Concurrent assessment of gait kinematics using marker-based and markerless motion capture

Biomechanical Gait Analysis Using a Smartphone-Based Motion Capture System (OpenCap) in Patients with Neurological Disorders

This study evaluates the utility of OpenCap (v0.3), a smartphone-based motion capture system, for performing gait analysis in patients with neurological disorders. We compared kinematic and kinetic gait parameters between 10 healthy controls and 10 patients with neurological conditions, including stroke, Parkinson's disease, and cerebral palsy. OpenCap captured 3D movement dynamics using two smartphones, with data processed through musculoskeletal modeling. The key findings indicate that the patient group exhibited significantly slower gait speeds (0.67 m/s vs. 1.10 m/s, p = 0.002), shorter stride lengths (0.81 m vs. 1.29 m, p = 0.001), and greater step length asymmetry (107.43% vs. 91.23%, p = 0.023) compared to the controls. Joint kinematic analysis revealed increased variability in pelvic tilt, hip flexion, knee extension, and ankle dorsiflexion throughout the gait cycle in patients, indicating impaired motor control and compensatory strategies. These results indicate that OpenCap can effectively identify significant gait differences, which may serve as valuable biomarkers for neurological disorders, thereby enhancing its utility in clinical settings where traditional motion capture systems are impractical. OpenCap has the potential to improve access to biomechanical assessments, thereby enabling better monitoring of gait abnormalities and informing therapeutic interventions for individuals with neurological disorders.

Computer vision for kinematic metrics of the drinking task in a pilot study of neurotypical participants

Assessment of the upper limb is critical to guiding the rehabilitation cycle. Drawbacks of observation-based assessment include subjectivity and coarse resolution of ordinal scales. Kinematic assessment gives rise to objective quantitative metrics, but uptake is encumbered by costly and impractical setups. Our objective was to investigate feasibility and accuracy of computer vision (CV) for acquiring kinematic metrics of the drinking task, which are recommended in stroke rehabilitation research. We implemented CV for upper limb kinematic assessment using modest cameras and an open-source machine learning solution. To explore feasibility, 10 neurotypical participants were recruited for repeated kinematic measures during the drinking task. To investigate accuracy, a simultaneous marker-based motion capture system was used, and error was quantified for the following kinematic metrics: Number of Movement Units (NMU), Trunk Displacement (TD), and Movement Time (MT). Across all participant trials, kinematic metrics of the drinking task were successfully acquired using CV. Compared to marker-based motion capture, no significant difference was observed for group mean values of kinematic metrics. Mean error for NMU, TD, and MT were - 0.12 units, 3.4 mm, and 0.15 s, respectively. Bland-Altman analysis revealed no bias. Kinematic metrics of the drinking task can be measured using CV, and preliminary findings support accuracy. Further study in neurodivergent populations is needed to determine validity of CV for kinematic assessment of the post-stroke upper limb.

Markerless three-dimensional gait analysis in healthy older adults: test-retest reliability and measurement error

In older adults, gait analysis may detect changes that signal early disease states, yet challenges in biomechanical screening limit widespread use in clinical or community settings. Recently, a markerless method from multi-camera video data has become accessible, making screenings less challenging. This study evaluated the test-retest reliability and measurement error of markerless gait kinematics and kinetics in healthy older adults. Twenty-nine healthy older adults performed gait analysis on two occasions, at preferred walking speed, using their everyday clothes. Lower limb angles and moments were averaged from 8 gait cycles. Integrated pointwise indices [Intraclass Correlation Coefficient (ICCA,K) and Standard Error of Measurement (SEM)] were calculated for curve data, as well as ICCA,K, and SEM [95 % confidence intervals] for selected peaks. Generally, kinematic ICCs were good (>0.75) and reasonably stable throughout the gait cycle, except for the hip kinematics during the swing phase in the sagittal plane and pelvis tilt and rotation. The integrated and peaks SEM were <2.4°. The reliability of kinetics was similar (ICC>0.75), except for the transverse hip moment and abduction peak, fluctuating more during the swing than through the stance phase. SEM were < 0.07Nm/Kg. In conclusion, these results showed good overall test-retest reliability for markerless gait kinematics and kinetics for the hip, knee, and ankle joints, moderate for the pelvis angles, and error levels of ≤5°, and SEM%≤5% for the sagittal plane. This supports this method's use in assessing gait in healthy older adults, including kinetics, for which reliability data from markerless systems is difficult to find reported.

Musculoskeletal modeling and movement simulation for structural hip disorder research: A scoping review of methods, validation, and applications

Musculoskeletal modeling is a powerful tool to quantify biomechanical factors typically not feasible to measure in vivo, such as hip contact forces and deep muscle activations. While technological advancements in musculoskeletal modeling have increased accessibility, selecting the appropriate modeling approach for a specific research question, particularly when investigating pathological populations, has become more challenging. The purposes of this review were to summarize current modeling and simulation methods in structural hip disorder research, as well as evaluate model validation and study reproducibility. MEDLINE and Web of Science were searched to identify literature relating to the use of musculoskeletal models to investigate structural hip disorders (i.e., involving a bony abnormality of the pelvis, femur, or both). Forty-seven articles were included for analysis, which either compared multiple modeling methods or applied a single modeling workflow to answer a research question. Findings from studies comparing methods were summarized, such as the effect of generic versus patient-specific modeling techniques on model-estimated hip contact forces or muscle forces. The review also discussed limitations in validation practices, as only 11 of the included studies conducted a validation and used qualitative approaches only. Given the lack of information related to model validation, additional details regarding the development and validation of generic models were retrieved from references and modeling software documentation. To address the wide variability and under-reporting of data collection, data processing, and modeling methods highlighted in this review, we developed a template that researchers can complete and include as a table within the methodology section of their manuscripts. The use of this table will help increase transparency and reporting of essential details related to reproducibility and methods without being limited by word count restrictions. Overall, this review provides a comprehensive synthesis of modeling approaches that can help researchers make modeling decisions and evaluate existing literature.

Hop Distance Symmetry Moderately Reflects Knee Biomechanics Symmetry During Landing But Not For Controlled Propulsions

Background

Landing with poor knee sagittal plane biomechanics has been identified as a risk factor for Anterior Cruciate Ligament (ACL) injury. However, it is unclear if the horizontal hop test battery reflects knee function and biomechanics.

Hypothesis/Purpose

To investigate the correlation between clinical limb symmetry index (LSI) and landing and propulsion knee biomechanics during the hop test battery using markerless motion capture.

Study Design

Cross-sectional biomechanics laboratory study.

Methods

Forty-two participants with and without knee surgery (age 28.0 ± 8.0 years) performed the hop test battery which consisted of a single hop for distance, crossover hop, triple hop, and 6-m timed hop in the order listed. Eight high speed cameras were used to collect simultaneous 3D motion data and Theia 3D (Theia Markerless Inc.) was used to generate 3D body model files. Lower limb joint kinematics were calculated in Visual3D. Correlation (Spearman's ρ) was computed between clinical LSI and symmetry in peak and initial contact (IC) knee flexion angle during propulsion and landing phases of each movement.

Results

In the single hop, clinical LSI showed positive correlation with kinematic LSI at peak landing (ρ= 0.39, p=0.011), but no correlation at peak propulsion (ρ= -0.03, p=0.851). In the crossover hop, non-significant correlations were found in both propulsion and landing. In the triple hop, positive correlation was found at peak propulsion (ρ= 0.38, p=0.027), peak landing (ρ= 0.48 - 0.66, p<0.001), and last landing IC (ρ= 0.45, p=0.009). In the timed hop, peak propulsion showed positive correlation (ρ= 0.51, p=0.003).

Conclusions

Single hop and triple hop distance symmetry reflected landing biomechanical symmetry better than propulsion symmetry. Poor scores on the hop test battery reflect asymmetrical knee landing biomechanics, emphasizing the importance of continuing to use the hop test battery as part of clinical decision making.

Level of Evidence

3b.

Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces

The estimation of joint contact forces in musculoskeletal multibody dynamics models typically requires the use of expensive and time-consuming technologies, such as reflective marker-based motion capture (Mocap) system. In this study, we aim to propose a more accessible and cost-effective solution that utilizes the dual smartphone videos (SPV)-driven musculoskeletal multibody dynamics modeling workflow to estimate the lower limb mechanics. Twelve participants were recruited to collect marker trajectory data, force plate data, and motion videos during walking and running. The smartphone videos were initially analyzed using the OpenCap platform to identify key joint points and anatomical markers. The markers were used as inputs for the musculoskeletal multibody dynamics model to calculate the lower limb joint kinematics, joint contact forces, and ground reaction forces, which were then evaluated by the Mocap-based workflow. The root mean square error (RMSE), mean absolute deviation (MAD), and Pearson correlation coefficient (ρ) were adopted to evaluate the results. Excellent or strong Pearson correlations were observed in most lower limb joint angles (ρ = 0.74 ~ 0.94). The averaged MADs and RMSEs for the joint angles were 1.93 ~ 6.56° and 2.14 ~ 7.08°, respectively. Excellent or strong Pearson correlations were observed in most lower limb joint contact forces and ground reaction forces (ρ = 0.78 ~ 0.92). The averaged MADs and RMSEs for the joint lower limb joint contact forces were 0.18 ~ 1.07 bodyweight (BW) and 0.28 ~ 1.32 BW, respectively. Overall, the proposed smartphone video-driven musculoskeletal multibody dynamics simulation workflow demonstrated reliable accuracy in predicting lower limb mechanics and ground reaction forces, which has the potential to expedite gait dynamics analysis in a clinical setting.

Marker Data Enhancement For Markerless Motion Capture

Objective

Human pose estimation models can measure movement from videos at a large scale and low cost; however, open-source pose estimation models typically detect only sparse keypoints, which leads to inaccurate joint kinematics. OpenCap, a freely available service for researchers to measure movement from videos, addresses this issue using a deep learning model- the marker enhancer-that transforms sparse keypoints into dense anatomical markers. However, OpenCap performs poorly on movements not included in the training data. Here, we create a much larger and more diverse training dataset and develop a more accurate and generalizable marker enhancer.

Methods

We compiled marker-based motion capture data from 1176 subjects and synthesized 1433 hours of keypoints and anatomical markers to train the marker enhancer. We evaluated its accuracy in computing kinematics using both benchmark movement videos and synthetic data representing unseen, diverse movements.

Results

The marker enhancer improved kinematic accuracy on benchmark movements (mean error: 4.1°, max: 8.7°) compared to using video keypoints (mean: 9.6°, max: 43.1°) and OpenCap's original enhancer (mean: 5.3°, max: 11.5°). It also better generalized to unseen, diverse movements (mean: 4.1°, max: 6.7°) than OpenCap's original enhancer (mean: 40.4°, max: 252.0°).

Conclusion

Our marker enhancer demonstrates both accuracy and generalizability across diverse movements.

Significance

We integrated the marker enhancer into OpenCap, thereby offering its thousands of users more accurate measurements across a broader range of movements.

Automating Video-Based Two-Dimensional Motion Analysis in Sport? Implications for Gait Event Detection, Pose Estimation, and Performance Parameter Analysis

BACKGROUND

Two-dimensional (2D) video is a common tool used during sports training and competition to analyze movement. In these videos, biomechanists determine key events, annotate joint centers, and calculate spatial, temporal, and kinematic parameters to provide performance reports to coaches and athletes. Automatic tools relying on computer vision and artificial intelligence methods hold promise to reduce the need for time-consuming manual methods.

OBJECTIVE

This study systematically analyzed the steps required to automate the video analysis workflow by investigating the applicability of a threshold-based event detection algorithm developed for 3D marker trajectories to 2D video data at four sampling rates; the agreement of 2D keypoints estimated by an off-the-shelf pose estimation model compared with gold-standard 3D marker trajectories projected to camera's field of view; and the influence of an offset in event detection on contact time and the sagittal knee joint angle at the key critical events of touch down and foot flat.

METHODS

Repeated measures limits of agreement were used to compare parameters determined by markerless and marker-based motion capture.

RESULTS

Results highlighted that a minimum video sampling rate of 100 Hz is required to detect key events, and the limited applicability of 3D marker trajectory-based event detection algorithms when using 2D video. Although detected keypoints showed good agreement with the gold-standard, misidentification of key events-such as touch down by 20 ms resulted in knee compression angle differences of up to 20°.

CONCLUSION

These findings emphasize the need for de novo accurate key event detection algorithms to automate 2D video analysis pipelines.

Differences between lower extremity joint running kinetics captured by marker-based and markerless systems were speed dependent

BACKGROUND

The development of computer vision technology has enabled the use of markerless movement tracking for biomechanical analysis. Recent research has reported the feasibility of markerless systems in motion analysis but has yet to fully explore their utility for capturing faster movements, such as running. Applied studies using markerless systems in clinical and sports settings are still lacking. Thus, the present study compared running biomechanics estimated by marker-based and markerless systems. Given running speed not only affects sports performance but is also associated with clinical injury prevention, diagnosis, and rehabilitation, we aimed to investigate the effects of speed on the comparison of estimated lower extremity joint moments and powers between markerless and marker-based technologies during treadmill running as a concurrent validating study.

METHODS

Kinematic data from marker-based/markerless technologies were collected, along with ground reaction force data, from 16 young adults running on an instrumented treadmill at 3 speeds: 2.24 m/s, 2.91 m/s, and 3.58 m/s (5.0 miles/h, 6.5 miles/h, and 8.0 miles/h). Sagittal plane moments and powers of the hip, knee, and ankle were calculated by inverse dynamic methods. Time series analysis and statistical parametric mapping were used to determine system differences.

RESULTS

Compared to the marker-based system, the markerless system estimated increased lower extremity joint kinetics with faster speed during the swing phase in most cases.

CONCLUSION

Despite the promising application of markerless technology in clinical settings, systematic markerless overestimation requires focused attention. Based on segment pose estimations, the centers of mass estimated by markerless technologies were farther away from the relevant distal joint centers, which led to greater joint moments and powers estimates by markerless vs. marker-based systems. The differences were amplified by running speed.

Kinematic synergies show good consistency when extracted with a low-cost markerless device and a marker-based motion tracking system

Recently, markerless tracking systems, such as RGB-Depth cameras, have spread to overcome some of the limitations of the gold standard marker-based tracking systems. Although these systems are valuable substitutes for human motion analysis, as they guarantee higher flexibility, faster setup time and lower costs, their tracking accuracy is lower with respect to marker-based systems. Many studies quantified the error made by markerless systems in terms of body segment length estimation, articular angles, and biomechanics, concluding that they are appropriate for many clinical applications related to motion analysis. We propose an innovative approach to compare a markerless tracking system (Kinect V2) with a gold standard marker-based system (Vicon), based on motor control assessment. We quantified kinematic synergies from the tracking data of fifteen participants performing multi-directional upper limb movements. Kinematic synergy analysis decomposes the kinematic data into a reduced set of motor primitives that describe how the central nervous system coordinates motion at spatial and temporal level. Synergies were extracted with the recently released mixed-matrix factorization algorithm. Four synergies were extracted from both marker-based and markerless datasets and synergies were grouped in 6 clusters for each dataset. Cosine similarity in each cluster was ⩾0.60 in both systems, showing good consistency of synergies. Good matching was found between synergies extracted from markerless and from marker-based data, with a cosine similarity between matched synergies ⩾0.60 in 5 out 6 synergies. These results showed that the markerless sensor can be feasible for kinematic synergy analysis for gross movements, as it correctly estimates the number of synergies and in most cases also their spatial and temporal organization.

Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis

(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.

Validation of OpenCap: A low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks

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.

The applicability of markerless motion capture for clinical gait analysis in children with cerebral palsy

The aim of this comparative, cross-sectional study was to determine whether markerless motion capture can track deviating gait patterns in children with cerebral palsy (CP) to a similar extent as marker-based motion capturing. Clinical gait analysis (CGA) was performed for 30 children with spastic CP and 15 typically developing (TD) children. Marker data were processed with the Human Body Model and video files with Theia3D markerless software, to calculate joint angles for both systems. Statistical parametric mapping paired t-tests were used to compare the trunk, pelvis, hip, knee and ankle joint angles, for both TD and CP, as well as for the deviation from the norm in the CP group. Individual differences were quantified using mean absolute differences. Markerless motion capture was able to track frontal plane angles and sagittal plane knee and ankle angles well, but individual deviations in pelvic tilt and transverse hip rotation as present in CP were not captured by the system. Markerless motion capture is a promising new method for CGA in children with CP, but requires improvement to better capture several clinically relevant deviations especially in pelvic tilt and transverse hip rotation.

Evaluation of the Margin of Stability during Gait Initiation in Young Healthy Adults, Elderly Healthy Adults and Patients with Parkinson's Disease: A Comparison of Force Plate and Markerless Motion Capture Systems

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.

Comparison of Concurrent and Asynchronous Running Kinematics and Kinetics From Marker-Based and Markerless Motion Capture Under Varying Clothing Conditions

As markerless motion capture is increasingly used to measure 3-dimensional human pose, it is important to understand how markerless results can be interpreted alongside historical marker-based data and how they are impacted by clothing. We compared concurrent running kinematics and kinetics between marker-based and markerless motion capture, and between 2 markerless clothing conditions. Thirty adults ran on an instrumented treadmill wearing motion capture clothing while concurrent marker-based and markerless data were recorded, and ran a second time wearing athletic clothing (shorts and t-shirt) while markerless data were recorded. Differences calculated between the concurrent signals from both systems, and also between each participant's mean signals from both asynchronous clothing conditions were summarized across all participants using root mean square differences. Most kinematic and kinetic signals were visually consistent between systems and markerless clothing conditions. Between systems, joint center positions differed by 3 cm or less, sagittal plane joint angles differed by 5° or less, and frontal and transverse plane angles differed by 5° to 10°. Joint moments differed by 0.3 N·m/kg or less between systems. Differences were sensitive to segment coordinate system definitions, highlighting the effects of these definitions when comparing against historical data or other motion capture modalities.

Comparison of kinematics and joint moments calculations for lower limbs during gait using markerless and marker-based motion capture

Objective: This study aimed at quantifying the difference in kinematic and joint moments calculation for lower limbs during gait utilizing a markerless motion system (TsingVA Technology, Beijing, China) in comparison to values estimated using a marker-based motion capture system (Nokov Motion Capture System, Beijing, China). Methods: Sixteen healthy participants were recruited for the study. The kinematic data of the lower limb during walking were acquired simultaneously based on the markerless motion capture system (120 Hz) and the marker-based motion capture system (120 Hz). The ground reaction force was recorded synchronously using a force platform (1,200 Hz). The kinematic and force data were input into Visual3D for inverse dynamics calculations. Results: The difference in the lower limb joint center position between the two systems was the least at the ankle joint in the posterior/anterior direction, with the mean absolute deviation (MAD) of 0.74 cm. The least difference in measuring lower limb angles between the two systems was found in flexion/extension movement, and the greatest difference was found in internal/external rotation movement. The coefficient of multiple correlations (CMC) of the lower limb three joint moments for both systems exceeded or equaled 0.75, except for the ad/abduction of the knee and ankle. All the Root Mean Squared Deviation (RMSD) of the lower limb joint moment are below 18 N·m. Conclusion: The markerless motion capture system and marker-based motion capture system showed a high similarity in kinematics and inverse dynamic calculation for lower limbs during gait in the sagittal plane. However, it should be noted that there is a notable deviation in ad/abduction moments at the knee and ankle.

Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change

Gait dysfunction is common in many clinical populations and often has a profound and deleterious impact on independence and quality of life. Gait analysis is a foundational component of rehabilitation because it is critical to identify and understand the specific deficits that should be targeted prior to the initiation of treatment. Unfortunately, current state-of-the-art approaches to gait analysis (e.g., marker-based motion capture systems, instrumented gait mats) are largely inaccessible due to prohibitive costs of time, money, and effort required to perform the assessments. Here, we demonstrate the ability to perform quantitative gait analyses in multiple clinical populations using only simple videos recorded using low-cost devices (tablets). We report four primary advances: 1) a novel, versatile workflow that leverages an open-source human pose estimation algorithm (OpenPose) to perform gait analyses using videos recorded from multiple different perspectives (e.g., frontal, sagittal), 2) validation of this workflow in three different populations of participants (adults without gait impairment, persons post-stroke, and persons with Parkinson's disease) via comparison to ground-truth three-dimensional motion capture, 3) demonstration of the ability to capture clinically relevant, condition-specific gait parameters, and 4) tracking of within-participant changes in gait, as is required to measure progress in rehabilitation and recovery. Importantly, our workflow has been made freely available and does not require prior gait analysis expertise. The ability to perform quantitative gait analyses in nearly any setting using only low-cost devices and computer vision offers significant potential for dramatic improvement in the accessibility of clinical gait analysis across different patient populations.

Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data post-processing and analysis

Markerless motion capture has recently attracted significant interest in clinical gait analysis and human movement science. Its ease of use and potential to streamline motion capture recordings bear great potential for out-of-the-laboratory measurements in large cohorts. While previous studies have shown that markerless systems can achieve acceptable accuracy and reliability for kinematic parameters of gait, they also noted higher inter-trial variability of markerless data. Since increased inter-trial variability can have important implications for data post-processing and analysis, this study compared the inter-trial variability of simultaneously recorded markerless and marker-based data. For this purpose, the data of 18 healthy volunteers were used who were instructed to simulate four different gait patterns: physiological, crouch, circumduction, and equinus gait. Gait analysis was performed using the smartphone-based markerless system OpenCap and a marker-based motion capture system. We compared the inter-trial variability of both systems and also evaluated if changes in inter-trial variability may depend on the analyzed gait pattern. Compared to the marker-based data, we observed an increase of inter-trial variability for the markerless system ranging from 6.6% to 22.0% for the different gait patterns. Our findings demonstrate that the markerless pose estimation pipelines can introduce additionally variability in the kinematic data across different gait patterns and levels of natural variability. We recommend using averaged waveforms rather than single ones to mitigate this problem. Further, caution is advised when using variability-based metrics in gait and human movement analysis based on markerless data as increased inter-trial variability can lead to misleading results.

Reliability of Markerless Motion Capture Systems for Assessing Movement Screenings

Background

Movement screenings are commonly used to detect unfavorable movement patterns. Markerless motion capture systems have been developed to track 3-dimensional motion.

Purpose

To determine the reliability of movement screenings assessed using a markerless motion capture system when comparing the results of multiple systems and multiple collection periods.

Study Design

Descriptive laboratory study.

Methods

The inter- and intrarater reliability of a commercially available markerless motion capture system were investigated in 21 recreationally active participants aged between 18 and 22 years. A total of 39 kinematic variables arising from 10 fundamental upper and lower body movements typical of a screening procedure in sports performance were considered. The data were statistically analyzed in terms of relative error via the intraclass correlation coefficient (ICC) and absolute error via the residual standard error (RSE).

Results

Both inter- and intrarater reliability ICCs were at least moderate across all variables (ICC, >0.50), with most movements and corresponding variables having excellent reliability (ICC, >0.90). Although maximum knee valgus angles were the kinematic variables with the lowest interrater reliability (ICCs, 0.59-0.82) and moderate relative agreement, there was agreement in absolute terms with an RSE of <1.3°.

Conclusion

Findings indicated that markerless motion capture provides reliable measurements of joint position during a movement screen, which allows for a more objective evaluation of the direction and subsequent success of interventions. However, practitioners should consider relative and absolute agreements when applying information provided by these systems.

Clinical Relevance

Markerless motion capture systems may assist clinicians by reliably assessing movement screenings using different systems over different collection periods.

Markerless motion capture provides accurate predictions of ground reaction forces across a range of movement tasks

Measuring or estimating the forces acting on the human body during movement is critical for determining the biomechanical aspects relating to injury, disease and healthy ageing. In this study we examined whether quantifying whole-body motion (segmental accelerations) using a commercial markerless motion capture system could accurately predict three-dimensional ground reaction force during a diverse range of human movements: walking, running, jumping and cutting. We synchronously recorded 3D ground reaction forces (force instrumented treadmill or in-ground plates) with high-resolution video from eight cameras that were spatially calibrated relative to a common coordinate system. We used a commercially available software to reconstruct whole body motion, along with a geometric skeletal model to calculate the acceleration of each segment and hence the whole-body centre of mass and ground reaction force across each movement task. The average root mean square difference (RMSD) across all three dimensions and all tasks was 0.75 N/kg, with the maximum average RMSD being 1.85 N/kg for running vertical force (7.89 % of maximum). There was very strong agreement between peak forces across tasks, with R2 values indicating that the markerless prediction algorithm was able to predict approximately 95-99 % of the variance in peak force across all axes and movements. The results were comparable to previous reports using whole-body marker-based approaches and hence this provides strong proof-of-principle evidence that markerless motion capture can be used to predict ground reaction forces and therefore potentially assess movement kinetics with limited requirements for participant preparation.

Markerless gait analysis through a single camera and computer vision

The assessment of gait performance using quantitative measures can yield crucial insights into an individual's health status. Recently, computer vision-based human pose estimation has emerged as a promising solution for markerless gait analysis, as it allows for the direct extraction of gait parameters from videos. This study aimed to compare the lower extremity kinematics and spatiotemporal gait parameters obtained from a single-camera-based markerless method with those acquired from a marker-based motion tracking system across a healthy population. Additionally, we investigated the impact of camera viewing angles and distances on the accuracy of the markerless method. Our findings demonstrated a robust correlation and agreement (Rxy > 0.75, Rc > 0.7) between the markerless and marker-based methods for most spatiotemporal gait parameters. We also observed strong correlations (Rxy > 0.8) between the two methods for hip flexion/extension, knee flexion/extension, hip abduction/adduction, and hip internal/external rotation. Statistical tests revealed significant effects of viewing angles and distances on the accuracy of the identified gait parameters. While the markerless method offers an alternative for general gait analysis, particularly when marker use is impractical, its accuracy for clinical applications remains insufficient and requires substantial improvement. Future investigations should explore the potential of the markerless system to measure gait parameters in pathological gaits.

Agreement between a markerless and a marker-based motion capture systems for balance related quantities

Balance studies usually focus on quantities describing the global body motion. Assessing such quantities using classical marker-based approach can be tedious and modify the participant's behaviour. The recent development of markerless motion capture methods could bypass the issues related to the use of markers. This work compared dynamic balance related quantities obtained with markers and videos. Sixteen young healthy participants performed four different motor tasks: walking at self-selected speed, balance loss, walking on a narrow beam and countermovement jumps. Their movements were recorded simultaneously by marker-based and markerless motion capture systems. Videos were processed using a commercial markerless pose estimation software, Theia3D. The centre of mass position (CoM) was computed, and the associated extrapolated centre of mass position (XCoM) and whole-body angular momentum (WBAM) were derived. Bland-Altman analysis was performed and root mean square difference (RMSD) and coefficient of correlation were computed to compare the results obtained with marker-based and markerless methods. Bias remained of the magnitude of a few mm for CoM and XCoM positions, and RMSD of CoM and XCoM was around 1 cm. RMSD of the WBAM was less than 10 % of the total amplitude in any direction, and bias was less than 1 %. Results suggest that outcomes of balance studies will be similar whether marker-based or markerless motion capture system are used. Nevertheless, one should be careful when assessing dynamic movements such as jumping, as they displayed the biggest differences (both bias and RMSD), although it is unclear whether these differences are due to errors in markerless or marker-based motion capture system.

Vertical Center-of-Mass Braking and Motor Performance during Gait Initiation in Young Healthy Adults, Elderly Healthy Adults, and Patients with Parkinson's Disease: A Comparison of Force-Plate and Markerless Motion Capture Systems

BACKGROUND

This study tested the agreement between a markerless motion capture system and force-plate system ("gold standard") to quantify stability control and motor performance during gait initiation.

METHODS

Healthy adults (young and elderly) and patients with Parkinson's disease performed gait initiation series at spontaneous and maximal velocity on a system of two force-plates placed in series while being filmed by a markerless motion capture system. Signals from both systems were used to compute the peak of forward center-of-mass velocity (indicator of motor performance) and the braking index (indicator of stability control).

RESULTS

Descriptive statistics indicated that both systems detected between-group differences and velocity effects similarly, while a Bland-Altman plot analysis showed that mean biases of both biomechanical indicators were virtually zero in all groups and conditions. Bayes factor 01 indicated strong (braking index) and moderate (motor performance) evidence that both systems provided equivalent values. However, a trial-by-trial analysis of Bland-Altman plots revealed the possibility of differences >10% between the two systems.

CONCLUSION

Although non-negligible differences do occur, a markerless motion capture system appears to be as efficient as a force-plate system in detecting Parkinson's disease and velocity condition effects on the braking index and motor performance.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

Positioning errors of anatomical landmarks identified by fixed vertices in homologous meshes

BACKGROUND

Human movement analysis is usually achieved by tracking markers attached to anatomical landmarks with photogrammetry. Such marker-based systems have disadvantages that have led to the development of markerless procedures, although their accuracy is not usually comparable to that of manual palpation procedures. New motion acquisition systems, such as 3D temporal scanners, provide homologous meshes that can be exploited for this purpose.

RESEARCH QUESTION

Can fixed vertices of a homologous mesh be used to identify anatomical landmarks with an accuracy equivalent to that of manual palpation?

METHODS

We used 3165 human shape scans from the CAESAR dataset, with labelled locations of anatomical landmarks. First, we fitted a template mesh to the scans, and assigned a vertex of that mesh to 53 anatomical landmarks in all subjects. Then we defined a nominal vertex for each landmark, as the more centred vertex out of the set assigned for that landmark. We calculated the errors of the template-fitting and the nominal vertex determination procedures, and analysed their relationship to subject's sex, height and body mass index, as well as their size compared to manual palpation errors.

RESULTS

The template-fitting errors were below 5 mm, and the nominal vertex determination errors reached maximum values of 24 mm. Except for the trochanter, those errors were the same order of magnitude or smaller than inter-examiner errors of lower limb landmarks. Errors increased with height and body mass index, and were smaller for men than for women of the same height and body mass index.

SIGNIFICANCE

We defined a set of vertices for 53 anatomical landmarks in a homologous mesh, which yields location errors comparable to those obtained by manual palpation for the majority of landmarks. We also quantified how the subject's sex and anthropometric features can affect the size of those errors.

Improving Gait Analysis Techniques with Markerless Pose Estimation Based on Smartphone Location

Marker-based 3D motion capture systems, widely used for gait analysis, are accurate but have disadvantages such as cost and accessibility. Whereas markerless pose estimation has emerged as a convenient and cost-effective alternative for gait analysis, challenges remain in achieving optimal accuracy. Given the limited research on the effects of camera location and orientation on data collection accuracy, this study investigates how camera placement affects gait assessment accuracy utilizing five smartphones. This study aimed to explore the differences in data collection accuracy between marker-based systems and pose estimation, as well as to assess the impact of camera location and orientation on accuracy in pose estimation. The results showed that the differences in joint angles between pose estimation and marker-based systems are below 5°, an acceptable level for gait analysis, with a strong correlation between the two datasets supporting the effectiveness of pose estimation in gait analysis. In addition, hip and knee angles were accurately measured at the front diagonal of the subject and ankle angle at the lateral side. This research highlights the significance of careful camera placement for reliable gait analysis using pose estimation, serving as a concise reference to guide future efforts in enhancing the quantitative accuracy of gait analysis.

Evaluation of 3D Markerless Motion Capture System Accuracy during Skate Skiing on a Treadmill

In this study, we developed a deep learning-based 3D markerless motion capture system for skate skiing on a treadmill and evaluated its accuracy against marker-based motion capture during G1 and G3 skating techniques. Participants performed roller skiing trials on a skiing treadmill. Trials were recorded with two synchronized video cameras (100 Hz). We then trained a custom model using DeepLabCut, and the skiing movements were analyzed using both DeepLabCut-based markerless motion capture and marker-based motion capture systems. We statistically compared joint centers and joint vector angles between the methods. The results demonstrated a high level of agreement for joint vector angles, with mean differences ranging from -2.47° to 3.69°. For joint center positions and toe placements, mean differences ranged from 24.0 to 40.8 mm. This level of accuracy suggests that our markerless approach could be useful as a skiing coaching tool. The method presents interesting opportunities for capturing and extracting value from large amounts of data without the need for markers attached to the skier and expensive cameras.

Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running

The knee flexion angle is an important measurement for studies of the human gait. Running is a common activity with a high risk of knee injury. Studying the running gait in realistic situations is challenging because accurate joint angle measurements typically come from optical motion-capture systems constrained to laboratory settings. This study considers the use of shank and thigh inertial sensors within three different filtering algorithms to estimate the knee flexion angle for running without requiring sensor-to-segment mounting assumptions, body measurements, specific calibration poses, or magnetometers. The objective of this study is to determine the knee flexion angle within running applications using accelerometer and gyroscope information only. Data were collected for a single test participant (21-year-old female) at four different treadmill speeds and used to validate the estimation results for three filter variations with respect to a Vicon optical motion-capture system. The knee flexion angle filtering algorithms resulted in root-mean-square errors of approximately three degrees. The results of this study indicate estimation results that are within acceptable limits of five degrees for clinical gait analysis. Specifically, a complementary filter approach is effective for knee flexion angle estimation in running applications.

Feasibility of OpenPose markerless motion analysis in a real athletics competition

This study tested the performance of OpenPose on footage collected by two cameras at 200 Hz from a real-life competitive setting by comparing it with manually analyzed data in SIMI motion. The same take-off recording from the men's Long Jump finals at the 2017 World Athletics Championships was used for both approaches (markerless and manual) to reconstruct the 3D coordinates from each of the camera's 2D coordinates. Joint angle and Centre of Mass (COM) variables during the final step and take-off phase of the jump were determined. Coefficients of Multiple Determinations (CMD) for joint angle waveforms showed large variation between athletes with the knee angle values typically being higher (take-off leg: 0.727 ± 0.242; swing leg: 0.729 ± 0.190) than those for hip (take-off leg: 0.388 ± 0.193; swing leg: 0.370 ± 0.227) and ankle angle (take-off leg: 0.247 ± 0.172; swing leg: 0.155 ± 0.228). COM data also showed considerable variation between athletes and parameters, with position (0.600 ± 0.322) and projection angle (0.658 ± 0.273) waveforms generally showing better agreement than COM velocity (0.217 ± 0.241). Agreement for discrete data was generally poor with high random error for joint kinematics and COM parameters at take-off and an average ICC across variables of 0.17. The poor agreement statistics and a range of unrealistic values returned by the pose estimation underline that OpenPose is not suitable for in-competition performance analysis in events such as the long jump, something that manual analysis still achieves with high levels of accuracy and reliability.

Using markerless motion capture and musculoskeletal models: An evaluation of joint kinematics

BACKGROUND

This study presents a comprehensive comparison between a marker-based motion capture system (MMC) and a video-based motion capture system (VMC) in the context of kinematic analysis using musculoskeletal models.

OBJECTIVE

Focusing on joint angles, the study aimed to evaluate the accuracy of VMC as a viable alternative for biomechanical research.

METHODS

Eighteen healthy subjects performed isolated movements with 17 joint degrees of freedom, and their kinematic data were collected using both an MMC and a VMC setup. The kinematic data were entered into the AnyBody Modelling System, which enables the calculation of joint angles. The mean absolute error (MAE) was calculated to quantify the deviations between the two systems.

RESULTS

The results showed good agreement between VMC and MMC at several joint angles. In particular, the shoulder, hip and knee joints showed small deviations in kinematics with MAE values of 4.8∘, 6.8∘ and 3.5∘, respectively. However, the study revealed problems in tracking hand and elbow movements, resulting in higher MAE values of 13.7∘ and 27.7∘. Deviations were also higher for head and thoracic movements.

CONCLUSION

Overall, VMC showed promising results for lower body and shoulder kinematics. However, the tracking of the wrist and pelvis still needs to be refined. The research results provide a basis for further investigations that promote the fusion of VMC and musculoskeletal models.

Thermal profile classification of the back of sportive and sedentary healthy individuals

BACKGROUND

Infrared thermography (IRT) is a non-harmful, risk-free imaging technique and it has application for healthy and pathological population.

OBJECTIVE

The aim of this study is to evaluate the thermographic profiles of the back of sport practitioners from different disciplines and compare it with those of sedentary healthy individuals.

METHOD

The back of 160 healthy subjects were evaluated, and participants were grouped considering their sport practice: team sport (TS), individual sport (IS), weight training (WT), inactive (I). Three regions of interest were identified to analyze the cervical, thoracic and lumbar temperatures of the back.

RESULTS

The Multivariate analysis of variance (MANOVA) resulted significant showing statistical differences for the cervical (p < 0.001), dorsal (p = 0.0011), and lumbar areas (p = 0.0366). The Tukey post-hoc test for pairwise comparison showed statistically significant differences between groups. For the cervical area significance was found between the IN and WT group (p = 0.002), the IN and IS group (p < 0.001), IN and TS group (p = 0.020). The dorsal area resulted significant between the IN and WT group (p = 0.007), the IN and IS group (p < 0.001), IN and TS group. The lumbar area showed significant differences only between the IN and WT group and the IN and IS group (p = 0.043).

CONCLUSION

This study demonstrated that inactive individuals manifest a statistically significant higher temperature in the cervical, dorsal and lumbar area of the back compared to sportive individuals.

The role of laboratory investigations in the classification of tremors

INTRODUCTION

Tremor is the most common movement disorder. Although clinical examination plays a significant role in evaluating patients with tremor, laboratory tests are useful to classify tremors according to the recent two-axis approach proposed by the International Parkinson and Movement Disorders Society.

METHODS

In the present review, we will discuss the usefulness and applicability of the various diagnostic methods in classifying and diagnosing tremors. We will evaluate a number of techniques, including laboratory and genetic tests, neurophysiology, and neuroimaging. The role of newly introduced innovative tremor assessment methods will also be discussed.

RESULTS

Neurophysiology plays a crucial role in tremor definition and classification, and it can be useful for the identification of specific tremor syndromes. Laboratory and genetic tests and neuroimaging may be of paramount importance in identifying specific etiologies. Highly promising innovative technologies are being developed for both clinical and research purposes.

CONCLUSIONS

Overall, laboratory investigations may support clinicians in the diagnostic process of tremor. Also, combining data from different techniques can help improve understanding of the pathophysiological bases underlying tremors and guide therapeutic management.

AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subject's skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

Examination of 2D frontal and sagittal markerless motion capture: Implications for markerless applications

This study examined if occluded joint locations, obtained from 2D markerless motion capture (single camera view), produced 2D joint angles with reduced agreement compared to visible joints, and if 2D frontal plane joint angles were usable for practical applications. Fifteen healthy participants performed over-ground walking whilst recorded by fifteen marker-based cameras and two machine vision cameras (frontal and sagittal plane). Repeated measures Bland-Altman analysis illustrated that markerless standard deviation of bias and limits of agreement for the occluded-side hip and knee joint angles in the sagittal plane were double that of the camera-side (visible) hip and knee. Camera-side sagittal plane knee and hip angles were near or within marker-based error values previously observed. While frontal plane limits of agreement accounted for 35-46% of total range of motion at the hip and knee, Bland-Altman bias and limits of agreement (-4.6-1.6 ± 3.7-4.2˚) were actually similar to previously reported marker-based error values. This was not true for the ankle, where the limits of agreement (± 12˚) were still too high for practical applications. Our results add to previous literature, highlighting shortcomings of current pose estimation algorithms and labelled datasets. As such, this paper finishes by reviewing methods for creating anatomically accurate markerless training data using marker-based motion capture data.

Markerless Motion Tracking With Noisy Video and IMU Data

OBJECTIVE

Marker-based motion capture, considered the gold standard in human motion analysis, is expensive and requires trained personnel. Advances in inertial sensing and computer vision offer new opportunities to obtain research-grade assessments in clinics and natural environments. A challenge that discourages clinical adoption, however, is the need for careful sensor-to-body alignment, which slows the data collection process in clinics and is prone to errors when patients take the sensors home.

METHODS

We propose deep learning models to estimate human movement with noisy data from videos (VideoNet), inertial sensors (IMUNet), and a combination of the two (FusionNet), obviating the need for careful calibration. The video and inertial sensing data used to train the models were generated synthetically from a marker-based motion capture dataset of a broad range of activities and augmented to account for sensor-misplacement and camera-occlusion errors. The models were tested using real data that included walking, jogging, squatting, sit-to-stand, and other activities.

RESULTS

On calibrated data, IMUNet was as accurate as state-of-the-art models, while VideoNet and FusionNet reduced mean ± std root-mean-squared errors by 7.6 ± 5.4 ° and 5.9 ± 3.3 °, respectively. Importantly, all the newly proposed models were less sensitive to noise than existing approaches, reducing errors by up to 14.0 ± 5.3 ° for sensor-misplacement errors of up to 30.0 ± 13.7 ° and by up to 7.4 ± 5.5 ° for joint-center-estimation errors of up to 101.1 ± 11.2 mm, across joints.

CONCLUSION

These tools offer clinicians and patients the opportunity to estimate movement with research-grade accuracy, without the need for time-consuming calibration steps or the high costs associated with commercial products such as Theia3D or Xsens, helping democratize the diagnosis, prognosis, and treatment of neuromusculoskeletal conditions.

Intrarater and Interrater Reliability and Agreement of a Method to Quantify Lower-Extremity Kinematics Using Remote Data Collection

CONTEXT

To assess the reliability of a remote 2D markerless motion tracking method (Kinovea) to quantify knee and hip angles during dynamic tasks.

METHODS

Fourteen healthy adults performed body weight squats and lateral lunges while video recording themselves at home. Knee and hip angles were quantified in the sagittal plane for the squats and in the frontal plane for the lateral lunges. Two students each performed the video analysis procedure twice, 2 weeks apart. Intraclass correlation coefficients were used to calculate the intrarater and interrater reliability for angles at maximum depth. The intrarater and interrater agreement over the joint angle-time signals were quantified using a validation metric; an acceptable agreement threshold was set at a validation metric of 0.803 or higher. Standard error of measurement (SEM) was also calculated.

RESULTS

Reliability was good to excellent (intraclass correlation coefficients = .80-.98) for all angle comparisons at maximum depth. The agreement over the entire joint angle-time signal was acceptable for all squat variables except for the interrater hip angle comparison (validation metric = 0.797). None of the lateral lunge variables met the threshold of acceptable agreement. The mean SEM across participants for all joint angle-time signal and for maximum depth was acceptable (<5°) for all measurements (SEM = 1.2°-4.9°).

CONCLUSIONS

Overall, the reliability, agreement, and SEM quantified in this study support the integration of remote methods to quantify lower-extremity kinematics into research and clinical practice.

The Three-Dimensional Body Center of Mass at the Workplace under Hypogravity

The center of mass dynamics of the seated posture of humans in a work environment under hypogravity (0 < g < 1) have rarely been investigated, and such research is yet to be carried out. The present study determined the difference in the body system of 32 participants working under simulated 1/6 g (Moon) and 1 g (Earth) for comparison using static and dynamic task measurements. This was based on a markerless motion capture method that analyzed participants' center of mass at the start, middle and end of the task when they began to get fatigued. According to this analysis, there is a positive relationship (p < 0.01) with a positive coefficient of correlation between the downward center of mass body shift along the proximodistal axis and gravity level for males and females. At the same time, the same positive relationship (p < 0.01) between the tilt of the body backward along the anterior-posterior axis and the level of gravity was found only in females. This offers fresh perspectives for comprehending hypogravity in a broader framework regarding its impact on musculoskeletal disorders. It can also improve workplace ergonomics, body stability, equipment design, and biomechanics.

Bioinspired robots can foster nature conservation

We live in a time of unprecedented scientific and human progress while being increasingly aware of its negative impacts on our planet's health. Aerial, terrestrial, and aquatic ecosystems have significantly declined putting us on course to a sixth mass extinction event. Nonetheless, the advances made in science, engineering, and technology have given us the opportunity to reverse some of our ecosystem damage and preserve them through conservation efforts around the world. However, current conservation efforts are primarily human led with assistance from conventional robotic systems which limit their scope and effectiveness, along with negatively impacting the surroundings. In this perspective, we present the field of bioinspired robotics to develop versatile agents for future conservation efforts that can operate in the natural environment while minimizing the disturbance/impact to its inhabitants and the environment's natural state. We provide an operational and environmental framework that should be considered while developing bioinspired robots for conservation. These considerations go beyond addressing the challenges of human-led conservation efforts and leverage the advancements in the field of materials, intelligence, and energy harvesting, to make bioinspired robots move and sense like animals. In doing so, it makes bioinspired robots an attractive, non-invasive, sustainable, and effective conservation tool for exploration, data collection, intervention, and maintenance tasks. Finally, we discuss the development of bioinspired robots in the context of collaboration, practicality, and applicability that would ensure their further development and widespread use to protect and preserve our natural world.

Hypogravity modeling of upper extremities: an investigation of manual handling in the workplace

Experiments on the lower limbs are the only approaches being used to study how hypogravity (HG) (0 < g < 1, e.g., Moon: 1/6 g, Mars: 3/8 g) affects human movement. The goal of this study was to expand this field experimentally by investigating the effect of HG on the upper extremities during one-handed manual handling tasks in a sitting posture: static weight holding with an outstretched arm, and slow repetitive weight lifting and lowering motions. The hypothesis was that while completing static and dynamic tasks with elements of repetition in HG, the upper body's tilt (angle regarding the vertical axis) would change differently from Earth's gravity. Specifically, upper arm and spine angles, joint torques, and forces were investigated. Twenty-four healthy participants aged 33.6 ± 8.2 years were involved in the trial. Joint angles were examined using vision-based 3D motion analysis. According to this investigation, there is a correlation between a body tilting backward and a gravity level reduction (p < 0.01). Thus, HG causes postural deviation, and this shows that workplace design must be adapted according to the level of gravity to promote comfortable and balanced body alignment, minimizing stress on muscles and joints. To lower the risk of musculoskeletal disorders (MSDs), enhance overall performance, and increase job satisfaction, proper support systems and restrictions for sitting positions should be taken into account, concerning different levels of gravity.

Hop to It! The Relationship Between Hop Tests and The Anterior Cruciate Ligament - Return to Sport Index After Anterior Cruciate Ligament Reconstruction in NCAA Division 1 Collegiate Athletes

Background

Outcomes after anterior cruciate ligament reconstruction (ACLR) may not be optimal, with poor physical and psychological function potentially affecting return to sport (RTS) ability. Understanding the relationship between commonly used hop tests and the Anterior Cruciate Ligament - Return to Sport Index (ACL-RSI) may improve rehabilitation strategies and optimize patient outcomes.

Hypothesis/Purpose

The purpose of this study was to examine the relationship between ACL-RSI scores and limb symmetry index (LSI) for the single hop for distance (SHD), triple hop for distance (THD), crossover hop for distance (CHD), timed 6-meter hop (T6H), and single leg vertical hop (SLVH) in a cohort of National Collegiate Athletic Association (NCAA) Division 1 collegiate athletes after ACLR. The hypothesis was that SLVH LSI would be more highly correlated with ACL-RSI score than all horizontal hop tests.

Study design

Cross-Sectional Study.

Methods

Twenty-one National Collegiate Athletic Association (NCAA) Division 1 collegiate athletes (7 males, 14 females) at 6.62 ± 1.69 months after ACLR were included in this retrospective study. Primary outcomes were ACL-RSI score and LSI for SHD, THD, CHD, T6H, and SLVH. The relationship between ACL-RSI scores and performance on hop tests (LSIs) was evaluated using correlation analysis and step-wise linear regression (p ≤ 0.05).

Results

There were significant correlations found when comparing ACL-RSI and the LSI for SHD (rs = 0.704, p < 0.001), THD (rs = 0.617, p = 0.003), CHD (rs = 0.580, p = 0.006), and SLVH (rs = 0.582, p = 0.006). The CHD explained 66% (R2 value of 0.660) of the variance in the ACL-RSI, while the other hop tests did not add to the predictive model.

Conclusions

Physical function has the capacity to influence psychological status after ACLR. Clinicians should recognize that SLVH, SHD, THD, and CHD are correlated with ACL-RSI and improvements in physical function during rehabilitation may improve psychological status and optimize RTS after ACLR.

Level of evidence

Level 3.

OpenCap: Human movement dynamics from smartphone videos

Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.

A comparison of three-dimensional kinematics between markerless and marker-based motion capture in overground gait

Vision-based methods using RGB inputs for human pose estimation have grown in recent years but have undergone limited testing in clinical and biomechanics research areas like gait analysis. The purpose of the present study was to compare lower extremity kinematics during overground gait between a traditional marker-based approach and a commercial multi-view markerless system in a sample of subjects including young adults, older adults, and adults diagnosed with Parkinson's disease. A convenience sample of 35 adults between the age of 18-85 years were included in this study, yielding a total of 114 trials and 228 gait cycles that were compared between systems. A total of 30 time normalized waveforms, including three-dimensional joint centers, segment angles, and joint angles were compared between systems using root mean-squared error (RMSE), range of motion difference (ΔROM), Pearson correlation coefficients (r), and interclass correlation coefficients (ICC). RMSEs for joint center positions were less than 28 mm in all joints with correlations indicating good to excellent agreement. RMSEs for segment and joint angles were in range of previous results, with highest agreement between systems in the sagittal plane. ΔROM differences were within reference values that characterize clinical groups like Parkinson's disease, stroke, or knee osteoarthritis. Further improvements in pelvis tracking, markerless keypoint model definitions, and standardization of comparison study protocols are needed. Nevertheless, markerless solutions seem promising toward unrestricted motion analysis in biomechanics research and clinical settings.

Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait

Markerless motion capturing has the potential to provide a low-cost and accessible alternative to traditional marker-based systems for real-world biomechanical assessment. However, before these systems can be put into practice, we need to rigorously evaluate their accuracy in estimating joint kinematics for various gait patterns. This study evaluated the accuracy of a low-cost, open-source, and smartphone-based markerless motion capture system, namely OpenCap, for measuring 3D joint kinematics in healthy and pathological gait compared to a marker-based system. 21 healthy volunteers were instructed to walk with four different gait patterns: physiological, crouch, circumduction, and equinus gait. Three-dimensional kinematic data were simultaneously recorded using the markerless and a marker-based motion capture system. The root mean square error (RMSE) and the peak error were calculated between every joint kinematic variable obtained by both systems. We found an overall RMSE of 5.8 (SD: 1.8 degrees) and a peak error of 11.3 degrees (SD: 3.9). A repeated measures ANOVA with post hoc tests indicated significant differences in RMSE and peak errors between the four gait patterns (p ¡ 0.05). Physiological gait presented the lowest, crouch and circumduction gait the highest errors. Our findings indicate a roughly comparable accuracy to IMU-based approaches and commercial markerless multi-camera solutions. However, errors are still above clinically desirable thresholds of two to five degrees. While our findings highlight the potential of markerless systems for assessing gait kinematics, they also underpin the need to further improve the underlying deep learning algorithms to make markerless pose estimation a valuable tool in clinical settings.

Lower-limb sagittal joint angles during gait can be predicted based on foot acceleration and angular velocity

Background and purpose

Continuous monitoring of lower-limb movement may help in the early detection and control/reduction of diseases (such as the progression of orthopedic diseases) by applying suitable interventions. Therefore, it is invaluable to calculate the lower-limb movement (sagittal joint angles) while walking daily for continuous evaluation of such risks. Although cameras in a motion capture system are necessary for calculating lower-limb sagittal joint angles during gait, the method is unrealistic considering the setting is difficult to achieve in daily life. Therefore, the estimation of lower-limb sagittal joint angles during walking based on variables, which can be measured using wearable sensors (e.g., foot acceleration and angular velocity), is important. This study estimates the lower-limb sagittal joint angles during gait from the norms of foot acceleration and angular velocity using machine learning and validates the accuracy of the estimated joint angles with those obtained using a motion capture system.

Methods

Healthy adults (n = 200) were asked to walk at a comfortable speed (10 trials), and their lower-limb sagittal joint angles, foot accelerations, and angular velocities were obtained. Using these variables, we established a feedforward neural network and estimated the lower-limb sagittal joint angles.

Results

The average root mean squared errors of the lower-limb sagittal joint angles during gait ranged between 2.5°-7.0° (hip: 7.0°; knee: 4.0°; and ankle: 2.5°).

Conclusion

These results show that we can estimate the lower-limb sagittal joint angles during gait using only the norms of foot acceleration and angular velocity, which can help calculate the lower-limb sagittal joint angles during daily walking.

AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subjecťs skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 c m , and residual force magnitudes smaller than 2 % of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements

Motion analysis is essential for assessing in-vivo human biomechanics. Marker-based motion capture is the standard to analyze human motion, but the inherent inaccuracy and practical challenges limit its utility in large-scale and real-world applications. Markerless motion capture has shown promise to overcome these practical barriers. However, its fidelity in quantifying joint kinematics and kinetics has not been verified across multiple common human movements. In this study, we concurrently captured marker-based and markerless motion data on 10 healthy study participants performing 8 daily living and exercise movements. We calculated the correlation (Rxy) and root-mean-square difference (RMSD) between markerless and marker-based estimates of ankle dorsi-plantarflexion, knee flexion, and three-dimensional hip kinematics (angles) and kinetics (moments) during each movement. Estimates from markerless motion capture matched closely with marker-based in ankle and knee joint angles (Rxy ≥ 0.877, RMSD ≤ 5.9°) and moments (Rxy ≥ 0.934, RMSD ≤ 2.66 % height × weight). High outcome comparability means the practical benefits of markerless motion capture can simplify experiments and facilitate large-scale analyses. Hip angles and moments demonstrated more differences between the two systems (RMSD: 6.7-15.9° and up to 7.15 % height × weight), especially during rapid movements such as running. Markerless motion capture appears to improve the accuracy of hip-related measures, yet more research is needed for validation. We encourage the biomechanics community to continue verifying, validating, and establishing best practices for markerless motion capture, which holds exciting potential to advance collaborative biomechanical research and expand real-world assessments needed for clinical translation.

Prediction of gait kinetics using Markerless-driven musculoskeletal modeling

Video-based motion analysis systems are emerging in the biomechanics research community, yet there is limited exploration of kinetics prediction using RGB-markerless kinematics and musculoskeletal modeling. This project aimed to provide ground reaction force (GRF) and ground reaction moment (GRM) predictions during over-ground gait by introducing RGB-markerless kinematics into a musculoskeletal modeling framework. Full-body markerless kinematic inputs and musculoskeletal modeling were used to obtain GRF and GRM predictions which were compared to measured force plate values. The markerless-driven predictions yielded average root mean-squared error (RMSE) in the stance phase of 0.035 ± 0.009 N∙BW-1, 0.070 ± 0.014 N∙BW-1, and 0.155 ± 0.041 N∙BW-1 in the mediolateral (ML), anteroposterior (AP), and vertical (V) GRFs. This was accompanied by moderate to high correlations and interclass correlation coefficients (ICC) indicating moderate to good agreement between measured and predicted values (95% Confidence Inervals: ML = [0.479, 0.717], AP = [0.714, 0.856], V = [0.803, 0.905]). For ground reaction moments (GRM), average RMSE was 0.029 ± 0.013 Nm∙BWH-1, 0.014 ± 0.005 Nm∙BWH-1, and 0.005 ± 0.002 Nm∙BWH-1 in the sagittal, frontal, and transverse planes. Pearson correlations and ICCs indicated poor agreement between systems for GRMs (95% Confidence Intervals: Sagittal = [0.314, 0.608], Frontal = [0.006, 0.373], Transverse = [0.269, 0.570]). Currently, RMSE is larger than target thresholds set from studies using Kinect, inertial, or marker-based kinematic drivers; but methodological considerations highlighted in this work may help guide follow-up iterations. At this point, further use in research or clinical practice is cautioned until methodological considerations are addressed, although results are promising at this point.

Patellofemoral Joint Loading Progression Across 35 Weightbearing Rehabilitation Exercises and Activities of Daily Living

BACKGROUND

Exercises that provide progressive therapeutic loading are a central component of patellofemoral pain rehabilitation, but quantitative evidence on patellofemoral joint loading is scarce for a majority of common weightbearing rehabilitation exercises.

PURPOSE

To define a loading index to quantify, compare, rank, and categorize overall loading levels in the patellofemoral joint across 35 types of weightbearing rehabilitation exercises and activities of daily living.

STUDY DESIGN

Descriptive laboratory study.

METHODS

Model-estimated knee flexion angles and extension moments based on motion capture and ground-reaction force data were used to quantify patellofemoral joint loading in 20 healthy participants who performed each exercise. A loading index was computed via a weighted sum of loading peak and cumulative loading impulse for each exercise. The 35 rehabilitation exercises and daily living activities were then ranked and categorized into low, moderate, and high "loading tiers" according to the loading index.

RESULTS

Overall patellofemoral loading levels varied substantially across the exercises and activities, with loading peak ranging from 0.6 times body weight during walking to 8.2 times body weight during single-leg decline squat. Most rehabilitation exercises generated a moderate level of patellofemoral joint loading. Few weightbearing exercises provided low-level loading that resembled walking or high-level loading with both high magnitude and duration. Exercises with high knee flexion tended to generate higher patellofemoral joint loading compared with high-intensity exercises.

CONCLUSION

This study quantified patellofemoral joint loading across a large collection of weightbearing exercises in the same cohort.

CLINICAL RELEVANCE

The visualized loading index ranks and modifiable worksheet may assist clinicians in planning patient-specific exercise programs for patellofemoral pain rehabilitation.

Comparison of kinematics between Theia markerless and conventional marker-based gait analysis in clinical patients

BACKGROUND

Markerless motion capture systems have the potential to make clinical gait analysis more efficient and convenient. Theia3D is a commercially available markerless system that may serve as an alternative to traditional gait analysis for clinical gait laboratories.

RESEARCH QUESTION

What is the concurrent validity of markerless gait analysis using Theia3D compared to traditional marker-based gait analysis in pediatric clinical gait patients?

METHODS

Thirty-six patients (20 male, age 2-25 years) with a range of diagnoses underwent clinical gait analysis with data being captured concurrently by a traditional marker-based motion capture system (Vicon Nexus) and a commercial markerless system (Theia3D). Multiple left strides were averaged for each subject, and the difference in kinematics (Theia - Vicon) was calculated over the gait cycle and evaluated using root mean square difference (RMSD), mean difference, and RMSD after subtracting the mean value across the gait cycle (RMSD_offset). Sub-analysis was performed for 25 patients with foot deformities, 9 wearing ankle-foot orthoses, and 6 walking with assistance (cane, crutches, walker, or handheld).

RESULTS

Kinematics showed similar patterns between the marker-based and markerless systems. RMSD was < 6° except for pelvic tilt, hip flexion, ankle inversion, foot progression, and transverse plane rotation of the hip, knee, and ankle. These measures mainly differed due to an offset between the curves. After adjusting for offsets, all RMSD_offset were < 6°. RMSD was larger for patients with foot deformities, wearing orthoses, or using assistive devices, but all RMSD_offset were still < 8°. In some cases, however, the markerless system had greater trial-to-trial variability, showed a larger knee varus "bump" in swing, or failed to track the subject.

SIGNIFICANCE

This study provides preliminary evidence of concurrent validity of Theia3D for pediatric patients with abnormal gait. However, some questions remain regarding identification of the knee axis and for patients with foot deformity or assistive devices.

Fifty years of performance-related sports biomechanics research

Over the past fifty years there has been considerable development in motion analysis systems and in computer simulation modelling of sports movements while the relevance and importance of functional variability of sports technique has become increasingly recognised. Technical developments for experimental work have led to increased, and still increasing, subject numbers. Increased subjects per study give better statistical power, the ability to utilise different data analyses, and thus the determination of more subtle and nuanced factors. The overall number of studies has also increased massively. Most actions in sport can, and have, been studied at some level with even the more challenging ones, such as player on player impacts, having some developing research. Computer simulation models of sports movements have ranged from simple (one or two segment) models to very complex musculoskeletal models and have used parameters ranging from the generic to individual-specific. Simple models have given insights into the key mechanics of movement while individual-specific model optimisations have been used to improve athlete performance. Our depth of understanding of the mechanics of sports techniques has increased across a wide range of sports. In the future there is likely to be more development and use of markerless motion capture, individual-specific model parameters, and more consideration of motor control aspects in the analysis of sports technique.