for mining interpretable primitives within individual channels. Experimental comparisons with several existing capsule network structures demonstrate the superior performance of the proposed architecture. Furthermore, in contrast to previous studies that vaguely discussed the interpretability of capsule networks, we include additional visual experiments that illustrate the interpretability of the proposed network structure in practical scenarios.

Learning full context feature for human motion prediction

Human motion prediction aims to predict the target poses given the previous poses. Most existing methods are devoted to extracting richer motion features from only the given previous poses to predict the target poses. However, we consider that the post poses after the target poses are helpful in acquiring the context feature and constraint between neighbor motions, which is also important for motion prediction. In this paper, we explore to make use of the post motion information for a powerful human motion prediction method. Specifically, we propose a human motion prediction model which learns the motion constraint from both the previous and post poses, in order to fully utilize the context features of the target poses. During training, the proposed memory dictionary module is used to learn the mapping from previous features to post features. In testing, the proposed memory dictionary module fully exploits the learned mapping to calculate the future motion feature according to the input previous feature. Thus, the context feature of human motion is enriched in our method. We evaluate the proposed method on two large-scale datasets, Human3.6M and CMU-Mocap. The experimental results demonstrate that our method improves the motion prediction performance, especially for long-term human motion.

Improving Small-Scale Human Action Recognition Performance Using a 3D Heatmap Volume.

In recent years, skeleton-based human action recognition has garnered significant research attention, with proposed recognition or segmentation methods typically validated on large-scale coarse-grained action datasets. However, there remains a lack of research on the recognition of small-scale fine-grained human actions using deep learning methods, which have greater practical significance. To address this gap, we propose a novel approach based on heatmap-based pseudo videos and a unified, general model applicable to all modality datasets. Leveraging anthropometric kinematics as prior information, we extract common human motion features among datasets through an ad hoc pre-trained model. To overcome joint mismatch issues, we partition the human skeleton into five parts, a simple yet effective technique for information sharing. Our approach is evaluated on two datasets, including the public Nursing Activities and our self-built Tai Chi Action dataset. Results from linear evaluation protocol and fine-tuned evaluation demonstrate that our pre-trained model effectively captures common motion features among human actions and achieves steady and precise accuracy across all training settings, while mitigating network overfitting. Notably, our model outperforms state-of-the-art models in recognition accuracy when fusing joint and limb modality features along the channel dimension.

KD-Former: Kinematic and dynamic coupled transformer network for 3D human motion prediction

Recent studies have made remarkable progress on 3D human motion prediction by describing motion with kinematic knowledge. However, kinematics only considers the 3D positions or rotations of human skeletons, failing to reveal the physical characteristics of human motion. Motion dynamics reflects the forces between joints, explicitly encoding the skeleton topology, whereas rarely exploited in motion prediction. In this paper, we propose the Kinematic and Dynamic coupled transFormer (KD-Former), which incorporates dynamics with kinematics, to learn powerful features for high-fidelity motion prediction. Specifically, We first formulate a reduced-order dynamic model of human body to calculate the forces of all joints. Then we construct a non-autoregressive encoder-decoder framework based on the transformer structure. The encoder involves a kinematic encoder and a dynamic encoder, which are respectively responsible for extracting the kinematic and dynamic features for given history sequences via a spatial transformer and a temporal transformer. Future query sequences are decoded in parallel in the decoder by leveraging the encoded kinematic and dynamic information of history sequences. Experiments on Human3.6M and CMU MoCap benchmarks verify the effectiveness and superiority of our method. Code will be available at: https://github.com/wslh852/KD-Former.git.