Pelvis and lower limb anatomical landmark calibration precision and its propagation to bone geometry and joint angles

Reconstruction of occluded pelvis markers during marker-based motion capture with industrial exoskeletons

Industrial back support exoskeletons are a promising solution to alleviate lumbar musculoskeletal strain. Due to the complexity of spinal loading, evaluation of EMG data alone has been considered insufficient to assess their support effects, and complementary kinematic and dynamic data are required. However, the acquisition of marker-based kinematics is challenging with exoskeletons, as anatomical reference points, particularly on the pelvis, are occluded by exoskeleton structures. The aim of this study was therefore to develop and validate a method to reliably reconstruct the occluded pelvic markers. The movement data of six subjects, for whom pelvic markers could be placed while wearing an exoskeleton, were used to test the reconstructions and compare them to anatomical landmarks during lifting, holding and walking. Two separate approaches were used for the reconstruction. One used a reference coordinate system based on only exoskeleton markers (EXO), as has been suggested in the literature, while our proposed method adds a technical marker in the lumbar region (LUMB) to compensate for any shifting between exoskeleton and pelvis. Reconstruction with EXO yielded on average an absolute linear deviation of 54 mm ± 16 mm (mean ± 1SD) compared to anatomical markers. The additional marker in LUMB reduced mean deviations to 14 mm ± 7 mm (mean ± 1SD). Both methods were compared to reference values from the literature for expected variances due to marker placement and soft tissue artifacts. For LUMB 99% of reconstructions were within the defined threshold of 24 mm ±9 mm while for EXO 91% were outside.

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.

Conventional video recordings dependably quantify whole-body lifting strategy using the Stoop-Squat-Index: A methods comparison against motion capture and a reliability study

Whole-body lifting strategies could be derived from conventional video recordings using the Stoop-Squat-Index, which quantifies the ratio between trunk forward lean and lower extremity joint flexion from 0 (full squat) to 100 (full stoop). The purpose of this study was to compare Stoop-Squat-Indices derived from conventional video recordings to those from a three-dimensional marker-based motion capture system and to evaluate interrater and intrarater reliability of the video-based approach. Thirty healthy participants lifted a 5-kg box under different conditions (freestyle, squat, stoop). Kinematic data were recorded using a Vicon motion capture system (serving as reference standard) and an iPad camera. Stoop-Squat-Indices over the entire lifting cycle were derived separately from both approaches. Agreement was assessed using mean differences (video minus motion capture) and limits of agreement. Reliability was investigated by calculating intraclass correlation coefficients (ICC) and minimal detectable changes (MDC) over the course of the lifting cycle. Systematic errors were identified with Statistical Parametric Mapping-based T-tests. Systematic errors between the video-based and the motion capture-based approach were observed among all conditions. Mean differences in Stoop-Squat-Indices over the lifting cycle ranged from -6.9 to 3.2 (freestyle), from -1.8 to 5.3 (squat) and from -2.8 to -1.1 (stoop). Limits of agreement were lower when the box was close to the floor, and higher towards upright standing. Reliability of the video-based approach was excellent for most of the lifting cycle, with ICC above 0.995 and MDC below 3.5. These findings support using a video-based assessment of Stoop-Squat-Indices to quantify whole-body lifting strategy in field.

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.

A review of combined functional neuroimaging and motion capture for motor rehabilitation

BACKGROUND

Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation.

RESEARCH OBJECTIVE

This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach.

METHODS

This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted.

RESULTS

Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships.

CONCLUSION

The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.

The knee kinematic patterns and associated factors in healthy Thai adults

BACKGROUND

Reference values for normal knee kinematics were limited in Asian population and were influenced by race and other factors. This study was aimed to establish the reference values and identify the factors associated with knee kinematics in healthy Thai adults, aged 18-40 years.

METHODS

A retrospective cohort study was conducted between 2016 and 2020. Healthy Thai adults aged 18-40 years old with body mass index (BMI) between 18.5 and 24.9 kg/m2 were included. All eligible participants were attached with reflective markers. Their walking was captured by 8-digital cameras, and assessed by motion analysis software. The primary outcomes were average knee kinematic data (degrees) in three dimensional planes as valgus-varus, flexion-extension, and internal-external rotation. Paired t-test and multiple linear regression were applied to compare the outcomes and to determine their associated factors.

RESULTS

Ninety-eight participants (60 females and 38 males) were included with mean age 28.5 ± 5.4 years, and BMI 21.1 ± 2.0 kg/m2. Knee kinematics showed slight adduction during the swing phase, flexion during the stance phase, and obvious external rotation throughout the gait cycle, with a peak of 30-31 degrees during mid-swing. Right knee was significantly more adducted, flexed and externally rotated than the left side, particularly at mid-stance (P = 0.047, 0.017, and < 0.001, respectively). Females had more knee abduction, flexion and external rotation than males. Age, sex, and BMI were significantly correlated with knee abduction at terminal stance (correlation coefficient - 0.12, 95% confidence interval (CI) -0.23, -0.01; -1.37, 95%CI -2.54, -0.20; and - 0.32, 95%CI -0.61, -0.39, respectively), and rotation at mid-swing (correlation coefficient - 0.36, 95%CI -0.69, -0.02; -7.37, 95%CI -10.82, -3.92; and 0.89, 95%CI 0.01, 1.78, respectively).

CONCLUSION

Knee kinematics demonstrates external tibial rotation throughout the gait cycle, significant side differences, and are associated with age, sex, and BMI. Reference values from this study will be useful for functional gait assessment in healthy Thais. However, further comprehensive knee kinetic study including spatio-temporal parameter is recommended.

Shape-model scaling is more robust than linear scaling to marker placement error

When reconstructing bone geometry to calculate joint kinematics, shape-model scaling can be more accurate and repeatable than linear scaling given the same anatomical landmarks. This study perturbed anatomical landmarks from optical motion capture and determined the robustness of shape-model scaling to misplaced markers compared to a traditional approach of linear scaling. We hypothesised that shape-model scaling would be less susceptible to variance in marker positions compared to linear scaling. The positions of hip joint centres and femoral/tibial segment lengths across perturbations were compared to determine each scaling method's range of geometric variation. The standard deviation (SD) of the hip joint centre location from the shape model had a maximum of 1.4 mm, compared to 4.2 mm for linear scaling. Femoral and tibial segments displayed SD's of 5.4 mm and 5.2 mm when shape-model scaled, compared to 9.2 mm and 9.5 mm with linear scaling, respectively, thus supporting our hypothesis. Geometric constraints within a shape model provide robustness to marker misplacement providing potential improvements in repeatability and data exchange.

Evaluation of lower limb and pelvic marker placement precision among different evaluators and its impact on gait kinematics computed with the Conventional Gait Model

BACKGROUND

Gait analysis relies on the accurate and precise identification of anatomical landmarks to provide reliable and reproducible data. More specifically, the precision of marker placement among repeated measurements is responsible for increased variability in the output gait data.

RESEARCH QUESTION

The objective of this study was to quantify the precision of marker placement on the lower limbs by a test-retest procedure and to investigate its propagation to kinematic data.

METHODS

The protocol was tested on a cohort of eight asymptomatic adults involving four evaluators, with different levels of experience. Each evaluator performed, three repeated marker placements for each participant. The standard deviation was used to calculate the precision of the marker placement, the precision of the orientation of the anatomical (segment) coordinate systems, and the precision of the lower limb kinematics. In addition, one-way ANOVA was used to compare the intra-evaluator marker placement precision and kinematic precisions among the different levels of the evaluator's experience. Finally, a Pearson correlation between marker placement precision and kinematic precision was analyzed.

RESULTS

Results have shown a precision of skin markers within 10 mm and 12 mm for intra-evaluator and inter-evaluator, respectively. Analysis of kinematic data showed good to moderate reliability for all parameters apart from hip and knee rotation that demonstrated poor intra- and inter-evaluator precision. Inter-trial variability was observed reduced than intra- and inter-evaluator variability. Moreover, experience had a positive impact on kinematic reliability since evaluators with higher experience showed a statistically significant increase in precision for most kinematic parameters. However, no correlation was observed between marker placement precision and kinematic precision which indicates that an error in the placement of one specific marker can be compensated or enhanced, in a non-linear way, by an error in the placement of other markers.

Joint Coordinate System Using Functional Axes Achieves Clinically Meaningful Kinematics of the Tibiofemoral Joint as Compared to the International Society of Biomechanics Recommendation

Quantification of clinically meaningful tibiofemoral motions requires a joint coordinate system (JCS) with motions free from kinematic crosstalk errors. The objectives were to use a JCS with literature-backed functional axes (FUNC) and a JCS recommended by the International Society of Biomechanics (ISB) to determine tibiofemoral kinematics of the native (i.e., healthy) knee, determine variability associated with each JCS, and determine whether the FUNC JCS significantly reduced kinematic crosstalk errors compared to the ISB JCS. Based on a kinematic model consisting of a three-cylindric joint chain, the FUNC JCS included functional flexion-extension (F-E) and internal-external (I-E) tibial rotation axes. In contrast, the ISB JCS included F-E and I-E axes defined using anatomic landmarks. Single-plane fluoroscopic images in 13 subjects performing a weighted deep knee bend were analyzed. Tibiofemoral kinematics using the FUNC JCS fell within the physiological range of motion in all six degrees-of-freedom. Internal tibial rotation averaged 13 deg for the FUNC JCS versus 10 deg for the ISB JCS and motions in the other four degrees-of-freedom (collectively termed off-axis motions) were minimal as expected based on biomechanical constraints. Off-axis motions for the ISB JCS were significantly greater; maximum valgus rotation was 4 deg and maximum anterior and distraction translations were 9 mm and 25 mm, respectively, which is not physiologic. Variabilities in off-axis motions were significantly greater with the ISB JCS (p < 0.0002). The FUNC JCS achieved clinically meaningful kinematics by significantly reducing kinematic crosstalk errors and is the more suitable coordinate system for quantifying tibiofemoral motions.

An automatic extraction method on medical feature points based on PointNet++ for robot-assisted knee arthroplasty

BACKGROUND

Image registration is a crucial technology in robot-assisted knee arthroplasty, which provides real-time patient information by registering the pre-operative image data with data acquired during the operation. The existing registration method requires surgeons to manually pick up medical feature points (i.e. anatomical points) in pre-operative images, which is time-consuming and relied on surgeons experience. Moreover, different doctors have different preferences in preoperative planning, which may influence the consistency of surgical results.

METHODS

A medical feature points automatic extraction method based on PointNet++ named Point_RegNet is proposed to improve the efficiency of preoperative preparation and ensure the consistency of surgical results. The proposed method replaces the classification and segmentation layer of PointNet++ with a regression layer to predict the position of feature points. The comparative experiment is adopted to determine the optimal set of abstraction layers in PointNet++.

RESULTS

The proposed network with three set abstraction layers is more suitable for extracting feature points. The feature points predictions mean error of our method is less than 5 mm, which is 1 mm less than the manual marking method. Ultimately, our method only requires less than 3 s to extract all medical feature points in practical application. It is much faster than the manual extraction way which usually requires more than half an hour to mark all necessary feature points.

CONCLUSION

Our deep learning-based method can improve the surgery accuracy and reduce the preoperative preparation time. Moreover, this method can also be applied to other surgical navigation systems.

Towards Single Camera Human 3D-Kinematics

Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techniques work in a multi-step approach by first detecting the pose of the body and then fitting a musculoskeletal model to the data for accurate kinematic estimation. Errors in training data of the pose detection algorithms, model scaling, as well the requirement of multiple cameras limit the use of these techniques in a clinical setting. Our goal is to pave the way toward fast, easily applicable and accurate 3D kinematic estimation. To this end, we propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines in terms of joint angles error by a large margin (35% from 5.44 to 3.54 degrees). We show that D3KE is superior to the multi-step approach and can run at video framerate speeds. This technology shows the potential for clinical analysis from mobile devices in the future.

An analytical model to quantify the impact of the propagation of uncertainty in knee joint angle computation

Joint kinematics are typically described using Cardan angles or the attitude vector and its projection on the joint axes. Whichever the notation used, the uncertainties present in gait measurements affect the computed kinematics, especially for the knee joint. One notation - the attitude vector - enables the derivation of an analytical model of the propagation of uncertainty. Thus, the objective of this study was to derive this analytical model and assess the propagation of uncertainty in knee joint angle computation. Multi-session gait data acquired from one asymptomatic adult participant was used as reference data (experimental mean curve and standard deviations). Findings showed that an input uncertainty of 5° in the attitude vector and joint axes parameters matched experimental standard deviations. Taking each uncertainty independently, the cross-talk effect could result from uncertainty in the orientation of either the attitude vector (intrinsic variability) or the first joint axis (extrinsic variability). We concluded that the model successfully estimated the propagation of input uncertainties on joint angles and enabled an investigation of how that propagation occurred. The analytical model could be used to a priori estimate the standard deviations of experimental kinematics curves based on expected intrinsic and extrinsic uncertainties.

Reproducibility and repeatability of a semi-automated pipeline to quantify trapeziometacarpal joint angles using dynamic computed tomography

Background

The trapeziometacarpal (TMC) joint is a mechanically complex joint and is commonly affected by musculoskeletal diseases such as osteoarthritis. Quantifying in vivo TMC joint biomechanics, such as joint angles, with traditional reflective marker-based methods can be difficult due to the joint’s location in the hand. Dynamic computed tomography (CT) can facilitate the quantification of TMC joint motion by continuously capturing three-dimensional volumes over time. However, post-processing of dynamic CT datasets can be time intensive and automated methods are needed to reduce processing times to allow for application to larger clinical studies. The purpose of this work is to introduce a fast, semi-automated pipeline to quantify joint angles from dynamic CT scans of the TMC joint and evaluate the associated error in joint angle and translation computation by means of a reproducibility and repeatability study.

Methods

Ten cadaveric hands were scanned with dynamic CT using a passive motion device to move thumbs in a radial abduction–adduction motion. Static CT scans and high-resolution peripheral quantitative CT scans were also acquired to generate high-resolution bone meshes. Abduction–adduction, flexion–extension, and axial rotation angles were computed using a joint coordinate system. Reproducibility and repeatability were assessed using intraclass correlation coefficients, Bland–Altman analysis, and root mean square errors. Target registration errors were computed to evaluate errors associated with image registration.

Results

We found good repeatability for flexion–extension, abduction–adduction, and axial rotation angles. Reproducibility was moderate for all three angles. Joint translations exhibited greater repeatability than reproducibility. Specimens with greater joint degeneration had lower repeatability and reproducibility. We found that the difference in resulting joint angles and translations were likely due to differences in segment coordinate system definition between multiple raters, rather than due to registration errors.

Conclusions

The proposed semi-automatic processing pipeline was fast, repeatable, and moderately reproducible when quantifying TMC joint angles and translations. This work provides a range of errors for TMC joint angles from dynamic CT scans using manually selected anatomical landmarks.

Accuracy of a 3D temporal scanning system for gait analysis: Comparative with a marker-based photogrammetry system

BACKGROUND

Combining the accuracy of marker-based stereophotogrammetry and the usability and comfort of markerless human movement analysis is a difficult challenge. 3D temporal scanners are a promising solution, since they provide moving meshes with thousands of vertices that can be used to analyze human movements.

RESEARCH QUESTION

Can a 3D temporal scanner be used as a markerless system for gait analysis with the same accuracy as traditional, marker-based stereophotogrammetry systems?

METHODS

A comparative study was carried out using a 3D temporal scanner synchronized with a marker-based stereophotogrammetry system. Two gait cycles of twelve healthy adults were measured simultaneously, extracting the positions of key anatomical points from both systems, and using them to analyze the 3D kinematics of the pelvis, right hip and knee joints. Measurement differences of marker positions and joint angles were described by their root mean square. A t-test was performed to rule out instrumental errors, and an F-test to evaluate the amplifications of marker position errors in dynamic conditions.

RESULTS

The differences in 3D landmark positions were between 1.9 and 2.4 mm in the reference pose. Marker position errors were significantly increased during motion in the medial-lateral and vertical directions. The angle relative errors were between 3% and 43% of the range of motion, with the greatest difference being observed in hip axial rotation.

SIGNIFICANCE

The differences in the results obtained between the 3D temporal scanner and the marker-based system were smaller than the usual errors due to lack of accuracy in the manual positioning of markers on anatomical landmarks and to soft-tissue artefacts. That level of accuracy is greater than other markerless systems, and proves that such technology is a good alternative to traditional, marker-based motion capture.

The Conventional Gait Model’s sensitivity to lower-limb marker placement

Clinical gait analysis supports treatment decisions for patients with motor disorders. Measurement reproducibility is affected by extrinsic errors such as marker misplacement—considered the main factor in gait analysis variability. However, how marker placement affects output kinematics is not completely understood. The present study aimed to evaluate the Conventional Gait Model’s sensitivity to marker placement. Using a dataset of kinematics for 20 children, eight lower-limb markers were virtually displaced by 10 mm in all four planes, and all the displacement combinations were recalculated. Root-mean-square deviation angles were calculated for each simulation with respect to the original kinematics. The marker movements with the greatest impact were for the femoral and tibial wands together with the lateral femoral epicondyle marker when displaced in the anterior–posterior axis. When displaced alone, the femoral wand was responsible for a deviation of 7.3° (± 1.8°) in hip rotation. Transversal plane measurements were affected most, with around 40% of simulations resulting in an effect greater than the acceptable limit of 5°. This study also provided insight into which markers need to be placed very carefully to obtain more reliable gait data.

Statistical Quantification of the Effects of Marker Misplacement and Soft-Tissue Artifact on Shoulder Kinematics and Kinetics

The assessment of shoulder kinematics and kinetics are commonly undertaken biomechanically and clinically by using rigid-body models and experimental skin-marker trajectories. However, the accuracy of these trajectories is plagued by inherent skin-based marker errors due to marker misplacements (offset) and soft-tissue artifacts (STA). This paper aimed to assess the individual contribution of each of these errors to kinematic and kinetic shoulder outcomes computed using a shoulder rigid-body model. Baseline experimental data of three shoulder planar motions in a young healthy adult were collected. The baseline marker trajectories were then perturbed by simulating typically observed population-based offset and/or STA using a probabilistic Monte-Carlo approach. The perturbed trajectories were then used together with a shoulder rigid-body model to compute shoulder angles and moments and study their accuracy and variability against baseline. Each type of error was studied individually, as well as in combination. On average, shoulder kinematics varied by 3%, 6% and 7% due to offset, STA or combined errors, respectively. Shoulder kinetics varied by 11%, 27% and 28% due to offset, STA or combined errors, respectively. In conclusion, to reduce shoulder kinematic and kinetic errors, one should prioritise reducing STA as they have the largest error contribution compared to marker misplacements.

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy

Two-dimensional deep-learning pose estimation algorithms can suffer from biases in joint pose localizations, which are reflected in triangulated coordinates, and then in 3D joint angle estimation. Pose2Sim, our robust markerless kinematics workflow, comes with a physically consistent OpenSim skeletal model, meant to mitigate these errors. Its accuracy was concurrently validated against a reference marker-based method. Lower-limb joint angles were estimated over three tasks (walking, running, and cycling) performed multiple times by one participant. When averaged over all joint angles, the coefficient of multiple correlation (CMC) remained above 0.9 in the sagittal plane, except for the hip in running, which suffered from a systematic 15° offset (CMC = 0.65), and for the ankle in cycling, which was partially occluded (CMC = 0.75). When averaged over all joint angles and all degrees of freedom, mean errors were 3.0°, 4.1°, and 4.0°, in walking, running, and cycling, respectively; and range of motion errors were 2.7°, 2.3°, and 4.3°, respectively. Given the magnitude of error traditionally reported in joint angles computed from a marker-based optoelectronic system, Pose2Sim is deemed accurate enough for the analysis of lower-body kinematics in walking, cycling, and running.

Using Bayesian inference to estimate plausible muscle forces in musculoskeletal models

Background

Musculoskeletal modeling is currently a preferred method for estimating the muscle forces that underlie observed movements. However, these estimates are sensitive to a variety of assumptions and uncertainties, which creates difficulty when trying to interpret the muscle forces from musculoskeletal simulations. Here, we describe an approach that uses Bayesian inference to identify plausible ranges of muscle forces for a simple motion while representing uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem.

Methods

We generated a reference elbow flexion–extension motion and computed a set of reference forces that would produce the motion while minimizing muscle excitations cubed via OpenSim Moco. We then used a Markov Chain Monte Carlo (MCMC) algorithm to sample from a posterior probability distribution of muscle excitations that would result in the reference elbow motion. We constructed a prior over the excitation parameters which down-weighted regions of the parameter space with greater muscle excitations. We used muscle excitations to find the corresponding kinematics using OpenSim, where the error in position and velocity trajectories (likelihood function) was combined with the sum of the cubed muscle excitations integrated over time (prior function) to compute the posterior probability density.

Results

We evaluated the muscle forces that resulted from the set of excitations that were visited in the MCMC chain (seven parallel chains, 500,000 iterations per chain). The estimated muscle forces compared favorably with the reference forces generated with OpenSim Moco, while the elbow angle and velocity from MCMC matched closely with the reference (average RMSE for elbow angle = 2°; and angular velocity = 32°/s). However, our rank plot analyses and potential scale reduction statistics, which we used to evaluate convergence of the algorithm, indicated that the chains did not fully mix.

Conclusions

While the results from this process are a promising step towards characterizing uncertainty in muscle force estimation, the computational time required to search the solution space with, and the lack of MCMC convergence indicates that further developments in MCMC algorithms are necessary for this process to become feasible for larger-scale models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01008-4.

Reliability and Validity of an Inertial Measurement System to Quantify Lower Extremity Joint Angle in Functional Movements

The purpose of this research was to determine if the commercially available Perception Neuron motion capture system was valid and reliable in clinically relevant lower limb functional tasks. Twenty healthy participants performed two sessions on different days: gait, squat, single-leg squat, side lunge, forward lunge, and counter-movement jump. Seven IMUs and an OptiTrack system were used to record the three-dimensional joint kinematics of the lower extremity. To evaluate the performance, the multiple correlation coefficient (CMC) and the root mean square error (RMSE) of the waveforms as well as the difference and intraclass correlation coefficient (ICC) of discrete parameters were calculated. In all tasks, the CMC revealed fair to excellent waveform similarity (0.47–0.99) and the RMSE was between 3.57° and 13.14°. The difference between discrete parameters was lower than 14.54°. The repeatability analysis of waveforms showed that the CMC was between 0.54 and 0.95 and the RMSE was less than 5° in the frontal and transverse planes. The ICC of all joint angles in the IMU was general to excellent (0.57–1). Our findings showed that the IMU system might be utilized to evaluate lower extremity 3D joint kinematics in functional motions.

A statistical shape model of the tibia-fibula complex: sexual dimorphism and effects of age on reconstruction accuracy from anatomical landmarks

A statistical shape model was created for a young adult population and used to predict tibia and fibula geometries from bony landmarks. Reconstruction errors with respect to CT data were quantified and compared to isometric scaling. Shape differences existed between sexes. The statistical shape model estimated tibia-fibula geometries from landmarks with high accuracy (RMSE = 1.51-1.62 mm), improving upon isometric scaling (RMSE = 1.78 mm). Reconstruction errors increased when the model was applied to older adults (RMSE = 2.11-2.17 mm). Improvements in geometric accuracy with shape model reconstruction changed hamstring moment arms 25-35% (1.0-1.3 mm) in young adults.

Prediction of the Risk Factors of Knee Injury During Drop-Jump Landing With Core-related Measurements in Amature Basketball Players

Purpose: To evaluate the relationship between specific aspects of core stability and knee injury risk factors during drop-jump (DJ) landing.

Methods: Eighteen college-aged male amateur basketball players participated in the project. Kinetic and kinematic data for DJ tasks were collected with force plates and infrared cameras. Raw data were processed to calculate knee joint angles and joint moments during DJ landing. Different components of core stability were represented by the sit-ups in 20 s (SU), trunk extensor endurance, trunk flexion and extension range of motion, dominant extremity single-leg stance time (DLS), and dominant extremity single-leg hop distance, respectively.

Methods: Correlation and regression were used to determine the relationship between jumping-related biomechanical parameters and core stability components.

Results: SU shared significant variance with the peak moment of knee extension (PMKE, p < 0.05), the peak moment of knee abduction (PMKA, p < 0.05), and the angle of knee internal rotation at initial contact (AKRI, p < 0.05). DLS shared significant variance with the angular motion of knee internal rotation (AMKR, p < 0.05) and the AKRI (p < 0.01). SU and DLS together could explain 52% of the variance observed in the AKRI, and the result was significant.

Conclusion: Core stability’s strength and motor control aspects played an essential role in preventing knee injury during DJ landing. An integrative training program addressing core strength and motor control could be considered for coaches and athletes to prevent knee injury through core training and conditioning.

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics-Part 1: Robustness

Being able to capture relevant information about elite athletes' movement "in the wild" is challenging, especially because reference marker-based approaches hinder natural movement and are highly sensitive to environmental conditions. We propose Pose2Sim, a markerless kinematics workflow that uses OpenPose 2D pose detections from multiple views as inputs, identifies the person of interest, robustly triangulates joint coordinates from calibrated cameras, and feeds those to a 3D inverse kinematic full-body OpenSim model in order to compute biomechanically congruent joint angles. We assessed the robustness of this workflow when facing simulated challenging conditions: (Im) degrades image quality (11-pixel Gaussian blur and 0.5 gamma compression); (4c) uses few cameras (4 vs. 8); and (Cal) introduces calibration errors (1 cm vs. perfect calibration). Three physical activities were investigated: walking, running, and cycling. When averaged over all joint angles, stride-to-stride standard deviations lay between 1.7° and 3.2° for all conditions and tasks, and mean absolute errors (compared to the reference condition-Ref) ranged between 0.35° and 1.6°. For walking, errors in the sagittal plane were: 1.5°, 0.90°, 0.19° for (Im), (4c), and (Cal), respectively. In conclusion, Pose2Sim provides a simple and robust markerless kinematics analysis from a network of calibrated cameras.

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

Kinematic analysis is a useful and widespread tool used in research and clinical biomechanics for the quantification of human movement. Common marker-based optical motion capture systems are time intensive and require highly trained operators to obtain kinematic data. Markerless motion capture systems offer an alternative method for the measurement of kinematic data with several practical benefits. This work compared the kinematics of human gait measured using a deep learning algorithm-based markerless motion capture system to those from a standard marker-based motion capture system. Thirty healthy adult participants walked on a treadmill while data were simultaneously recorded using eight video cameras and seven infrared optical motion capture cameras, providing synchronized markerless and marker-based data for comparison. The average root mean square distance (RMSD) between corresponding joint centers was less than 2.5 cm for all joints except the hip, which was 3.6 cm. Lower limb segment angles relative to the global coordinate system indicated the global segment pose estimates from both systems were very similar, with RMSD of less than 5.5° for all segment angles except those that represent rotations about the long axis of the segment. Lower limb joint angles captured similar patterns for flexion/extension at all joints, ab/adduction at the knee and hip, and toe-in/toe-out at the ankle. These findings indicate that the markerless system would be a suitable alternative technology in cases where the practical benefits of markerless data collection are preferred.

Estimating soft tissue artifact of the thigh in high knee flexion tasks using optical motion Capture: Implications for marker cluster placement

Soft tissue artifact in motion capture is widely accepted as a significant source of error in kinematic and kinetic measurements. Non-invasive methods of estimating soft tissue artifact, those requiring only motion capture, provide a feasible method to evaluate marker placement on a segment and enable recommendations for marker configurations which can minimize soft tissue artifact. The purpose of this study was to investigate the effect of thigh marker cluster location on soft tissue artifact during high knee flexion (>120 deg) as unique deformation of soft tissue occurs in this range (e.g. thigh-calf contact). Motion of the pelvis and lower limbs were recorded during squatting and kneeling in fifty participants. Six rigid marker clusters were affixed to the skin on the anterior, lateral, and anterolateral aspect, at the distal and middle third of the thighs. To estimate soft tissue artifact, the functional hip joint center was reconstructed relative to the pelvis cluster and each of the six thigh clusters throughout motion. The difference in the position of these two points was input into Bland-Altman analyses and compared between the thigh clusters. Across the tasks, the total mean difference ranged from 2.81 to 8.95 cm while the lower and upper limits of agreement ranged from -0.79 to 2.54 cm and 5.04 to 17.65 cm, respectively. Using this non-invasive method, the mid-anterolateral cluster was least susceptible to soft tissue artifact and thus would be recommended, while the lateral clusters were most susceptible and should avoided in high knee flexion and similar tasks.

The Influence of Kinematic Constraints on Model Performance During Inverse Kinematics Analysis of the Thoracolumbar Spine

Motion analysis is increasingly applied to spine musculoskeletal models using kinematic constraints to estimate individual intervertebral joint movements, which cannot be directly measured from the skin surface markers. Traditionally, kinematic constraints have allowed a single spinal degree of freedom (DOF) in each direction, and there has been little examination of how different kinematic constraints affect evaluations of spine motion. Thus, the objective of this study was to evaluate the performance of different kinematic constraints for inverse kinematics analysis. We collected motion analysis marker data in seven healthy participants (4F, 3M, aged 27–67) during flexion–extension, lateral bending, and axial rotation tasks. Inverse kinematics analyses were performed on subject-specific models with 17 thoracolumbar joints allowing 51 rotational DOF (51DOF) and corresponding models including seven sets of kinematic constraints that limited spine motion from 3 to 9DOF. Outcomes included: (1) root mean square (RMS) error of spine markers (measured vs. model); (2) lag-one autocorrelation coefficients to assess smoothness of angular motions; (3) maximum range of motion (ROM) of intervertebral joints in three directions of motion (FE, LB, AR) to assess whether they are physiologically reasonable; and (4) segmental spine angles in static ROM trials. We found that RMS error of spine markers was higher with constraints than without (p < 0.0001) but did not notably improve kinematic constraints above 6DOF. Compared to segmental angles calculated directly from spine markers, models with kinematic constraints had moderate to good intraclass correlation coefficients (ICCs) for flexion–extension and lateral bending, though weak to moderate ICCs for axial rotation. Adding more DOF to kinematic constraints did not improve performance in matching segmental angles. Kinematic constraints with 4–6DOF produced similar levels of smoothness across all tasks and generally improved smoothness compared to 9DOF or unconstrained (51DOF) models. Our results also revealed that the maximum joint ROMs predicted using 4–6DOF constraints were largely within physiologically acceptable ranges throughout the spine and in all directions of motions. We conclude that a kinematic constraint with 5DOF can produce smooth spine motions with physiologically reasonable joint ROMs and relatively low marker error.

Between-session reliability of skin marker-derived spinal kinematics during functional activities

BACKGROUND

Skin marker-based analysis of functional spinal movement is a promising method for quantifying longitudinal effects of treatment interventions in patients with spinal pathologies. However, observed day-to-day changes might not necessarily be due to a treatment intervention, but can result from errors related to soft tissue artifacts, marker placement inaccuracies or biological day-to-day variability.

RESEARCH QUESTION

How reliable are skin marker-derived three-dimensional spinal kinematics during functional activities between two separate measurement sessions?

METHODS

Twenty healthy adults (11 females/9 males) were invited to a movement analysis laboratory for two visits separated by 7-10 days. At each visit, they performed various functional activities (i.e. sitting, standing, walking, running, chair rising, box lifting and vertical jumping), while marker trajectories were recorded using a skin marker-based 10-camera optical motion capture system and used to calculate sagittal and frontal plane spinal curvature angles as well as transverse plane segmental rotational angles in the lumbar and thoracic regions. Between-session reliability for continuous data and discrete parameters was determined by analyzing systematic errors using one sample T-tests as well as by calculating intraclass correlation coefficients (ICCs) and minimal detectable changes (MDCs).

RESULTS AND SIGNIFICANCE

The analysis indicated high relative consistency for sagittal plane curvature angles during all activities, but not for frontal and transverse plane angles during walking and running. MDCs were mostly below 15°, with relative values ranging between 10 % and 750 %. This study provides important information that can serve as a basis for researchers and clinicians aiming at investigating longitudinal effects of treatment interventions on spinal motion behavior in patients with spinal pathologies.

The effect of simulated marker misplacement on the interpretation of inter-limb differences during a change of direction task

The objective assessment of biomechanical asymmetries during movement tasks is used to monitor rehabilitation following anterior cruciate ligament reconstruction (ACLR). Marker placement is an important source of methodological variability within human motion analysis. It is currently unclear how marker placement error effects the interpretation of biomechanical asymmetries throughout post ACLR rehabilitation. The aim of this investigation was to determine the effect of random marker placement variation on the interpretation of inter-limb differences during a change of direction (CoD) task. Forty-seven participants 9 months post-ACLR and fifty uninjured controls completed a 90° CoD task on both limbs. Inter-limb differences in kinematic and kinetic metrics during the CoD stance phase were calculated for both groups using the Vicon Plug-in Gait model, and ACLR subjects were classified as having 'normal' or 'abnormal' inter-limb differences relative to the control group. Simulated random marker displacements based on published marker placement error ranges were then repeatedly applied to the lateral thigh, femoral epicondyle and tibia markers. ACLR inter-limb differences were recalculated each time, allowing the estimation of 95% confidence intervals and minimal identifiable between-session changes. ACLR subjects were also reclassified relative to the control group after each simulation and the percentage of participants to change classification was calculated. Marker displacements caused large deviations in inter-limb difference measures in several variables including hip rotation angle, knee abduction angle and knee abduction moment, thus limiting the ability to identify participants with large inter-limb differences relative to a control group. These findings highlight the challenges in using marker-based biomechanical models to conduct objective assessments of inter-limb differences during CoD tasks.

Fear of movement is not associated with trunk movement variability during gait in patients with low back pain

BACKGROUND

Literature describing differences in motor control between low back pain (LBP) patients and healthy controls is very inconsistent, which may be an indication for the existence of subgroups. Pain-related psychological factors might play a role causing these differences.

PURPOSE

To examine the relation between fear of movement and variability of kinematics and muscle activation during gait in LBP patients.

STUDY DESIGN

Cross-sectional experimental design.

PATIENT SAMPLE

Thirty-one Chinese LBP patients.

OUTCOME MEASURES

Self-report measures: Visual Analog Score for pain; TAMPA-score; Physiologic measures: electromyography, range of motion.

FUNCTIONAL MEASURES

LBP history; the physical load of profession, physical activity.

METHODS

Patients were divided in high and low fear of movement groups. Participants walked on a treadmill at four speeds: very slow, slow, preferred and fast. Kinematics of the thorax and the pelvis were recorded, together with the electromyography of five bilateral trunk muscle pairs. Kinematic and electromyography data were analysed in terms of stride-to-stride pattern variability. Factor analysis was applied to assess interdependence of 11 variability measures. To test for differences between groups, a mixed-design multivariate analysis of variance was conducted.

RESULTS

Kinematic variability and variability of muscle activation consistently loaded on different factors and thus represented different underlying variables. No significant Group effects on variability of kinematics and muscle activation were found (Hotelling's Trace F=0.237; 0.396, p=.959; .846, respectively). Speed significantly decreased kinematic variability and increased variability in muscle activation (Hotelling's Trace F=8.363; 4.595, p<.0001; <.0001, respectively). No significant interactions between Group and Speed were found (Hotelling's Trace F=0.204; 0.100, p=.762; .963, respectively).

CONCLUSIONS

The results of this study do not support the hypothesis that variability in trunk kinematics and trunk muscle activation during gait in LBP patients are associated with fear of movement.

Between-session reliability of subject-specific musculoskeletal models of the spine derived from optoelectronic motion capture data

This study evaluated the between-session reliability of creating subject-specific musculoskeletal models with optoelectronic motion capture data, and using them to estimate spine loading. Nineteen healthy participants aged 24-74 years underwent the same set of measurements on two separate occasions. Retroreflective markers were placed on anatomical regions, including C7, T1, T4, T5, T8, T9, T12 and L1 spinous processes, pelvis, upper and lower limbs, and head. We created full-body musculoskeletal models with detailed thoracolumbar spines, and scaled these to create subject-specific models for each individual and each session. Models were scaled from distances between markers, and spine curvature was adjusted according to marker-estimated measurements. Using these models, we estimated vertebral compressive loading for five different standardized postures: neutral standing, 45˚ trunk flexion, 15˚ trunk extension, 20˚ lateral bend to the right, and 45˚ axial rotation to the right. Intraclass correlation coefficients (ICCs) and standard error of measurement were calculated as measures of between-session reliability and measurement error, respectively. Spine curvature measures showed excellent reliability (ICC = 0.79-0.91) and body scaling segments showed fair to excellent reliability (ICC = 0.46-0.95). We found that musculoskeletal models showed mostly excellent between-session reliability to estimate spine loading, with 91% of ICC values > 0.75 for all activities. This information is a necessary precursor for using motion capture data to estimate spine loading from subject-specific musculoskeletal models, and suggests that marker data will deliver reproducible subject-specific models and estimates of spine loading.

Biomechanical measures of short-term maximal cycling on an ergometer: a test-retest study

An understanding of test-retest reliability is important for biomechanists, such as when assessing the longitudinal effect of training or equipment interventions. Our aim was to quantify the test-retest reliability of biomechanical variables measured during short-term maximal cycling. Fourteen track sprint cyclists performed 3 × 4 s seated sprints at 135 rpm on an isokinetic ergometer, repeating the session 7.6 ± 2.5 days later. Joint moments were calculated via inverse dynamics, using pedal forces and limb kinematics. EMG activity was measured for 9 lower limb muscles. Reliability was explored by quantifying systematic and random differences within- and between-session. Within-session reliability was better than between-sessions reliability. The test-retest reliability level was typically moderate to excellent for the biomechanical variables that describe maximal cycling. However, some variables, such as peak knee flexion moment and maximum hip joint power, demonstrated lower reliability, indicating that care needs to be taken when using these variables to evaluate biomechanical changes. Although measurement error (instrumentation error, anatomical marker misplacement, soft tissue artefacts) can explain some of our reliability observations, we speculate that biological variability may also be a contributor to the lower repeatability observed in several variables including ineffective crank force, ankle kinematics and hamstring muscles' activation patterns.

Impact of knee marker misplacement on gait kinematics of children with cerebral palsy using the Conventional Gait Model-A sensitivity study

Clinical gait analysis is widely used in clinical routine to assess the function of patients with motor disorders. The proper assessment of the patient’s function relies greatly on the repeatability between the measurements. Marker misplacement has been reported as the largest source of variability between measurements and its impact on kinematics is not fully understood. Thus, the purpose of this study was: 1) to evaluate the impact of the misplacement of the lateral femoral epicondyle marker on lower limb kinematics, and 2) evaluate if such impact can be predicted. The kinematic data of 10 children with cerebral palsy and 10 aged-match typical developing children were included. The lateral femoral epicondyle marker was virtually misplaced around its measured position at different magnitudes and directions. The outcome to represent the impact of each marker misplacement on the lower limb was the root mean square deviations between the resultant kinematics from each simulated misplacement and the originally calculated kinematics. Correlation and regression equations were estimated between the root mean square deviation and the magnitude of the misplacement expressed in percentage of leg length. Results indicated that the lower-limb kinematics is highly sensitive to the lateral femoral epicondyle marker misplacement in the anterior-posterior direction. The joint angles most impacted by the anterior-posterior misplacement were the hip internal-external rotation (5.3° per 10 mm), the ankle internal-external rotation (4.4° per 10 mm) and the knee flexion-extension (4.2° per 10 mm). Finally, it was observed that the lower the leg length, the higher the impact of misplacement on kinematics. This impact was predicted by regression equations using the magnitude of misplacement expressed in percentage of leg length. An error below 5° on all joints requires a marker placement repeatability under 1.2% of the leg length. In conclusion, the placement of the lateral femoral epicondyle marker in the antero-posterior direction plays a crucial role on the reliability of gait measurements with the Conventional Gait Model.

The sensitivity of joint kinematics and kinetics to marker placement during a change of direction task

The conventional gait model (CGM) refers to several closely related biomechanical models used in the objective analysis of human motion. Their use has become popular in the analysis of change of direction tasks to inform best practice in the prevention and rehabilitation of anterior cruciate ligament injury. As externally-placed markers define segment axes origins and orientations, kinematic and kinetic outputs from the CGM are sensitive to marker placement. The aim of this investigation was to quantify the sensitivity of lower extremity kinematics and knee moments to systematic differences in marker placement across the stance phase of a change of direction task. Systematic anterior/posterior displacements were applied to the lateral thigh, femoral epicondyle and tibia markers in software. One-dimensional statistical parametric mapping was used to determine the effect of marker placement across the entire stance phase of a 90° change of direction task. Marker placement error within previously reported inter-tester variability ranges caused significant differences in knee abduction moment, hip rotation angle, knee rotation angle, ankle abduction and rotation angle across various periods of stance. Discrete measures of these variables have been associated with increased frontal plane knee loading during change of direction, considered a key mechanism of anterior cruciate ligament injury. Systematic differences in marker placement may lead to incorrect group statistical inferences in such discrete measures.

Human kinematic, kinetic and EMG data during different walking and stair ascending and descending tasks

This paper reports the kinematic, kinetic and electromyographic (EMG) dataset of human locomotion during level walking at different velocities, toe- and heel-walking, stairs ascending and descending. A sample of 50 healthy subjects, with an age between 6 and 72 years, is included. For each task, both raw data and computed variables are reported including: the 3D coordinates of external markers, the joint angles of lower limb in the sagittal, transversal and horizontal anatomical planes, the ground reaction forces and torques, the center of pressure, the lower limb joint mechanical moments and power, the displacement of the whole body center of mass, and the surface EMG signals of the main lower limb muscles. The data reported in the present study, acquired from subjects with different ages, represents a valuable dataset useful for future studies on locomotor function in humans, particularly as normative reference to analyze pathological gait, to test the performance of simulation models of bipedal locomotion, and to develop control algorithms for bipedal robots or active lower limb exoskeletons for rehabilitation.

ISB recommendations on the reporting of intersegmental forces and moments during human motion analysis

The International Society of Biomechanics (ISB) has charged this committee with development of a standard similar in scope to the kinematic standard proposed in Wu et al. (2002) and Wu et al. (2005). Given the variety of purposes for which intersegmental forces and moments are used in biomechanical research, it is not possible to recommend a particular set of analysis standards that will be acceptable in all applications. Instead, it is the purpose of this paper to recommend a set of reporting standards that will result in an understanding of the differences between investigations and the ability to reproduce the research. The end products of this standard are (1) a critical checklist that can be used during submission of manuscripts and abstracts to insure adequate description of methods, and (2) a web based visualization tool that can be used to alter the coordinate system, normalization technique and internal/external perspective of intersegmental forces and moments during walking and running so that the shape and magnitude of the curves can be compared to one's own data.

Measurement and Analysis of Gait Pattern during Stair Walk for Improvement of Robotic Locomotion Rehabilitation System

Background

Robotic locomotion rehabilitation systems have been used for gait training in patients who have had a stroke. Most commercialized systems allow patients to perform simple exercises such as balancing or level walking, but an additional function such as stair-walk training is required to provide a wide range of recovery cycle rehabilitation. In this study, we analyzed stair-gait patterns and applied the result to a robotic rehabilitation system that can provide a vertical motion of footplates.

Methods

To obtain applicable data for the robotic system with vertically movable footplates, stair-walk action was measured using an optical marker-based motion capture system. The spatial position data of joints during stair walking was obtained from six healthy adults who participated in the experiment. The measured marker data were converted into joint kinematic data by using an algorithm that included resampling and normalization. The spatial position data are represented as angular trajectories and the relative displacement of each joint on the anatomical sagittal plane and movements of hip joints on the anatomical transverse plane.

Results

The average range of motion (ROM) of each joint was estimated as (−6.75°, 48.69°) at the hip, (8.20°, 93.78°) at the knee, and (−17.78°, 11.75°) at the ankle during ascent and as (6.41°, 31.67°) at the hip, (7.38°, 91.93°) at the knee, and (−24.89°, 24.18°) at the ankle during descent. Additionally, we attempted to create a more natural stair-gait pattern by analyzing the movement of the hip on the anatomical transverse plane. The hip movements were estimated to within ±1.57 cm and ±2.00 cm for hip translation and to within ±2.52° and ±2.70° for hip rotation during stair ascent and stair descent, respectively.

Conclusions

Based on the results, standard patterns of stair ascent and stair descent were derived and applied to a lower-limb rehabilitation robot with vertically movable footplates. The relative trajectory from the experiment ascertained that the function of stair walking in the robotic system properly worked within a normal ROM.

Variability, agreement and reliability of MRI knee landmarks

Surface mesh reconstructions of bones are often required to define landmark-based coordinate systems, regions of interest and morphological features when studying the soft tissues of the knee from MRI scans. This study reports the variability, agreement and reliability of osseous landmarks to better understand their downstream effects. Fifteen landmarks were defined on the distal femur and twelve on the proximal tibia. Surface meshes were created from twenty right knee MRI scans with a mean subject age of 30.9 years. A single observer identified landmarks on all twenty knees, while three observers repeated the observations three times on a subset of eight knees. All observations were aligned to the Procrustes mean shapes. Principal component analysis was used to study inter-subject variability and two-way ANOVA for inter- and intra-observer agreement and reliability. Inter-subject landmark variation ranged from 0.6 to 5.26 mm, while inter- and intra-observer agreement were at most 5.1 and 5.69 mm respectively. Between-observer reliability ranged from 0.07 to 0.98 while within-observer values were between 0.51 and 0.98. Landmarks derived from fitted spheres or circles often performed well, while most others had their poorest agreement or greatest variation limited to only one or two cardinal directions.

Joint coordinate system for biomechanical analysis of the sacroiliac joint

Sacroiliac joint (SIJ) biomechanics have been described in both in vitro and in vivo studies. A standard for joint coordinate systems has been created by the International Society of Biomechanics for most of the joints in the human body. However, a standardized joint coordinate system for sacroiliac joint motion analysis is currently still lacking. This impedes the comparison across studies and hinders communication among scientists and clinicians. As SIJ motion is reported to be quite limited, a proper standardization and reproducibility of this procedure is essential for the interpretation of future biomechanical SIJ studies. This paper proposes a joint coordinate system for the analysis of sacroiliac joint motion, based on the procedure developed by Grood and Suntay, using semi-automated anatomical landmarks on 3D joint surfaces. This coordinate system offers high inter-rater reliability and aspires to a more intuitive representation of biomechanical data, as it is aligned with SIJ articular surfaces. This study aims to encourage further reflection and debate on biomechanical data representation, in order to facilitate interpretation of SIJ biomechanics and improve communication between researchers and clinicians. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Validity and Reliability of Standing Posture Measurements Using a Mobile Application

OBJECTIVE

The purpose of this study was to evaluate the validity and reliability of standing posture assessments in asymptomatic men using the PostureScreen Mobile (PSM) iOS application.

METHODS

The standing posture of 50 asymptomatic male participants (24.04 ± 1.81 years) was measured during 3 trials on the same day. The following 10 measurements using the PSM app were compared to the criterion VICON 3-dimensional analysis: from the frontal plane, shift and tilt of the head, shoulders, and hips; and from the sagittal plane, shift of the head, shoulders, hips, and knees. We used Bayesian methods to analyze the data.

RESULTS

Compared with the VICON measurements, PSM assessments of head tilt, shoulder tilt and shift, and hip tilt and shift in the frontal plane were biased. In the sagittal plane, PSM measurements of shoulder, hip, and knee shift were biased. Only head shift in the frontal and sagittal planes were comparable between the VICON and the PSM. The VICON and PSM had similar intraclass correlations in 6 of 10 measurements. The PSM assessments of head shift and tilt and shoulder tilt in the sagittal plane were significantly less reliable than with VICON.

CONCLUSION

The use of the PSM app introduced significant bias in postural measurements in the frontal and sagittal plane. Until further research reports additional validity and reliability data of the PSM app, we suggest caution in the use of PSM app when highly accurate postural assessments are necessary.

The LAMB gait analysis protocol: Definition and experimental assessment of operator-related variability

Gait analysis has demonstrated to efficaciously support clinical investigations. The patterns of the outcome variables (joint angles, moments and powers) are characterized by an intrinsic and extrinsic variability. Particularly, extrinsic variability is induced by operator-dependent differences in markers' placement, with errors propagating non-linearly to alter outcome variable patterns. The aims of this study are (1) to consider a specific gait analysis protocol named LAMB and provide a description of its procedures, (2) to experimentally assess the between-operator and within-operator variability induced by operator-dependent marking of required anatomical landmarks and (3) to evidence how such inaccuracies propagates to the gait analysis kinematic and kinetic outcome variables. Six expert gait analysis operators performed LAMB anatomical landmarks marking on three healthy adult participants; moreover, one operator repeated three times the marking on one participant. The participants then performed a set of locomotor tasks including stair negotiation and heel- and toe-walking. An anatomical calibration approach let to register each marking and to compute, starting from one single raw data set, a set of outcome variables for each marking/operator. The between-operator variability of gait analysis outcome was assessed in terms of mean absolute variability to quantify offsets and minimal correlation coefficient to quantify patterns' similarity. The results evidence average minimal correlation coefficient ranging from 0.857 for moments to 0.907 for angles and average mean absolute variability accounted for few degrees in angular variables (worst between-operator mean absolute variability is 7.3°), while dynamic variables mean absolute variability, relative to the variable range, was below 5% for moment and below 10% for powers. The variability indexes are comparable to those related to previously published protocols and are independent from the considered task, thus suggesting that the LAMB is a reliable protocol suitable for the analysis of different locomotor tasks.

Functional estimation of bony segment lengths using magneto-inertial sensing: Application to the humerus

Inertial sensor technology has assumed an increasingly important role in the field of human motion analysis. However, the reliability of the kinematic estimates could still be critical for specific applications in the field of functional evaluation and motor rehabilitation. Within this context, the definition of subject-specific multi-body kinematic models is crucial since it affects the accuracy and repeatability of movement reconstruction. A key step for kinematic model calibration is the determination of bony segment lengths. This study proposes a functional approach for the in vivo estimation of the humerus length using a single magneto-inertial measurement unit (MIMU) positioned on the right distal posterior forearm. The humerus length was estimated as the distance between the shoulder elevation axis and the elbow flexion-extension axis. The calibration exercise involved five shoulder elevations in the sagittal plane with the elbow completely extended and five elbow flexion-extensions with the upper arm rigidly aligned to the trunk. Validation of the method was conducted on five healthy subjects using the humerus length computed from magnetic resonance imaging as the gold standard. The method showed mean absolute errors of 12 ± 9 mm, which were in the estimate of the humerus length. When using magneto-inertial technology, the proposed functional method represents a promising alternative to the regressive methods or manual measurements for performing kinematic model calibrations. Although the proposed methodology was validated for the estimation of the humerus length, the same approach can be potentially extended to other body segments.

Healthy 3D knee kinematics during gait: Differences between women and men, and correlation with x-ray alignment

BACKGROUND

Normal 3D knee kinematics during gait is still not well understood, especially regarding differences between women and men.

RESEARCH QUESTION

The objective of the present study was to characterize 3D knee kinematics during gait in healthy women and men with a validated tool.

METHODS

Knee kinematics was analysed with the KneeKG™ system in 90 healthy subjects (49 females and 41 males). 3D knee rotations were compared between women and men, and between right and left knees. Each subject underwent full-length weight-bearing x-rays. Correlations between abduction-adduction angles and lower-limb alignment measures on x-rays were assessed.

RESULTS

In the frontal plane, 2.0-5.0° more abduction occurred in women compared to men (0.000 ≤ p ≤ 0.015) throughout the entire gait cycle. In the transverse plane, 2.4-3.7° more external tibial rotation was seen in women than in men (0.002 ≤ p ≤ 0.041) during the initial and mid-swing phases. No difference was found between the right and left knees. Low correlations (-0.52 ≤ r≤-0.41, p < 0.001) were observed between radiographic hip-knee-ankle angle (HKA) and abduction-adduction angles throughout the stance phase.

SIGNIFICANCE

Kinematic differences between women and men in the frontal plane can be partly explained by their anatomical differences: women were less in varus than men (HKA of -0.8° vs. -2.6°, p < 0.001). Our study contributes to a better understanding of healthy 3D knee kinematics during gait and highlights the need for accounting of gender differences in future investigations. Better knowledge of natural knee kinematics will be helpful in assessing pathological gait patterns or determining the efficiency of conservative and surgical treatments to restore normal kinematics.

Runners with patellofemoral pain demonstrate sub-groups of pelvic acceleration profiles using hierarchical cluster analysis: an exploratory cross-sectional study

Background

Previous studies have suggested that distinct and homogenous sub-groups of gait patterns exist among runners with patellofemoral pain (PFP), based on gait analysis. However, acquisition of 3D kinematic data using optical systems is time consuming and prone to marker placement errors. In contrast, axial segment acceleration data can represent an overall running pattern, being easy to acquire and not influenced by marker placement error. Therefore, the purpose of this study was to determine if pelvic acceleration patterns during running could be used to classify PFP patients into homogeneous sub-groups. A secondary purpose was to analyze lower limb kinematic data to investigate the practical implications of clustering these subjects based on 3D pelvic acceleration data.

Methods

A hierarchical cluster analysis was used to determine sub-groups of similar running profiles among 110 PFP subjects, separately for males (n = 44) and females (n = 66), using pelvic acceleration data (reduced with principal component analysis) during treadmill running acquired with optical motion capture system. In a secondary analysis, peak joint angles were compared between clusters (α = 0.05) to provide clinical context and deeper understanding of variables that separated clusters.

Results

The results reveal two distinct running gait sub-groups (C1 and C2) for female subjects and no sub-groups were identified for males. Two pelvic acceleration components were different between clusters (PC1 and PC5; p < 0.001). While females in C1 presented similar acceleration patterns to males, C2 presented greater vertical and anterior peak accelerations. All females presented higher and delayed mediolateral acceleration peaks than males. Males presented greater ankle eversion (p < 0.001), lower knee abduction (p = 0.007) and hip adduction (p = 0.002) than all females, and lower hip internal rotation than C1 (p = 0.007).

Conclusions

Two distinct and homogeneous kinematic PFP sub-groups were identified for female subjects, but not for males. The results suggest that differences in running gait patterns between clusters occur mainly due to sex-related factors, but there are subtle differences among female subjects. This study shows the potential use of pelvic acceleration patterns, which can be acquired with accessible wearable technology (i.e. accelerometers).

Three-dimensional assessment of the asymptomatic and post-stroke shoulder: intra-rater test-retest reliability and within-subject repeatability of the palpation and digitization approach

Purpose: Altered three-dimensional (3D) joint kinematics can contribute to shoulder pathology, including post-stroke shoulder pain. Reliable assessment methods enable comparative studies between asymptomatic shoulders of healthy subjects and painful shoulders of post-stroke subjects, and could inform treatment planning for post-stroke shoulder pain. The study purpose was to establish intra-rater test-retest reliability and within-subject repeatability of a palpation/digitization protocol, which assesses 3D clavicular/scapular/humeral rotations, in asymptomatic and painful post-stroke shoulders. Methods: Repeated measurements of 3D clavicular/scapular/humeral joint/segment rotations were obtained using palpation/digitization in 32 asymptomatic and six painful post-stroke shoulders during four reaching postures (rest/flexion/abduction/external rotation). Intra-class correlation coefficients (ICCs), standard error of the measurement and 95% confidence intervals were calculated. Results: All ICC values indicated high to very high test-retest reliability (≥0.70), with lower reliability for scapular anterior/posterior tilt during external rotation in asymptomatic subjects, and scapular medial/lateral rotation, humeral horizontal abduction/adduction and axial rotation during abduction in post-stroke subjects. All standard error of measurement values demonstrated within-subject repeatability error ≤5° for all clavicular/scapular/humeral joint/segment rotations (asymptomatic ≤3.75°; post-stroke ≤5.0°), except for humeral axial rotation (asymptomatic ≤5°; post-stroke ≤15°). Conclusions: This noninvasive, clinically feasible palpation/digitization protocol was reliable and repeatable in asymptomatic shoulders, and in a smaller sample of painful post-stroke shoulders. Implications for Rehabilitation In the clinical setting, a reliable and repeatable noninvasive method for assessment of three-dimensional (3D) clavicular/scapular/humeral joint orientation and range of motion (ROM) is currently required. The established reliability and repeatability of this proposed palpation/digitization protocol will enable comparative 3D ROM studies between asymptomatic and post-stroke shoulders, which will further inform treatment planning. Intra-rater test-retest repeatability, which is measured by the standard error of the measure, indicates the range of error associated with a single test measure. Therefore, clinicians can use the standard error of the measure to determine the "true" differences between pre-treatment and post-treatment test scores.

Cluster-based upper body marker models for three-dimensional kinematic analysis: Comparison with an anatomical model and reliability analysis

Quantifying angular joint kinematics of the upper body is a useful method for assessing upper limb function. Joint angles are commonly obtained via motion capture, tracking markers placed on anatomical landmarks. This method is associated with limitations including administrative burden, soft tissue artifacts, and intra- and inter-tester variability. An alternative method involves the tracking of rigid marker clusters affixed to body segments, calibrated relative to anatomical landmarks or known joint angles. The accuracy and reliability of applying this cluster method to the upper body has, however, not been comprehensively explored. Our objective was to compare three different upper body cluster models with an anatomical model, with respect to joint angles and reliability. Non-disabled participants performed two standardized functional upper limb tasks with anatomical and cluster markers applied concurrently. Joint angle curves obtained via the marker clusters with three different calibration methods were compared to those from an anatomical model, and between-session reliability was assessed for all models. The cluster models produced joint angle curves which were comparable to and highly correlated with those from the anatomical model, but exhibited notable offsets and differences in sensitivity for some degrees of freedom. Between-session reliability was comparable between all models, and good for most degrees of freedom. Overall, the cluster models produced reliable joint angles that, however, cannot be used interchangeably with anatomical model outputs to calculate kinematic metrics. Cluster models appear to be an adequate, and possibly advantageous alternative to anatomical models when the objective is to assess trends in movement behavior.

How to choose and interpret similarity indices to quantify the variability in gait joint kinematics

Repeatability and reproducibility indices are often used in gait analysis to validate models and assess patients in their follow-up. When comparing joint kinematics, their interpretation can be ambiguous due to a lack of understanding of the exact sources of their variations. This paper studied four indices (Root Mean Square Deviation, Mean Absolute Variability, Coefficient of Multiple Correlation, and Linear Fit Method) in relation to five confusing-factors: joints’ range of motion, sample-by-sample amplitude variability, offset, time shift and curve shape. A first simulation was conducted to test the mathematics behind each index. A second simulation tested the influence of the curve shape on the indices using a Fourier’s decomposition. The Coefficient of Multiple Correlation and the Linear Fit method Coefficients were independent from the range of motion. Different Coefficients of Multiple Correlation were found among different joints, leading to misinterpretation of the results. The Linear Fit Method coefficients should not be adopted when time shift increases. Root Mean Square Deviation and Mean Absolute Variability were sensitive to all the confusing-factors. The Linear Fit Method coefficients seemed to be the most suitable to assess gait data variability, complemented with Root Mean Square Deviation or Mean Absolute Variability as measurements of data dispersion.

Is the reliability of 3D kinematics of young obese participants dependent on the hip joint center localization method used?

The aim of this study was to investigate if the test-retest reliability for three-dimensional (3D) gait kinematics in a young obese population is affected by using either a predictive (Davis) or a functional (SCoRE) hip joint center (HJC) localization approach. A secondary goal was to analyze how consistent both methods perform in estimating the HJC position. A convenience sample of ten participants, two females and eight males with an age-based body mass index (BMI) above the 97th percentile (mean±SD: 34.2±3.9kg/m²) was recruited. Participants underwent two 3D gait analysis sessions separated by a minimum of one day and a maximum of seven days. The standard error of measurement (SEM) and the root mean square error (RMSE) of key kinematic parameters along with the root mean square deviation (RMSD) of the entire waveforms were used to analyze the test-retest reliability. To get an estimate of the consistency of both HJC localization methods, the HJC positions determined by both methods were compared to each other. SEM, RMSE, and RMSD results indicate that the HJC position estimations between both methods are not different and demonstrate moderate to good reliability to estimate joint kinematics. With respect to the localization of the HJC, notable inconsistencies ranging from 0 to 5.4cm were observed. In conclusion, both approaches appear equally reliable. However, the inconsistent HJC estimation points out, that accuracy seems to be a big issue in these methods. Future research should attend to this matter.