Motion Capture Technology Research Articles

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

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

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.

Clinicians’ Attitudes, Perspectives, and Clinical Practices on Gait Retraining After Anterior Cruciate Ligament Reconstruction

Introduction: It has been shown that 45%–85% of patients with anterior cruciate ligament reconstruction (ACLR) will have early-onset arthritis within 10–12 years following surgery. Over the past two decades, the amount of literature regarding ACLR, gait maladaptations after ACLR and their potential link to early-onset arthritis, and rehabilitation techniques has grown exponentially; however, long-term patient outcomes remain modest. Methods: To evaluate current clinicians’ attitudes, perspectives, and clinical practice approach for rehabilitation of patients following ACLR, a survey questionnaire was designed using the Delphi technique. Results: Of the 263 respondents, 84.4% (n = 226) reported that they believed gait training to be “Very” or “Extremely Important.” However, only 35.7% (n = 94) reported objectively measuring gait during ACLR rehabilitation. Of the total respondents, only 6.8% (n = 18) assessed gait during rehabilitation using two-dimensional or three-dimensional motion capture technologies. Discussion: Our results suggest that while gait evaluation was perceived as important, most respondents did not objectively measure gait metrics as a clinical outcome during ACLR rehabilitation. These findings provide a prospective rehabilitation target to potentially mitigate a known risk factor of early-onset arthritis (gait maladaptations) in individuals following ACLR.

Biological reliability of a movement analysis assessment using a markerless motion capture system.

Advances in motion capture technology include markerless systems to facilitate valid data collection. Recently, the technological reliability of this technology has been reported for human movement assessments. To further understand sources of potential error, biological reliability must also be determined. The aim of this study was to determine the day-to-day reliability for a three-dimensional markerless motion capture (MMC) system to quantify 4 movement analysis composite scores, and 81 kinematic variables. Twenty-two healthy men (n = 11; ; age = 23.0 ± 2.6 years, height = 180.4.8 cm, weight = 80.4 ± 7.3 kg) and women (n = 11; age = 20.8 ± 1.1 years, height = 172.2 ± 7.4 cm, weight = 68.0 ± 7.3 kg) participated in this study. All subjects performed 4 standardized test batteries consisting of 14 different movements on four separate days. A three-dimensional MMC system (DARI Motion, Lenexa, KS) using 8 cameras surrounding the testing area was used to quantify movement characteristics. 1 × 4 RMANOVAs were used to determine significant differences across days for the composite movement analysis scores, and RM-MANOVAs were used to determine test day differences for the kinematic data (p < 0.05). Intraclass correlation coefficients (ICCs) were reported for all variables to determine test reliability. To determine biological variability, mean absolute differences from previously reported technological variability data were subtracted from the total variability data from the present study. No differences were observed for any composite score (i.e., athleticism, explosiveness, quality, readiness; or any of the 81 kinematic variables. Furthermore, 84 of 85 measured variables exhibited good to excellent ICCs (0.61-0.99). When compared to previously reported technological variability data, 62.3% of item variability was due to biological variability, with 66 of 85 variables exhibiting biological variability as the primary source of error (i.e., >50% total variability). Combined, these findings effectively add to the body of literature suggesting sufficient reliability for MMC solutions in capturing kinematic features of human movement.

Fuzzy-pid control design of biped robot based on motion capture technology

Abstract To improve the precision of the biped robot’s walking control system, a fuzzy PID controller with a variable theory domain was added to the design of the biped robot’s lower limb control system to optimize the tracking process of joint angular displacement and improve the motion effect. Firstly, the motion capture experiment is carried out to obtain the angular displacement data of the joint. The theoretical angle was calculated according to the biped robot’s lower limb diagram model and kinematics equation, and the expected trajectory was obtained by simple gait planning and using MATLAB to verify the validity of the angle parameters. Then the fuzzy PID controller of the lower limb joint was established, and the variable theory domain fuzzy controller was established by introducing the expansion factor. Finally, the Simulink test bench is built to simulate the two controllers, and the expected data and simulation data are compared. The results show that the error range of the variable theory domain fuzzy controller is only ±2°, and the variable theory domain adaptive fuzzy controller can accurately track the desired trajectory, reduce the joint angular displacement error of the biped robot, ensure the precision and stability of the biped robot motion, and basically meet the requirements of practical production.

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

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

Pengembangan Game Terapi Bagi Anak Autisme Berbasis Motion Capture dengan Metode Optimasi Kalman Filter

Children with autism spectrum disorder often face challenges in developing motor skills and social interaction abilities. This research aimed to develop an interactive motion capture-based therapy game that could assist autistic children in practicing their motor skills through an enjoyable play experience. The Kalman Filter optimization method was employed to enhance the accuracy and quality of motion detection in the motion capture process. The development of this therapy game involved consultations with therapists, psychologists, and relevant experts in designing game activities tailored to the needs of autistic children. Motion capture technology was utilized to capture the children's body movements in real-time, while the Kalman Filter algorithm was implemented to filter and optimize the acquired motion data. The game interface was designed to be simple, featuring attractive visual objects, bright colors, and familiar sounds. The testing results demonstrated that the application of the Kalman Filter method could increase motion detection accuracy up to 90%, ensuring a more responsive and immersive play experience for the children. This therapy game proved effective in improving motor skills, body coordination, as well as motivation and engagement of autistic children in the therapy process. This research contributes to the development of an enjoyable and effective interactive therapy solution to assist autistic children in developing essential skills for daily life

Animated VR and 360-degree VR to assess and train team sports decision-making: a scoping review.

In team sports, athletes' ability to make quick decisions plays a crucial role. Decision-making proficiency relies on the intricate balance of athletes' perceptual and cognitive abilities, enabling them to assess the competitive environment swiftly and select the most appropriate actions from various options. Virtual reality (VR) technology is emerging as a valuable tool for evaluating and refining athletes' decision-making skills. This study systematically examined the integration of VR technology into decision-making processes in team sports, aiming to identify more effective methods for presenting and interacting with virtual decision-making systems, thus enhancing the evaluation and refinement of athletes' decision making abilities. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, a thorough search of respected research databases, including Web of Science, PubMed, SPORTDiscus, ScienceDirect, PsycINFO, and IEEE, was conducted using carefully selected keywords. Twenty research papers meeting predefined inclusion criteria were included after careful evaluation. These papers were systematically analyzed to delineate the attributes of virtual decision-making task environments, the interactive dynamics inherent in motor decision-making tasks, and the significant findings. This review indicate that (1) the effectiveness of VR technology in assessing and improving athletes' decision-making skills in team sports; (2) the construction of virtual environments using the Head-Mounted Display (HMD) system, characterized by enhanced ease and efficiency; (3) the potential for future investigations to explore computer simulations to create more expansive virtual motion scenarios, thus efficiently generating substantial task scenario material, diverging from the constraints posed by 360-degree panoramic videos; and (4) the integration of motion capture technology for identifying and monitoring athletes' decision-making behaviors, which not only enhances ecological validity but also augments the transfer validity of virtual sports decision-making systems. Future research endeavors could explore integrating eye-tracking technology with virtual reality to gain insights into the intrinsic cognitive-action associations exhibited by athletes.