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Traditional gait analysis systems are typically complex to operate, lack portability, and involve high equipment costs. This study aims to establish a musculoskeletal dynamics calculation process driven by Azure Kinect. Building upon the full-body model of the Anybody musculoskeletal simulation software and incorporating a foot-ground contact model, the study utilized Azure Kinect-driven skeletal data from depth videos of 10 participants. The in-depth videos were prepossessed to extract keypoint of the participants, which were then adopted as inputs for the musculoskeletal model to compute lower limb joint angles, joint contact forces, and ground reaction forces. To validate the Azure Kinect computational model, the calculated results were compared with kinematic and kinetic data obtained using the traditional Vicon system. The forces in the lower limb joints and the ground reaction forces were normalized by dividing them by the body weight. The lower limb joint angle curves showed a strong correlation with Vicon results (mean ρ values: 0.78 ~ 0.92) but with root mean square errors as high as 5.66°. For lower limb joint force prediction, the model exhibited root mean square errors ranging from 0.44 to 0.68, while ground reaction force root mean square errors ranged from 0.01 to 0.09. The established musculoskeletal dynamics model based on Azure Kinect shows good prediction capabilities for lower limb joint forces and vertical ground reaction forces, but some errors remain in predicting lower limb joint angles.

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BACKGROUND: Spinopelvic stiffness (primarily in the sagittal plane) has been identified as a factor associated with inferior patient-reported outcomes (PROs) and increased dislocation risk after THA. Incorporating preoperative spinopelvic characteristics into surgical planning has been suggested to determine a patient-specific cup orientation that minimizes dislocation risk. Sagittal plane radiographic analysis of static postures indicates that patients exhibit a degree of normalization in their spinopelvic characteristics after THA. It is not yet known whether normalization is also evident during dynamic movement patterns, nor whether it occurs in the coronal and axial planes as well. QUESTIONS/PURPOSES: (1) Does motion capture analysis of sagittal spinopelvic motion provide evidence of normalization after THA? (2) Do changes in coronal and axial plane motion accompany those in the sagittal plane? METHODS: Between April 2019 and February 2020, 25 patients agreed to undergo motion capture movement analysis before THA for the treatment of hip osteoarthritis (OA). Of those, 20 underwent the same assessment between 8 and 31 months after THA. Five patients were excluded because of revision surgery (n = 1), contralateral hip OA (n = 1), and technical issues with a force plate during post-THA assessment (n = 3), leaving a cohort total of 15 (median age [IQR] 65 years [10]; seven male and eight female patients). A convenience sample of nine asymptomatic volunteers, who were free of hip and spinal pathology, was also assessed (median age 51 years [34]; four male and five female patients). Although the patients in the control group were younger than those in the patient group, this set a high bar for our threshold of spinopelvic normalization, reducing the possibility of false positive results. Three-dimensional motion capture was performed to measure spinal, pelvic, and hip motion while participants completed three tasks: seated bend and reach, seated trunk rotation, and gait on a level surface. ROM during each task was assessed and compared between pre- and post-THA conditions and between patients and controls. Statistical parametric mapping (SPM) was used to assess the timing of differences in motion during gait, and spatiotemporal gait parameters were also measured. RESULTS: After THA, patients demonstrated improvements in sagittal spinal (median [IQR] 32° [18°] versus 41° [14°]; difference of medians 9°; p = 0.004), pelvis (25° [21°] versus 30° [8°]; difference of medians 5°; p = 0.02), and hip ROM (21° [18°] versus 27° [10°]; difference of medians 6°; p = 0.02) during seated bend and reach as well in sagittal hip ROM during gait (30° [11°] versus 44° [7°]; difference of medians 14°; p < 0.001) compared with their pre-THA results, and they showed a high degree of normalization overall. These sagittal plane changes were accompanied by post-THA increases in coronal hip ROM (12° [9°] versus 18° [8°]; difference of medians 6°; p = 0.01) during seated trunk rotation, by both coronal (6° [4°] versus 9° [3°]; difference of medians 3°; p = 0.01) and axial (10° [8°] versus 16° [7°]; difference of medians 6°; p = 0.003) spinal ROM, as well as coronal (8° [3°] versus 13° [4°]; difference of medians 5°; p < 0.001) and axial hip ROM (21° [11°] versus 34° [24°]; difference of medians 13°; p = 0.01) during gait compared with before THA. The SPM analysis showed these improvements occurred during the late swing and early stance phases of gait. CONCLUSION: When restricted preoperatively, spinopelvic characteristics during daily tasks show normalization after THA, concurring with previous radiographic findings in the sagittal plane. Thus, spinopelvic characteristics change dynamically, and incorporating them into surgical planning would require predictive models on post-THA improvements to be of use. LEVEL OF EVIDENCE: Level II, prognostic study.

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BACKGROUND: The gait variable minimum toe clearance (MTC) has been investigated concerning trip-related fall research in older adults. However, comparing studies is difficult due to the different methods used to measure MTC and shoe conditions, which may affect agreement. Measurement methods can include using a single virtual point (SVP), multiple virtual points (MVPS), or metatarsal head markers (marker-based). The shoe types used in MTC studies include standard shoes (SS), personal shoes (PS), and barefoot (BF) conditions. RESEARCH QUESTION: What is the agreement, inter and intra-rater reliability, and repeatability for the 3 commonly used methods of measuring MTC (SVP, MVPS, marker-based) under the 3 shoe conditions for optical motion capture systems (SS, PS, BF)? METHODS: Twelve healthy young adults (mean [SD] 23.8 [1.9] years,7 males) participated in this observational study. In a randomized order, participants completed 25 walking trials at self-selected normal and slow speeds in SS, PS, and BF conditions while infrared cameras recorded the maker trajectories. Each participant performed a familiarization trial for at least 1 minute before collecting data on each shoe condition. Statistical analyses included Bland-Altman 95 % limits of agreement (LOA) analyses, interclass correlation coefficient (ICC) analyses for inter- and intra-rater reliability, and the repeatability coefficient (RC). RESULTS: The SVP and MVPS had a tighter 95 % LOA than the marker-based method, particularly under SS and BF conditions. The inter-rater reliability was good to excellent under these shoe conditions. Intra-reliability for all methods under all shoe conditions was excellent (ICC >.90). The RC was very similar for each method, with none exceeding 1.02 cm. SIGNIFICANCE: The study provides estimates of the agreement between MTC methods and suggests that only SVP or MVPS produced similar results in SS/BF conditions. Additionally, a "true" change in MTC requires a difference greater than 1.02 cm.

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BACKGROUND: Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia. OBJECTIVE: The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease. METHODS: A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results. The inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full-text screening, out of which 26 eligible studies that met the selection criteria were included in the review. . RESULTS: Primarily, the selected studies focused on gait analysis (n=24), while other functional tasks, such as sit to stand (n=5) and stepping in place (n=1), were also explored. However, activities of daily living were not evaluated in any of the included studies. MMC models varied across the studies, encompassing depth cameras (n=18) versus standard video cameras (n=5) or mobile phone cameras (n=2) with postprocessing using deep learning models. However, only 6 studies conducted rigorous comparisons with established gold-standard motion capture models. CONCLUSIONS: Despite its potential as an effective tool for analyzing movement and posture in individuals with dementia, mild cognitive impairment, and Parkinson disease, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real world.

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OpenCap, a smartphone- and web-based markerless system, has shown acceptable accuracy compared to marker-based systems, but lacks information on repeatability. This study fills this gap by evaluating the intersession repeatability of OpenCap and investigating the effects of clothing on gait kinematics. Twenty healthy volunteers participated in a test-retest study, performing walking and sit-to-stand tasks with minimal clothing and regular street wear. Segment lengths and lower-limb kinematics were compared between both sessions and for both clothing conditions using the root-mean-square-deviation (RMSD) for entire waveforms and the standard error of measurement (SEM) and minimal detectable change (MDC) for discrete kinematic parameters. In general, the RMSD test-retest values were 2.8 degrees (SD: 1.0) for walking and 3.3 degrees (SD: 1.2) for sit-to-stand. The highest intersession variability was observed in the trunk, pelvis, and hip kinematics of the sagittal plane. SEM and MDC values were on average 2.2 and 6.0 degrees, respectively, for walking, and 2.4 and 6.5 degrees for sit-to-stand. Clothing had minimal effects on kinematics by adding on average less than one degree to the RMSD values for most variables. The segment lengths showed moderate to excellent agreement between both sessions and poor to moderate agreement between clothing conditions. The study highlights the reliability of OpenCap for markerless motion capture, emphasizing its potential for large-scale field studies. However, some variables showed high MDC values above 5 degrees and thus warrant further enhancement of the technology. Although clothing had minimal effects, it is still recommended to maintain consistent clothing to minimize overall variability.

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Sit-to-walk (STW) is a crucial daily task that impacts mobility, independence, and thus quality of life. Existing repositories have limited STW data with small sample sizes (n = 10). Hence, this study presents a STW dataset obtained via the time-up-and-go test, for 65 healthy adults across three age groups - young (19-35 years), middle (36-55 years) and older (above 56 years). The dataset contains lower body motion capture, ground reaction force, surface electromyography, inertial measurement unit data, and responses for the knee injury and osteoarthritis outcome score survey. For validation, the within subjects intraclass correlation coefficients for the maximum and minimum lower body joint angles were calculated with values greater than 0.74, indicating good test-retest reliability. The joint angle trajectories and maximum voluntary contractions are comparable with existing literature, matching in overall trends and range. Accordingly, this dataset allows STW biomechanics, executions, and characteristics to be studied across age groups. Biomechanical trajectories of healthy adults serve as a benchmark in assessing neuromusculoskeletal impairments and when designing assistive technology for treatment or rehabilitation.

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Biomechanical analysis of the human spine is crucial to understanding injury patterns. Motion capture technology has gained attention due to its non-invasive nature. Nevertheless, traditional motion capture studies consider the spine a single rigid segment, although its alignment changes during movement. Moreover, guidelines that indicate where markers should be placed for a specific exercise do not exist. This study aims to review the methods used to assess spine biomechanics using motion capture systems to determine the marker sets used, the protocols used, the resulting parameters, the analysed activities, and the characteristics of the studied populations. PRISMA guidelines were used to perform a Scoping Review using SCOPUS and Web of Science databases. Fifty-six journal and conference articles from 1997 to 2023 were considered for the analysis. This review showed that Plug-in-Gait is the most used marker set. The lumbar spine is the segment that generates the most interest because of its high mobility and function as a weight supporter. Furthermore, angular position and velocity are the most common outcomes when studying the spine. Walking, standing, and range of movement were the most studied activities compared to sports and work-related activities. Male and female participants were recruited similarly across all included articles. This review presents the motion capture techniques and measurement outcomes of biomechanical studies of the human spine, to help standardize the field. This work also discusses trends in marker sets, study outcomes, studied segments and segmentation approaches.

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Digital quantification of gait can be used to measure aging- and disease-related decline in mobility. Gait performance also predicts prognosis, disease progression, and response to therapies. Most gait analysis systems require large amounts of space, resources, and expertise to implement and are not widely accessible. Thus, there is a need for a portable system that accurately characterizes gait. Here, depth video from two portable cameras accurately reconstructed gait metrics comparable to those reported by a pressure-sensitive walkway. 392 research participants walked across a four-meter pressure-sensitive walkway while depth video was recorded. Gait speed, cadence, and step and stride durations and lengths strongly correlated (r > 0.9) between modalities, with root-mean-squared-errors (RMSE) of 0.04 m/s, 2.3 steps/min, 0.03 s, and 0.05-0.08 m for speed, cadence, step/stride duration, and step/stride length, respectively. Step, stance, and double support durations (gait cycle percentage) significantly correlated (r > 0.6) between modalities, with 5% RMSE for step and stance and 10% RMSE for double support. In an exploratory analysis, gait speed from both modalities significantly related to healthy, mild, moderate, or severe categorizations of Charleson Comorbidity Indices (ANOVA, Tukey's HSD, p < 0.0125). These findings demonstrate the viability of using depth video to expand access to quantitative gait assessments.

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BACKGROUND: The prone hip extension test is used as a clinical tool to diagnose specific motor control impairments that have been identified in individuals with chronic low back pain. However, conventional protocols for performing the test are subjective and lack evidence for their effectiveness. The objective of the current study was to quantify lumbopelvic motion and muscle activation during this test and identify which motor control patterns best distinguish individuals with low back pain from asymptomatic controls. METHODS: 18 individuals with sub-acute or chronic low back pain and 32 asymptomatic controls performed the prone hip extension test while a 3D motion capture system measured lumbar and pelvic movement patterns and an electromyography system measured the muscle activation patterns of the paraspinal, gluteus maximus, and hamstring muscles. A three-stage statistical analysis was performed, the final stage being a stepwise logistic regression analysis aimed at identifying the movement and muscle activation pattern variables that best distinguished the two groups. FINDINGS: The final regression model included three lumbar kinematic variables and several electromyographic amplitude variables for the gluteus maximus and hamstring muscles during right-sided prone hip extension. The final model correctly classified 86.7 % of the control group and 83.3 % of the low back pain group. INTERPRETATION: The subject of asymmetrical gluteus maximus and hamstring muscle activation appears to be a potentially interesting area for future research on the utility of the prone hip extension test as a clinical tool in diagnosing motor control impairments associated with low back pain.

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High-quality cardiopulmonary resuscitation (CPR) is important for successful revival. This test aimed to accurately and effectively assess parameters in CPR operations, particularly focusing on the non-quantified arm posture, to assist instructors in guiding students to enhance the quality of their performance. We used motion capture system (Mars series, Nokov, China) to collect compression data from four trained students about five rounds of CPR, recording dynamic data of each marker in three-dimensional space following time. Using the processed data, we calculated compression depth, frequency, and arm angles. Their performance still needed improvement, and there were variations in data within individuals. This suggests that when instructors conduct assessments, they should focus on not only the overall performance but also each compression. This experiment provides a new perspective for quantifying compression parameters, and future efforts should continue to optimize and incorporate new parameters for assessment.

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Falls are a major issue for those over the age of 65 years worldwide. Objective assessment of fall risk is rare in clinical practice. The most common methods of assessment are time-consuming observational tests (clinical tests). Computer-aided diagnosis could be a great help. A popular clinical test for fall risk is the five times sit-to-stand. The time taken to complete the test is the most commonly used metric to identify the most at-risk patients. However, tracking the movement of skeletal joints can provide much richer insights. We use markerless motion capture, allied with a representational model, to identify those at risk of falls. Our method uses an LSTM autoencoder to derive a distance measure. Using this measure, we introduce a new scoring system, allowing individuals with differing falls risks to be placed on a continuous scale. Evaluating our method on the KINECAL dataset, we achieved an accuracy of 0.84 in identifying those at elevated falls risk. In addition to identifying potential fallers, our method could find applications in rehabilitation. This aligns with the goals of the KINECAL Dataset. KINECAL contains the recordings of 90 individuals undertaking 11 movements used in clinical assessments. KINECAL is labelled to disambiguate age-related decline and falls risk.

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Low-cost markerless motion capture systems offer the potential for 3D measurement of joint angles during human movement. This study aimed to validate a smartphone-based markerless motion capture system's (OpenCap) derived lower extremity kinematics during common return-to-sport tasks, comparing it to an established optoelectronic motion capture system. Athletes with prior anterior cruciate ligament reconstruction (12-18 months post-surgery) performed three movements: a jump-landing-rebound, single-leg hop, and lateral-vertical hop. Kinematics were recorded concurrently with two smartphones running OpenCap's software and with a 10-camera, marker-based motion capture system. Validity of lower extremity joint kinematics was assessed across 437 recorded trials using measures of agreement (coefficient of multiple correlation: CMC) and error (mean absolute error: MAE, root mean squared error: RMSE) across the time series of movement. Agreement was best in the sagittal plane for the knee and hip in all movements (CMC > 0.94), followed by the ankle (CMC = 0.84-0.93). Lower agreement was observed for frontal (CMC = 0.47-0.78) and transverse (CMC = 0.51-0.6) plane motion. OpenCap presented a grand mean error of 3.85° (MAE) and 4.34° (RMSE) across all joint angles and movements. These results were comparable to other available markerless systems. Most notably, OpenCap's user-friendly interface, free software, and small physical footprint have the potential to extend motion analysis applications beyond conventional biomechanics labs, thus enhancing the accessibility for a diverse range of users.

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Lip language recognition urgently needs wearable and easy-to-use interfaces for interference-free and high-fidelity lip-reading acquisition and to develop accompanying data-efficient decoder-modeling methods. Existing solutions suffer from unreliable lip reading, are data hungry, and exhibit poor generalization. Here, we propose a wearable lip language decoding technology that enables interference-free and high-fidelity acquisition of lip movements and data-efficient recognition of fluent lip language based on wearable motion capture and continuous lip speech movement reconstruction. The method allows us to artificially generate any wanted continuous speech datasets from a very limited corpus of word samples from users. By using these artificial datasets to train the decoder, we achieve an average accuracy of 92.0% across individuals (n = 7) for actual continuous and fluent lip speech recognition for 93 English sentences, even observing no training burn on users because all training datasets are artificially generated. Our method greatly minimizes users' training/learning load and presents a data-efficient and easy-to-use paradigm for lip language recognition.

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The serve is the most important stroke in tennis. It is a complex gesture consisting of numerous rotations with a wide amplitude, which are important to manage for performance. The aim of this study was to investigate whether correlations exist between joint kinematic parameters and racket velocity. A quantitative kinematics analysis of four ranked players (two boys and two girls) was carried out using an optoelectronic system composed of 10 cameras (150 Hz). Five flat serves per player were analyzed. Eighty-two markers were located across the 15 body segments and on the racket. A descriptive statistical analysis including a correlation analysis was carried out between joint angles and racket kinematic parameters (vertical position, velocity, and acceleration) during the cocking and acceleration phases. Ten very high (0.7 < r < 0.9) and three almost perfect (r > 0.9) correlations were found. Shoulder and hip axial rotations, knee flexion, and trunk extension were correlated linearly with racket vertical position and velocity during the cocking phase. For the acceleration phase, elbow flexion, trunk flexion/extension, and trunk axial rotation were linked to racket kinematics. Some of these parameters showed differences between slow and fast serves. These parameters, which are involved in transmitting ball velocity, are important to consider for tennis players and coaches in training programs, education, and performance enhancement.

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Gait initiation (GI) is a functional task classically used in the literature to evaluate the capacity of individuals to maintain postural stability. Postural stability during GI can be evaluated through the "margin of stability" (MoS), a variable that is often computed from force plate recordings. The markerless motion capture system (MLS) is a recent innovative technology based on deep learning that has the potential to compute the MoS. This study tested the agreement between a force plate measurement system (FPS, gold standard) and an MLS to compute the MoS during GI. Healthy adults (young [YH] and elderly [EH]) and Parkinson's disease patients (PD) performed GI series at spontaneous (SVC) and maximum velocity (MVC) on an FPS while being filmed by a MLS. Descriptive statistics revealed a significant effect of the group (YH vs. EH vs. PD) and velocity condition (SVC vs. MVC) on the MoS but failed to reveal any significant effect of the system (MLS vs. PFS) or interaction between factors. Bland-Altman plot analysis further showed that mean MoS biases were zero in all groups and velocity conditions, while the Bayes factor 01 indicated "moderate evidence" that both systems provided equivalent MoS. Trial-by-trial analysis of Bland-Altman plots, however, revealed that differences of >20% between the two systems did occur. Globally taken, these findings suggest that the two systems are similarly effective in detecting an effect of the group and velocity on the MoS. These findings may have important implications in both clinical and laboratory settings due to the ease of use of the MLS compared to the FPS.

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Work-related diseases and disorders remain a significant global health concern, necessitating multifaceted measures for mitigation. One potential measure is work technique training utilizing augmented feedback through wearable motion capture systems. However, there exists a research gap regarding its current effectiveness in both real work environments and controlled settings, as well as its ability to reduce postural exposure and retention effects over short, medium, and long durations. A rapid review was conducted, utilizing two databases and three previous literature reviews to identify relevant studies published within the last twenty years, including recent literature up to the end of 2023. Sixteen studies met the inclusion criteria, of which 14 were of high or moderate quality. These studies were summarized descriptively, and the strength of evidence was assessed. Among the included studies, six were rated as high quality, while eight were considered moderate quality. Notably, the reporting of participation rates, blinding of assessors, and a-priori power calculations were infrequently performed. Four studies were conducted in real work environments, while ten were conducted in controlled settings. Vibration feedback was the most common feedback type utilized (n = 9), followed by auditory (n = 7) and visual feedback (n = 1). All studies employed corrective feedback initiated by the system. In controlled environments, evidence regarding the effectiveness of augmented feedback from wearable motion capture systems to reduce postural exposure ranged from strong evidence to no evidence, depending on the time elapsed after feedback administration. Conversely, for studies conducted in real work environments, the evidence ranged from very limited evidence to no evidence. Future reach needs are identified and discussed.

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Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment.

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Motion Capture (MoCap) has become an integral tool in fields such as sports, medicine, and the entertainment industry. The cost of deploying high-end equipment and the lack of expertise and knowledge limit the usage of MoCap from its full potential, especially at beginner and intermediate levels of sports coaching. The challenges faced while developing affordable MoCap systems for such levels have been discussed in order to initiate an easily accessible system with minimal resources.

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(1) Background: Marker-based 3D motion capture systems (MBS) are considered the gold standard in gait analysis. However, they have limitations for which markerless camera-based 3D motion capture systems (MCBS) could provide a solution. The aim of this systematic review and meta-analysis is to compare the accuracy, validity, and reliability of MCBS and MBS. (2) Methods: A total of 2047 papers were systematically searched according to PRISMA guidelines on 7 February 2024, in two different databases: Pubmed (1339) and WoS (708). The COSMIN-tool and EBRO guidelines were used to assess risk of bias and level of evidence. (3) Results: After full text screening, 22 papers were included. Spatiotemporal parameters showed overall good to excellent accuracy, validity, and reliability. For kinematic variables, hip and knee showed moderate to excellent agreement between the systems, while for the ankle joint, poor concurrent validity and reliability were measured. The accuracy and concurrent validity of walking speed were considered excellent in all cases, with only a small bias. The meta-analysis of the inter-rater reliability and concurrent validity of walking speed, step time, and step length resulted in a good-to-excellent intraclass correlation coefficient (ICC) (0.81; 0.98). (4) Discussion and conclusions: MCBS are comparable in terms of accuracy, concurrent validity, and reliability to MBS in spatiotemporal parameters. Additionally, kinematic parameters for hip and knee in the sagittal plane are considered most valid and reliable but lack valid and accurate measurement outcomes in transverse and frontal planes. Customization and standardization of methodological procedures are necessary for future research to adequately compare protocols in clinical settings, with more attention to patient populations.

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