Characteristics Of Human Motion Research Articles

Application of human-computer interaction technology integrating biomimetic vision system in animation design with a biomechanical perspective

The combination of Human-Computer Interaction (HCI) technology with biomimetic vision systems has transformational potential in animation design, particularly by incorporating biomechanical principles to create immersive and interactive experiences. Traditional animation approaches frequently lack sensitivity to real-time human motions, which can restrict engagement and realism. This study addresses this constraint by creating a framework that uses Virtual Reality (VR) and Augmented Reality (AR) to generate dynamic settings that include a variety of human activities, informed by biomechanical analysis. A biomimetic vision system is used to record these motions with wearable sensors, allowing for precise monitoring of user activity while considering biomechanical factors such as joint angles, force distribution, and movement patterns. The recorded data is preprocessed using Z-score normalization methods and extracted using Principal Component Analysis (PCA). This study proposed an Egyptian Vulture optimized Adjustable Long Short-Term Memory Network (EVO-ALSTM) technique for motion classification, specifically tailored to recognize biomechanical characteristics of human movements. Results demonstrate a significant improvement in precision (93%), F1-score (91%), accuracy (95%), and recall (90%) for the motion recognition system, highlighting the effectiveness of biomechanical insights in enhancing animation design. The findings indicate that integrating real-time biomechanical data into the animation process leads to more engaging and realistic user experiences. This study not only advances the subject of HCI but also provides the framework for future investigations into sophisticated animation technologies that use biomimetic and biomechanical systems.

Analysis of Volleyball Tactics and Movements Based on 3D Spatio-Temporal Residual Network

Sports technology and 3D motion recognition require models to be able to accurately identify athletes’ movements, which is crucial for training analysis, game strategy development and refereeing assistance decisions. To maintain a high recognition rate under different competition scenes, different athlete styles and different environmental conditions, and to ensure the practicality and reliability of the model, two independent 3D convolutional neural networks are applied to construct the action recognition model of Two-stream 3D Residual Networks. According to the temporal-spatial characteristics of human movements in video, the model introduces attention mechanism and combines time dimension to build a Two-stream 3D Residual Networks action recognition model integrating time-channel attention. The average accuracy of Top-1 and Top-5 action recognition models of Two-stream 3D Residual Networks integrated with pre-activation structure is 68.97% and 91.68%. The residual block of the pre-activated structure can enhance the model’s effectiveness. The average precision of Top-1 and Top-5 of action recognition model of two-stream 3D spatio-temporal residual network integrating time-channel attention is 85.73% and 92.05%, which has higher accuracy. The action recognition model of the two-stream 3D spatio-temporal residual network, which integrates time-channel attention, is accurate and achieves good recognition results with volleyball action recognition in real scenes.

Patient activity recognition for robust performance under varied illumination and cluttered background in indoor environment

Human Activity Recognition (HAR) holds significant importance in health and human-machine interaction. However, recognizing actions from 2D information faces challenges like occlusion, illumination variation, cluttered backgrounds, and view invariance. These hurdles are particularly pronounced in indoor patient monitoring settings due to fluctuating lighting conditions and cluttered backgrounds, which compromise the accuracy of activity recognition systems. A new architecture named illuminationRevive has been proposed to tackle this issue. Which utilizes an encoder-decoder convolutional neural network and image post-processing blocks to enhance the image's visual appearance. A new dataset comprising seven indoor physical activities has been proposed, created with contributions from thirty individuals aged 20 to 45. A hybrid fusion architecture is proposed to classify activities, integrating motion sequence information and body joint features. The proposed classification model incorporates generated Skeleton Motion History Images (SMHIs), collected human joint motion features from video frames, and novel kinematic and geometric features within window frames as inputs. The model can extract spatial and temporal feature vectors by integrating ResNet50-ViT (Residual Network-50 layers, Vision Transformer) and CNN-BiLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) layers. The suggested classification model was evaluated alongside state-of-the-art models using the LNMIIT-SMD (The LNM Institute of Information Technology-Skelton Motion Dataset) and established NTU-RGBD (Nanyang Technological University's Red Blue Green and Depth information) dataset. The evaluation aimed to assess the effectiveness of the proposed classification model architecture. Results demonstrate the superiority of the proposed model, achieving impressive accuracies of 98.21% on real-time data, 98.45% on the proposed dataset, and 97.12% on the NTU-RGBD dataset.

Machine learning-assisted wearable triboelectric-electromagnetic sensor for monitoring human motion feature

Machine learning-assisted wearable triboelectric-electromagnetic sensor for monitoring human motion feature

Parametric classification based on kinematic characteristics of objects for identification of criminals

The problem of identifying objects in conditions of their disguise and impossibility of assessment by video images is studied. Potential criminals can be considered as such objects. The task is to assess whether the observed object is a potential criminal element or an ordinary person.It is proposed to use kinematic characteristics of human motion as factors-features for solving the recognition problem, in particular, components of the speed and acceleration vectors of characteristic points of the body in the system of generalized coordinates using Lame functions.It is proposed to use a parametric classification algorithm to solve the identification problem with such a choice of factors-features. The proposed idea, if practically implemented, can create conditions for detecting criminals or identifying suspicious persons for subsequent control checks

Human motion recognition based on feature fusion and residual networks

Addressing the issue of low recognition accuracy in human motion detection when relying on a single feature, a novel approach integrating Frequency Modulated Continuous Wave (FMCW) radar technology with a Residual Network (ResNet) architecture has been proposed. This method commences by capturing the echo signals of six distinct human motions using an FMCW radar. These signals undergo preprocessing, followed by the application of a two-dimensional Fourier transform to derive the Range-time Map (RTM) and Doppler-time Map (DTM) representations of the human motions. To enhance the extraction and precise identification of human motion features, the conventional single-channel input structure of convolutional neural networks has been refined. Specifically, the ResNet18 residuals have been upgraded by incorporating Inception V1 modules. Furthermore, the Convolutional Block Attention Module (CBAM) has been integrated to engineer a dual-channel fusion residual network capable of recognizing and classifying human motions effectively. Empirical results demonstrate that the recognition accuracy of human motion detection has been enhanced by 1–4% when employing this dual-feature fusion structure, as compared to single-feature domain recognition. This improvement attests to the robust recognition capabilities of the proposed model.

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%.