Motion Capture Technology Research Articles

The biomechanics of public speaking: Enhancing political communication and persuasion through posture and gesture analysis

Effective public speaking is critical in political communication, where non-verbal cues such as posture, gestures, facial expressions, and body movements significantly influence audience perception. Despite the known impact of these biomechanics, research in diverse cultural contexts remains limited. This study addresses this gap by examining the biomechanics of public speaking in the Philippines, focusing on 16 political speakers and 211 audience participants from varied demographic backgrounds. The study uses advanced motion capture technology, high-definition video recordings, and audience perception surveys to investigate how specific non-verbal elements affect audience engagement and perception. Challenges include accurately measuring and isolating the effects of these biomechanical factors amidst varied cultural and linguistic influences. The findings reveal that an upright posture significantly enhances perceived confidence (r = 0.72, p = 0.003) and trustworthiness (r = 0.65, p = 0.004), while high-frequency gestures positively correlate with clarity (β = 0.47, p = 0.008) and persuasiveness (β = 0.66, p = 0.003). Head movements, such as nodding, significantly increase audience engagement (F-value = 5.73, p = 0.002), and high-intensity smiling enhances emotional responses (t-value = 4.86, p = 0.001). These results underscore the importance of biomechanics in political communication, demonstrating that specific gestures and postures are critical in conveying confidence and persuasiveness. The study contributes to the field by offering insights into the strategic use of body language, particularly in multicultural settings, to enhance audience engagement and speaker effectiveness.

Biomechanical and machine learning approaches to automating the identification of musical styles and emotions through human motion analysis

This study explores the intricate relationship between biomechanical movements and musical expression, focusing on the identification of musical styles and emotions. Violin performance is characterized by complex interactions between physical actions—such as bowing techniques, finger placements, and posture—and the resulting acoustic output. Recent advances in motion capture technology and sound analysis have enabled a more objective examination of these processes. However, the current literature frequently addresses biomechanics and acoustic features in isolation, lacking an integrated understanding of how physical movements translate into specific musical expressions. Machine Learning (ML), particularly Long Short-Term Memory (LSTM) networks, provides a promising avenue for bridging this gap. LSTM models are adept at capturing temporal dependencies in sequential data, making them suitable for analyzing the dynamic nature of violin performance. In this work, they have proposed a comprehensive model that combines biomechanical analysis with Mel-spectrogram-based LSTM modeling to automate the identification of musical styles and emotions in violin performances. Using motion capture systems, Inertial Measurement Units (IMUs), and high-fidelity audio recordings, we collected synchronized biomechanical and acoustic data from violinists performing various musical excerpts. The LSTM model was trained on this dataset to learn the intricate connections between physical movements and the acoustic features of each performance. Key findings from the study demonstrate the effectiveness of this integrated approach. The LSTM model achieved a validation accuracy of 92.5% in classifying musical styles and emotions, with precision, recall, and F1-score reaching 94.3%, 92.6%, and 93.4%, respectively, by the 100th epoch. The analysis also revealed strong correlations between specific biomechanical parameters, such as shoulder joint angle and bowing velocity, and acoustic features, like sound intensity and vibrato amplitude.

Human motion and musical expression: Exploring the link between physical movement and emotional resonance in performance

Musical performance has traditionally been understood as an auditory experience, with emotions conveyed primarily through sound. However, emerging research highlights the role of physical movement—gestures, posture, and body language—in enhancing emotional expression and creating a deeper connection between performers and their audiences. While existing studies have explored some aspects of this relationship, there is limited understanding of how specific movements modulate emotional responses across different musical genres. This study addresses these gaps by investigating the interaction between human motion and musical expression, focusing on the emotional resonance experienced by performers and audience members. The need for this study arises from the lack of comprehensive data on the correlation between movement and emotional engagement during musical performances, particularly concerning different genres. Using motion capture, biometric sensors, and facial recognition technology, the research analyzed the performances of professional and amateur musicians across classical, jazz, and contemporary music. Audience members’ emotional responses were captured through physiological data and post-performance surveys. One limitation of this study is that it focused solely on live performance environments, leaving digital or virtual performances unexplored. Key findings reveal that expressive movements increased emotional intensity ratings by 1.6 points (p < 0.001), with heart rate rising by 6.9 BPM (p < 0.0012) and electrodermal activity increasing by 1.6 µS (p < 0.0008). Movement synchronization with musical elements, especially during climaxes, strongly correlated with heightened emotional responses (r = 0.85, p < 0.01). Classical performances exhibited the highest synchronization between movement and emotion, with audience emotional intensity peaking at 8.3 on a Likert scale.

Evaluation of changes in fixed flexion deformity following medial unicompartmental knee arthroplasty.

While residual fixed flexion deformity (FFD) in unicompartmental knee arthroplasty (UKA) has been associated with worse functional outcomes, limited evidence exists regarding FFD changes. The objective of this study was to quantify FFD changes in patients with medial unicompartmental knee arthritis undergoing UKA, and investigate any correlation with clinical outcomes. This study included 136 patients undergoing robotic arm-assisted medial UKA between January 2018 and December 2022. The study included 75 males (55.1%) and 61 (44.9%) females, with a mean age of 67.1 years (45 to 90). Patients were divided into three study groups based on the degree of preoperative FFD: ≤ 5°, 5° to ≤ 10°, and > 10°. Intraoperative optical motion capture technology was used to assess pre- and postoperative FFD. Clinical FFD was measured pre- and postoperatively at six weeks and one year following surgery. Preoperative and one-year postoperative Oxford Knee Scores (OKS) were collected. Overall, the median preoperative navigated (NAV) FFD measured 6.0° (IQR 3.1 to 8), while the median postoperative NAV FFD was 3.0° (IQR 1° to 4.4°), representing a mean correction of 49.2%. The median preoperative clinical FFD was 5° (IQR 0° to 9.75°) for the entire cohort, which decreased to 3.0° (IQR 0° to 5°) and 2° (IQR 0° to 3°) at six weeks and one year postoperatively, respectively. A statistically significant improvement in PROMs compared with baseline was evident in all groups (p < 0.001). Regression analyses showed that participants who experienced a larger FFD correction, showed greater improvement in PROMs (β = 0.609, p = 0.049; 95% CI 0.002 to 1.216). This study found that UKA was associated with an approximately 50% improvement in preoperative FFD across all three examined groups. Participants with greater correction of FFD also demonstrated larger OKS gains. These findings could prove a useful augment to clinical decision-making regarding candidacy for UKA and anticipated improvements in FFD.

The Synergistic Effect of Biomechanics and Psychological Feedback in Physical Education Teaching: Enhancing Motor Skills and Psychological Resilience

The combination of biomechanics and psychological feedback presents a unique approach to improving physical education outcomes. Biomechanics offers a detailed understanding of movement, allowing for the optimization of motor skills based on scientific principles. Meanwhile, psychological feedback helps students adapt mentally, fostering resilience and improving performance under pressure. This research explores how the dual application of biomechanical analysis and psychological feedback can enhance both physical and mental capacities in students during physical education. Through motion capture technology and real-time data analysis, motor skill performance is broken down into key components such as force, speed, and coordination. Students receive targeted feedback, allowing them to refine their techniques efficiently. Concurrently, psychological feedback mechanisms, such as self-reflection and stress management strategies, are introduced to help students build mental resilience and maintain focus. Results are a significant improvement in both motor skills and psychological well-being. Students displayed greater accuracy and efficiency in performing complex physical tasks and reported reduced anxiety and stress during physical education activities. The synergy between biomechanics and psychological feedback not only improved motor performance but also enhanced students' overall experience, making physical education more engaging and effective.

Spatial adaptation in children with autism - a study based on behavioural dynamic video data

Children with autism show unique behavioural patterns and sensitivity to spatial elements; therefore, size and furniture arrangement at rehabilitation facilities can affect behaviour. In this study, spatial adaptation during three situations (learning, playing with toys, and finding objects) was evaluated for different spatial sizes in children with autism using video data. In particular, 330 videos of training sessions involving 15 boys with autism aged 4–6 years in spaces classified as training space (large, medium, and small) and immediate surroundings (activity areas and hallways) were evaluated. Skeletal data were acquired using AI motion capture technology and transformed into movement volume and positions using Grasshopper. Differences between children with autism in normal and aggressive states in different spaces were analyzed. The movement of volume increased in all space sizes during the aggressive state, especially in large spaces. In large spaces, the movement volume during learning was significantly larger, but object-seeking was significantly smaller in the aggressive state than in the normal state. In the immediate surrounding, the volume of movement varied significantly across behavioural states. Additionally, compared with the normal state, the movement location of children with autism in the aggressive state were more dispersed. The movement of location for learning, activity area and hallway were closer to the wall. Playing with toys and finding objects overlapped more with the furniture. This study provides a basis for the spatial adaptation of intervention services for children with autism and a reference for comparisons with current design standards.

Advances in Markerless Motion Capture Systems: A Review of OpenCap and Its Applications

Motion capture technology has long been a cornerstone of biomechanical research and analysis, traditionally relying on marker-based systems. However, these systems have inherent limitations, including high costs, time-consuming setup, and constraints on natural movement. Recent advancements in computer vision and machine learning have paved the way for markerless motion capture systems, such as OpenCap, which offer a more accessible and natural approach to biomechanical analysis. This review focuses on OpenCap, exploring its accuracy, challenges, and future potential, while comparing it with traditional motion capture technologies and discussing its applications in sports, clinical rehabilitation, and everyday use. Areas in need of refinement, such as improving pose estimation algorithms and addressing inter-trial variability, are identified as key future research directions. Other markerless systems are also compared in terms of advantages, limitations, and application. The findings suggest that while OpenCap and similar systems hold significant promise, further research and refinement are necessary to fully realize their potential and integrate them seamlessly into various biomechanical applications.

Using Motion Capture Technology in the Instrumented Timed Up and Go Test to Detect the Risk of Falling in Aged Adults.

Despite efforts made by medicine and technology, the incidence of falls in older adults is still increasing. Therefore, early detection of the falling risk is becoming increasingly important for falling prevention. The Timed Up and Go (TUG) test is a well-accepted tool to assess mobility and can be used in predicting future fall risk in aged adults. In clinical practice, the total time to complete the test is the main outcome measure of the TUG test. Owing to its simplicity and generality, the traditional TUG test has been considered a global test for movement analysis. However, recently, researchers have attempted to split the TUG test into subcomponents and have updated its score system for further investigations. The instrumented Time Up and Go (iTUG) test, which is a new modification of the traditional TUG test, has been reported to be a sensitive tool for predicting movement disorders and the risk of falls in older adults. The goal of the present study was to analyze the iTUG test subcomponents using motion capture technology, and to determine which iTUG test subtasks are related to the potential risk of future falls.

Enhancing dance education through convolutional neural networks and blended learning

This article explores the evolving landscape of dance teaching, acknowledging the transformative impact of the internet and technology. With the emergence of online platforms, dance education is no longer confined to physical classrooms but can extend to virtual spaces, facilitating a more flexible and accessible learning experience. Blended learning, integrating traditional offline methods and online resources, offers a versatile approach that transcends geographical and temporal constraints. The article highlights the utilization of the dual-wing harmonium (DWH) multi-view metric learning (MVML) algorithm for facial emotion recognition, enhancing the assessment of students’ emotional expression in dance performances. Moreover, the integration of motion capture technology with convolutional neural networks (CNNs) facilitates a precise analysis of students’ dance movements, offering detailed feedback and recommendations for improvement. A holistic assessment of students’ performance is attained by combining the evaluation of emotional expression with the analysis of dance movements. Experimental findings support the efficacy of this approach, demonstrating high recognition accuracy and offering valuable insights into the effectiveness of dance teaching. By embracing technological advancements, this method introduces novel ideas and methodologies for objective evaluation in dance education, paving the way for enhanced learning outcomes and pedagogical practices in the future.

Using motion capture technology to assess gait patterns in runners and investigate injury risks: A short review

Motion capture technology has become integral in studying gait patterns among runners to understand their implications for injury risks. This review synthesizes current literature to examine methodologies and findings related to using motion capture technology for gait analysis in runners. A systematic search was conducted across major academic databases to identify relevant studies published in peer-reviewed journals and conference proceedings. Inclusion criteria focused on studies utilizing motion capture technology to assess biomechanical factors associated with running-related injuries.The review highlights key biomechanical parameters analyzed through motion capture, including joint angles, forces, and muscle activations during running. Studies consistently report on the relationship between gait abnormalities and various injuries such as stress fractures, patellofemoral pain syndrome, and Achilles tendonitis. Practical implications for injury prevention strategies are discussed including personalized gait assessments, targeted interventions such as gait retraining, and advancements in footwear design aimed at optimizing running mechanics and reducing injury risks. Motion capture technology offers a robust platform for advancing our understanding of gait mechanics in runners and their impact on injury prevention. Future research directions should focus on standardizing methodologies, integrating multi-modal data, and applying findings to enhance clinical practice and athletic performance.

The Validity and Usability of Markerless Motion Capture and Inertial Measurement Units for Quantifying Dynamic Movements

ABSTRACT Purpose Motion capture technology is quickly evolving providing researchers, clinicians, and coaches with more access to biomechanics data. Markerless motion capture and inertial measurement units (IMUs) are continually developing biomechanics tools that need validation for dynamic movements before widespread use in applied settings. This study evaluated the validity of a markerless motion capture, IMU, and red, green, blue, and depth (RGBD) camera system as compared to marker-based motion capture during countermovement jumps, overhead squats, lunges, and runs with cuts. Methods Thirty adults were recruited for this study (sex: 18 females, 12 males; age: 25.4 ± 8.6 years; height: 1.71 ± 0.08 m; weight: 71.6 ± 11.5 kg). Data were collected simultaneously with four motion capture technologies (i.e., Vicon – marker-based, Theia/Optitrack – markerless, APDM Opals – IMUs, and Vald HumanTrak – RGBD camera). System validity for lower and upper body joint angles was evaluated using bias, root mean squared error (RMSE), precision, maximum absolute error, and intraclass correlation coefficients. System usability was descriptively analyzed. Results Overall, markerless motion capture had the highest validity (sagittal plane RMSE: 3.20-15.66°; frontal plane RMSE: 2.12-9.14°; transverse plane RMSE: 3.160-56.61°), followed by the IMU system (sagittal plane RMSE: 8.11-28.37°; frontal plane RMSE: 3.26-16.98°; transverse plane RMSE: 5.08-116.75°), and lastly the RGBD system (sagittal plane bias: 0.55-129.48°; frontal plane bias: 1.35-52.06°). Conclusions Markerless motion capture and IMUs have moderate validity for joint kinematics, while the RGBD system did not have adequate validity. Markerless systems have lower data processing time, require moderate technical expertise, but have high data storage size. IMUs are easier to use, can collect data in any location, but require participant set-up. Overall, individuals using motion capture should consider the specific movements, testing locations, and technical expertise available prior to selecting a system.

CP decomposition-based algorithms for completion problem of motion capture data

Motion capture (MoCap) technology is an essential tool for recording and analyzing movements of objects or humans. However, MoCap systems frequently encounter the challenge of missing data, stemming from mismatched markers, occlusion, or equipment limitations. Recovery of these missing data is imperative to maintain the reliability and integrity of MoCap recordings. This paper introduces a novel application of the tensor framework for MoCap data completion. We propose three completion algorithms based on the canonical polyadic (CP) decomposition of tensors. The first algorithm utilizes CP decomposition to capture the low-rank structure of the tensor. However, relying only on low-rank assumptions may be insufficient to deal with complex motion data. Thus, we propose two modified CP decompositions that incorporate additional information, SmoothCP and SparseCP decompositions. SmoothCP integrates piecewise smoothness prior, while SparseCP incorporates sparsity prior, each aiming to improve the accuracy and robustness of MoCap data recovery. To compare and evaluate the merit of the proposed algorithms over other tensor completion methods in terms of several evaluation metrics, we conduct numerical experiments with different MoCap sequences from the CMU motion capture dataset.

Relationship Between Lifestyle Factors and Accelerometery Based Motion Capture Analysis During An Active Stand In Healthy Older Adults

Abstract Background One in three individuals aged over 65 and one in two individuals over 80 experience falls in a year. As the population ages, preventing balance issues and falls among healthy older adults becomes increasingly critical. As the population ages, preventing balance issues and falls among healthy older adults becomes increasingly critical. We hypothesized that individuals may display distinct patterns of micro-movement during this test, undetectable to the naked eye but identifiable using motion capture technology. Methods Participants aged 50 years and older were recruited from a local healthy ageing group. Participants underwent an Active Stand (AS) test, consisting of lying down for 10 minutes followed by standing up as quickly as possible and remaining motionless for 3 minutes. During the test, participants wore an accelerometer Participants underwent an Active Stand (AS) test, consisting of lying down for 10 minutes followed by standing up as quickly as possible and remaining motionless for 3 minutes, during the test, participants wore an accelerometer. Computations were carried out on the signals for all remaining participants. Computations were carried out on the signals for all remaining participants. Results 31 participants were initially recruited, with 26 ultimately included after data quality checks. The mean age was 70.5 ± 5.4 years, with 85% being women A lower BMI was found to be significantly associated with a higher MI values a lower BMI was found to be significantly associated with a higher MI values across all three phases of the active stand test across all three phases of the test. Conclusion These findings underscore the potential role of modifiable lifestyle factors, like maintaining a healthy BMI and avoiding smoking, in mitigating early balance problems in older adults.

Virtual Simulation of Dance by Integrating VR Technology and Motion Capture Technology

Virtual Simulation of Dance by Integrating VR Technology and Motion Capture Technology

Localized electrocortical activity as a function of single-leg squat phases and its relationship to knee frontal plane stability.

This study investigated differences in electroencephalography (EEG) activity within motor-related brain areas during three phases of a single-leg squat (SLS)-i.e., descending, holding, and ascending phases. Specifically, utilizing advanced magnetic resonance imaging guided EEG source localization techniques and markerless motion capture technology, we explored the interplay between concurrently recorded lower-extremity biomechanics and brain activity. Among the phases of a nondominant leg SLS, differences in contralateral brain activity (right hemisphere) were found in the activity of the precentral gyrus, the postcentral gyrus, and the sensory motor area. Alternatively, during the dominant SLS leg, differences among the three SLS phases in contralateral brain activity were fewer. Hemispheric dependent brain activity also significantly correlated with participants' knee valgus angle range of motion (right hemisphere) and peak knee valgus angles (left hemisphere). In addition to the novel brain and biomechanical findings, this study sheds light on the technical feasibility of recording EEG during complex multi-joint movements and its potential applications in understanding sensorimotor behavior.

Exploring stroke survivors’ and physiotherapists’ perspectives of the potential for markerless motion capture technology in community rehabilitation

IntroductionMany stroke survivors do not receive optimal levels of personalised therapy to support their recovery. Use of technology for stroke rehabilitation has increased in recent years to help minimise gaps in service provision. Markerless motion capture technology is currently being used for musculoskeletal and occupational health screening and could offer a means to provide personalised guidance to stroke survivors struggling to access rehabilitation.AimsThis study considered context, stakeholders, and key uncertainties surrounding the use of markerless motion capture technology in community stroke rehabilitation from the perspectives of stroke survivors and physiotherapists with a view to adapting an existing intervention in a new context.MethodsThree focus groups were conducted with eight stroke survivors and five therapists. Data were analysed using reflexive thematic analysis.ResultsFive themes were identified: limited access to community care; personal motivation; pandemic changed rehabilitation practice; perceptions of technology; and role of markerless technology for providing feedback.ConclusionsParticipants identified problems associated with the access of community stroke rehabilitation, exacerbated by Covid-19 restrictions. Participants were positive about the potential for the use of markerless motion capture technology to support personalised, effective stroke rehabilitation in the future, providing it is developed to meet stroke survivor specific needs.

Fine-scale tracking reveals visual field use for predator detection and escape in collective foraging of pigeon flocks.

During collective vigilance, it is commonly assumed that individual animals compromise their feeding time to be vigilant against predators, benefiting the entire group. One notable issue with this assumption concerns the unclear nature of predator 'detection', particularly in terms of vision. It remains uncertain how a vigilant individual utilizes its high-acuity vision (such as the fovea) to detect a predator cue and subsequently guide individual and collective escape responses. Using fine-scale motion-capture technologies, we tracked the head and body orientations of pigeons (hence reconstructed their visual fields and foveal projections) foraging in a flock during simulated predator attacks. Pigeons used their fovea to inspect predator cues. Earlier foveation on a predator cue was linked to preceding behaviors related to vigilance and feeding, such as head-up or down positions, head-scanning, and food-pecking. Moreover, earlier foveation predicted earlier evasion flights at both the individual and collective levels. However, we also found that relatively long delay between their foveation and escape responses in individuals obscured the relationship between these two responses. While our results largely support the existing assumptions about vigilance, they also underscore the importance of considering vision and addressing the disparity between detection and escape responses in future research.

Evaluating Joint Angle Data for Clinical Assessment Using Multidimensional Inverse Kinematics with Average Segment Morphometry.

Movement analysis is a critical tool in understanding and addressing various disabilities associated with movement deficits. By analyzing movement patterns, healthcare professionals can identify the root causes of these alterations, which is essential for preventing, diagnosing, and rehabilitating a broad spectrum of medical conditions, disabilities, and injuries. With the advent of affordable motion capture technologies, quantitative data on patient movement is more accessible to clinicians, enhancing the quality of care. Nonetheless, it is crucial that these technologies undergo rigorous validation to ensure their accuracy in collecting and monitoring patient movements, particularly for remote healthcare services where direct patient observation is not possible. In this study, motion capture technology was used to track upper extremity movements during a reaching task presented in virtual reality. Kinematic data was then calculated for each participant using a scaled dynamic inertial model. The goal was to evaluate the accuracy of joint angle calculations using inverse kinematics from motion capture relative to the typical movement redundancy. Shoulder, elbow, radioulnar, and wrist joint angles were calculated with models scaled using either direct measurements of each individual's arm segment lengths or those lengths were calculated from individual height using published average proportions. The errors in joint angle trajectories calculated using the two methods of model scaling were compared to the inter-trial variability of those trajectories. The variance of this error was primarily within the normal range of variability between repetitions of the same movements. This suggests that arm joint angles can be inferred with good enough accuracy from motion capture data and individual height to be useful for the clinical assessment of motor deficits.

Analysis of health related event detection in big data for physical education training movement detection.

Physical education and training are essential ways to improve the physical quality of the nation, and China has incorporated "building a healthy China" and "fitness for all" into its national development strategy, integrating a strong sports nation into the Chinese dream. The study of digital recording and automated training in sports is of profound value. Motion capture technology can digitally record the training process in a digital physical education training system. At the same time, accurate modeling and calculation can analyze the training effects and give appropriate guidance and feedback. This study develops a new and improved hierarchical K-means algorithm by combining the known classification algorithm K-means with a hierarchical algorithm. The performance of the old and new algorithms are compared and then applied to physical education training data to produce clustering results and analysis to reduce the model, which is used to reduce the number of parameters in the model and improve the recognition speed. The experimental results demonstrate that the relevant models proposed in this study achieve an average accuracy of 91.27% and 92.26%, respectively, which is better than a single network model and can effectively use big data for health event detection. The empirical results show that the improved model algorithm outperforms the single network model and can detect health events using big data.

Digital Twin-Based Alternate Ego Modeling and Simulation: Eva Herzigová as a 3D MetaHuman Avatar

Abstract Due to Dimension Studios and Unsigned Group, by likeness and movement digitization, the human supermodel Eva Herzigová’s highly realistic metahuman and 3D digital twin lead to perpetual career extension (e.g., on virtual runway shows by catwalk replication or in advertising campaigns by particular pose adoption), including in virtual worlds such as metaverse spaces, by use of extended reality technologies, in terms of virtual clothing, digital hair, skin texture, facial and body expressions, and makeup styling as avatar appearance and persona. In this paper, we show how, by likeness capturing (e.g., signature walk by motion capture shoot for face and body movement tracking) and virtual clothing, Eva Herzigová’s hyperreal 3D avatar (lifelike 3D digital human clone or realistic virtual human) is configured with subsequent restyled hair and make-up, 3D clothing modeling, and extended reality fashion shows on virtual catwalks or campaign shoots. We clarify that photographic reference materials and a sensor-based motion capture suit assisted Metahuman Creator in capturing facial and motion data for Eva Herzigová’s digital avatar, configuring realistic depiction and virtual activations of beauty standards, in addition to digital likeness, signature style, talent representation, and narrations, typifying digital human creation and presence in virtual worlds (e.g., for metaverse-based shoots). We conclude that Eva Herzigová’s hyperrealistic 3D digital human twin and MetaHuman avatar operate as a styled virtual alter ego and versatile personal brand extension by motion capture technologies for immersive virtual fashion shows, live video streams and broadcast, and digital wearable modeling.