Accuracy and repeatability of joint angles measured using a single camera markerless motion capture system

Markerless motion capture systems have developed in an effort to evaluate human movement in a natural setting. However, the accuracy and reliability of these systems remain understudied. Therefore, the goals of this study were to quantify the accuracy and repeatability of joint angles using a single camera markerless motion capture system and to compare the markerless system performance with that of a marker-based system. A jig was placed in multiple static postures with marker trajectories collected using a ten camera motion analysis system. Depth and color image data were simultaneously collected from a single Microsoft Kinect camera, which was subsequently used to calculate virtual marker trajectories. A digital inclinometer provided a measure of ground-truth for sagittal and frontal plane joint angles. Joint angles were calculated with marker data from both motion capture systems using successive body-fixed rotations. The sagittal and frontal plane joint angles calculated from the marker-based and markerless system agreed with inclinometer measurements by <0.5°. The systems agreed with each other by <0.5° for sagittal and frontal plane joint angles and <2° for transverse plane rotation. Both systems showed a coefficient of reliability <0.5° for all angles. These results illustrate the feasibility of a single camera markerless motion capture system to accurately measure lower extremity kinematics and provide a first step in using this technology to discern clinically relevant differences in the joint kinematics of patient populations.     

Posturographic analysis through markerless motion capture without ground reaction forces measurement

The ability of the central nervous system to control posture and balance has been used with increasing frequency for the diagnosis and/or treatment evaluation of various neuromuscular diseases. Typically this analysis (Posturographic Analysis) is based on tracking the motion of the center of mass (COM) during quiet standing, however direct measurement of the COM has been commonly approximated using the movement of the center of pressure (COP). The purpose of this study was to apply and validate a new method to track the COM (center of mass) and COP (center of pressure) from a visual hull measured using a markerless motion capture (MMC) method. The method was tested by comparing the calculation of the COP from direct measurements of the COP. The deviations between the methods, below 2 mm, were small relative to the average range of movement guaranteeing a satisfactory signal to noise ratio. This new method requires only kinematic data through MMC method and without the need of a force plate can identify the influence of individual body segments to motion of the COM.     

Static and dynamic error of a biplanar videoradiography system using marker-based and markerless tracking techniques

The use of biplanar videoradiography technology has become increasingly popular for evaluating joint function in vivo. Two fundamentally different methods are currently employed to reconstruct 3D bone motions captured using this technology. Marker-based tracking requires at least three radio-opaque markers to be implanted in the bone of interest. Markerless tracking makes use of algorithms designed to match 3D bone shapes to biplanar videoradiography data. In order to reliably quantify in vivo bone motion, the systematic error of these tracking techniques should be evaluated. Herein, we present new markerless tracking software that makes use of modern GPU technology, describe a versatile method for quantifying the systematic error of a biplanar videoradiography motion capture system using independent gold standard instrumentation, and evaluate the systematic error of the W.M. Keck XROMM Facility's biplanar videoradiography system using both marker-based and markerless tracking algorithms under static and dynamic motion conditions. A polycarbonate flag embedded with 12 radio-opaque markers was used to evaluate the systematic error of the marker-based tracking algorithm. Three human cadaveric bones (distal femur, distal radius, and distal ulna) were used to evaluate the systematic error of the markerless tracking algorithm. The systematic error was evaluated by comparing motions to independent gold standard instrumentation. Static motions were compared to high accuracy linear and rotary stages while dynamic motions were compared to a high accuracy angular displacement transducer. Marker-based tracking was shown to effectively track motion to within 0.1?mm and 0.1 deg under static and dynamic conditions. Furthermore, the presented results indicate that markerless tracking can be used to effectively track rapid bone motions to within 0.15 deg for the distal aspects of the femur, radius, and ulna. Both marker-based and markerless tracking techniques were in excellent agreement with the gold standard instrumentation for both static and dynamic testing protocols. Future research will employ these techniques to quantify in vivo joint motion for high-speed upper and lower extremity impacts such as jumping, landing, and hammering.     

A computer vision based method for 3D posture estimation of symmetrical lifting

Work-related musculoskeletal disorders (WMSD) are commonly observed among the workers involved in material handling tasks such as lifting. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks. Such an assessment has been mainly conducted using surface marker-based methods, which is time consuming and tedious. During the past decade, computer vision based pose estimation techniques have gained an increasing interest and may be a viable alternative for surface marker-based human movement analysis. The aim of this study is to develop and validate a computer vision based marker-less motion capture method to assess 3D joint kinematics of lifting tasks. Twelve subjects performing three types of symmetrical lifting tasks were filmed from two views using optical cameras. The joints kinematics were calculated by the proposed computer vision based motion capture method as well as a surface marker-based motion capture method. The joint kinematics estimated from the computer vision based method were practically comparable to the joint kinematics obtained by the surface marker-based method. The mean and standard deviation of the difference between the joint angles estimated by the computer vision based method and these obtained by the surface marker-based method was 2.31 ± 4.00°. One potential application of the proposed computer vision based marker-less method is to noninvasively assess 3D joint kinematics of industrial tasks such as lifting.     

Assessment of the functional method of hip joint center location subject to reduced range of hip motion

Motion analysis of the lower extremities usually requires determination of the location of the hip joint center. The results of several recent studies have suggested that kinematic and kinetic variables calculated from motion analysis data are highly sensitive to errors in hip joint center location. "Functional" methods in which the location of the hip joint center is determined from the relative motion of the thigh and pelvis, rather than from the locations of bony landmarks, are promising but may be ineffective when motion is limited. The aims of the present study were to determine whether the accuracy of the functional method is compromised in young and elderly subjects when limitations on hip motion are imposed and to investigate the possibility of locating the hip joint center using data collected during commonly studied motions (walking, sit-to-stand, stair ascent, stair descent) rather than using data from an ad hoc trial in which varied hip motions are performed. The results of the study suggested that functional methods would result in worst-case hip joint center location errors of 26mm (comparable to the average errors previously reported for joint center location based on bony landmarks) when available hip motion is substantially limited. Much larger errors ( approximately 70mm worst-case), however, resulted when hip joint centers were located from data collected during commonly performed motions, perhaps because these motions are, for the most part, restricted to the sagittal plane. It appears that the functional method can be successfully implemented when range of motion is limited but still requires collection of a special motion trial in which hip motion in both the sagittal and frontal planes is recorded.     

Model-based approach for human kinematics reconstruction from markerless and marker-based motion analysis systems

Modeling tools related to the musculoskeletal system have been previously developed. However, the integration of the real underlying functional joint behavior is lacking and therefore available kinematic models do not reasonably replicate individual human motion. In order to improve our understanding of the relationships between muscle behavior, i.e. excursion and motion data, modeling tools must guarantee that the model of joint kinematics is correctly validated to ensure meaningful muscle behavior interpretation. This paper presents a model-based method that allows fusing accurate joint kinematic information with motion analysis data collected using either marker-based stereophotogrammetry (MBS) (i.e. bone displacement collected from reflective markers fixed on the subject's skin) or markerless single-camera (MLS) hardware. This paper describes a model-based approach (MBA) for human motion data reconstruction by a scalable registration method for combining joint physiological kinematics with limb segment poses. The presented results and kinematics analysis show that model-based MBS and MLS methods lead to physiologically-acceptable human kinematics. The proposed method is therefore available for further exploitation of the underlying model that can then be used for further modeling, the quality of which will depend on the underlying kinematic model.     

A new method for estimating joint parameters from motion data

Joint centers and axes of rotation (joint parameters) are central to all branches of movement analysis. In gait analysis, the standard protocol used to determine hip and knee joint parameters is prone to errors arising from palpation, anthropometric regression equations, and misplaced alignment devices. Several alternative methods have been proposed, but to date none have been shown to be accurate and reliable enough for use in the clinical setting. This article describes a new method for joint parameter estimation. The new method can be summarized as follows: (i) the motions of two adjacent segments spanning a single joint are tracked, (ii) the axis of rotation between every pair of observed segment configurations is computed, (iii) the most likely intersection of all axes (effective joint center) and most likely orientation of the axes (effective joint axis) is found. Initial validation of the method was conducted on a hinged mechanical analog and a single healthy adult subject. For the analog, the center was found to be within 3.8 mm of the geometric center and 2.0 degrees of the geometric axis (standard deviation). For the adult subject, hip centers varied on the order of 1-3 mm, knee centers by 3-9 mm, and knee axes by 2.0 degrees. The results suggest that the new method is an objective, precise, and practical alternative to the standard clinical approach.     

Kinematical models to reduce the effect of skin artifacts on marker-based human motion estimation

The estimation of the skeletal motion obtained from marker-based motion capture systems is known to be affected by significant bias caused by skin movement artifacts, which affects joint center and rotation axis estimation. Among different techniques proposed in the literature, that based on rigid body model, still the most used by commercial motion capture systems, can smooth only part of the above effects without eliminating their main components. In order to sensibly improve the accuracy of the motion estimation, a novel technique, named local motion estimation (LME), is proposed. This rests on a recently described approach that, using virtual humans and extended Kalman filters, estimates the kinematical variables directly from 2D measurements without requiring the 3D marker reconstruction. In this paper, we show how such method can be extended to include the computation of the local marker displacement due to skin artifacts. The 3D marker coordinates, expressed in the corresponding local reference coordinate frames, are inserted into the state vector of the filter and their dynamics is automatically estimated, with adequate accuracy, without assuming any particular deformation function. Simulated experiments of lower limb motion, involving systematic mislocations (5, 10, 20 mm) and random errors of the marker coordinates and joint center locations (+/-5, +/-10, +/-15 mm), have shown that artifact motion can be substantially decoupled from the global skeletal motion with an effective increase of the accuracy wrt standard techniques. In particular, the comparison between the nominal kinematical variables and the one recovered from markers attached to the skin surface proved LME to be sensibly superior (50% in the worse condition) to the methods imposing marker-bone rigidity. In conclusion, while requiring further validation on real movement data, we argue that the proposed method can constitute an appropriate approach toward the improvement of the human motion estimation.     

In-vivo measurement of dynamic joint motion using high speed biplane radiography and CT: application to canine ACL deficiency

Dynamic assessment of three-dimensional (3D) skeletal kinematics is essential for understanding normal joint function as well as the effects of injury or disease. This paper presents a novel technique for measuring in-vivo skeletal kinematics that combines data collected from high-speed biplane radiography and static computed tomography (CT). The goals of the present study were to demonstrate that highly precise measurements can be obtained during dynamic movement studies employing high frame-rate biplane video-radiography, to develop a method for expressing joint kinematics in an anatomically relevant coordinate system and to demonstrate the application of this technique by calculating canine tibio-femoral kinematics during dynamic motion. The method consists of four components: the generation and acquisition of high frame rate biplane radiographs, identification and 3D tracking of implanted bone markers, CT-based coordinate system determination, and kinematic analysis routines for determining joint motion in anatomically based coordinates. Results from dynamic tracking of markers inserted in a phantom object showed the system bias was insignificant (-0.02 mm). The average precision in tracking implanted markers in-vivo was 0.064 mm for the distance between markers and 0.31 degree for the angles between markers. Across-trial standard deviations for tibio-femoral translations were similar for all three motion directions, averaging 0.14 mm (range 0.08 to 0.20 mm). Variability in tibio-femoral rotations was more dependent on rotation axis, with across-trial standard deviations averaging 1.71 degrees for flexion/extension, 0.90 degree for internal/external rotation, and 0.40 degree for varus/valgus rotation. Advantages of this technique over traditional motion analysis methods include the elimination of skin motion artifacts, improved tracking precision and the ability to present results in a consistent anatomical reference frame.     

A novel validation and calibration method for motion capture systems based on micro-triangulation

Motion capture systems are widely used to measure human kinematics. Nevertheless, users must consider system errors when evaluating their results. Most validation techniques for these systems are based on relative distance and displacement measurements. In contrast, our study aimed to analyse the absolute volume accuracy of optical motion capture systems by means of engineering surveying reference measurement of the marker coordinates (uncertainty: 0.75 mm). The method is exemplified on an 18 camera OptiTrack Flex13 motion capture system. The absolute accuracy was defined by the root mean square error (RMSE) between the coordinates measured by the camera system and by engineering surveying (micro-triangulation). The original RMSE of 1.82 mm due to scaling error was managed to be reduced to 0.77 mm while the correlation of errors to their distance from the origin reduced from 0.855 to 0.209. A simply feasible but less accurate absolute accuracy compensation method using tape measure on large distances was also tested, which resulted in similar scaling compensation compared to the surveying method or direct wand size compensation by a high precision 3D scanner. The presented validation methods can be less precise in some respects as compared to previous techniques, but they address an error type, which has not been and cannot be studied with the previous validation methods.     

In vivo motion of the scaphotrapezio-trapezoidal (STT) joint

It has previously been shown that the articulation of the scaphotrapezio-trapezoidal (STT) joint can be modeled such that the trapezoid and trapezium are tightly linked and move together on a single path relative to the scaphoid during all directions of wrist motion. The simplicity of such a model is fascinating, but it leaves unanswered why two distinct carpal bones would have a mutually articulating surface if there were no motion between them, and how such a simplistic model of STT joint motion translates into the more complex global carpal motion. We performed an in vivo analysis of the trapezoids and trapeziums of 10 subjects (20 wrists) using a markerless bone registration technique. In particular, we analyzed the centroid spacing, centroid displacements, kinematics, and postures of the trapezoid and trapezium relative to the scaphoid. We found that, on a gross level, the in vivo STT motion was consistent with that reported in vitro. In addition, we found that the magnitude of trapezoid and trapezium motion was dependent upon the direction of wrist motion. However, we also found that when small rotations and displacements are considered there were small but statistically significant relative motions between the trapezoid and trapezium (0.4 mm in maximum flexion, 0.3 mm in radial deviation and at least 10 degrees in flexion extension and ulnar deviation) as well as slight off-path rotations. The results of this study indicate that the STT joint should be considered a mobile joint with motions more complex than previously appreciated.     

Evaluation of a new algorithm to determine the hip joint center

Accurately locating the hip joint center is a challenging and important step in many biomechanical investigations. The purpose of this study was to test the accuracy and robustness of a "pivoting" algorithm used to locate the hip center. We tested the performance of this algorithm with data acquired by manipulating a ball and socket model of the hip through several motion patterns. The smallest mean errors of 2.2+/-0.2 mm occurred with a circumduction motion pattern, while the largest errors of 4.2+/-1.3 mm occurred with single-plane motion (e.g., flexion/extension). Introducing random noise with an amplitude of 30 mm increased the errors by only 1.3+/-0.5 mm with a circumduction motion pattern. The pivoting algorithm performs well in the laboratory, and further work is warranted to evaluate its performance in a clinical setting.     

Mandibular kinematics represented by a non-orthogonal floating axis joint coordinate system

There are many methods used to represent joint kinematics (e.g., roll, pitch, and yaw angles; instantaneous center of rotation; kinematic center; helical axis). Often in biomechanics internal landmarks are inferred from external landmarks. This study represents mandibular kinematics using a non-orthogonal floating axis joint coordinate system based on 3-D geometric models with parameters that are "clinician friendly" and mathematically rigorous. Kinematics data for two controls were acquired from passive fiducial markers attached to a custom dental clutch. The geometric models were constructed from MRI data. The superior point along the arc of the long axis of the condyle was used to define the coordinate axes. The kinematic data and geometric models were registered through fiducial markers visible during both protocols. The mean absolute maxima across the subjects for sagittal rotation, coronal rotation, axial rotation, medial-lateral translation, anterior-posterior translation, and inferior-superior translation were 34.10 degrees, 1.82 degrees, 1.14 degrees, 2.31, 21.07, and 6.95 mm, respectively. All the parameters, except for one subject's axial rotation, were reproducible across two motion recording sessions. There was a linear correlation between sagittal rotation and translation, the dominant motion plane, with approximately 1.5 degrees of rotation per millimeter of translation. The novel approach of combining the floating axis system with geometric models succinctly described mandibular kinematics with reproducible and clinician friendly parameters.     

Non-invasive determination of coupled motion of the scapula and humerus--an in-vitro validation

Measuring the motion of the scapula and humerus with sub-millimeter levels of accuracy in six-degrees-of-freedom (6-DOF) is a challenging problem. The current methods to measure shoulder joint motion via the skin do not produce clinically significant levels of accuracy. Thus, the purpose of this study was to validate a non-invasive markerless dual fluoroscopic imaging system (DFIS) model-based tracking technique for measuring dynamic in-vivo shoulder kinematics. Our DFIS tracks the positions of bones based on their projected silhouettes to contours on recorded pairs of fluoroscopic images. For this study, we compared markerlessly tracking the bones of the scapula and humerus to track them with implanted titanium spheres using a radiostereometric analysis (RSA) while manually manipulating a cadaver specimen's arms. Additionally, we report the repeatability of the DFIS to track the scapula and humerus during dynamic shoulder motion. The difference between the markerless model-based tracking technique and the RSA was ±0.3 mm in translation and ±0.5° in rotation. Furthermore, the repeatability of the markerless DFIS model-based tracking technique for the scapula and humerus was ±0.2 mm and ±0.4°, respectively. The model-based tracking technique achieves an accuracy that is similar to an invasive RSA tracking technique and is highly suited for non-invasively studying the in-vivo motion of the shoulder. This technique could be used to investigate the scapular and humeral biomechanics in both healthy individuals and in patients with various pathologies under a variety of dynamic shoulder motions encountered during the activities of daily living.     

Markerless analysis of front crawl swimming

Research on motion analysis of swimmers is commonly based on video recordings of the subject's motion, which are analyzed by manual digitization of feature points by an operator. This procedure has two main drawbacks: it is time-consuming, and it is affected by low repeatability. Therefore, the application of video-based, automatic approaches to motion analysis was investigated. A video-based, markerless system for the analysis of arm movements during front crawl swimming was developed. The method proposed by Corazza et al. (2010) was modified in order to be used into water environment. Three dimensional coordinates of shoulder, elbow and wrist joints centers of 5 sprint swimmers performing front crawl swimming were determined. Wrist joint velocity was also calculated. Accuracy and reliability of the proposed technique were evaluated by means of comparison with traditional manual digitization (SIMI Reality Motion Systems GmbH). Root mean square distance (RMSD) values between trajectories estimated with the two techniques were determined. Results show good accuracy for wrist joint (RMSD<56mm), and reliability, evaluated on one subject, comparable to the inter-operator variability associated with the manual digitization procedure. The proposed technique is therefore very promising for quantitative, wide-scale studies on swimmers' motion.     

A Deep Neural Network-based method for estimation of 3D lifting motions

The aim of this study is developing and validating a Deep Neural Network (DNN) based method for 3D pose estimation during lifting. The proposed DNN based method addresses problems associated with marker-based motion capture systems like excessive preparation time, movement obstruction, and controlled environment requirement. Twelve healthy adults participated in a protocol and performed nine lifting tasks with different vertical heights and asymmetry angles. They lifted a crate and placed it on a shelf while being filmed by two camcorders and a synchronized motion capture system, which directly measured their body movement. A DNN with two-stage cascaded structure was designed to estimate subjects’ 3D body pose from images captured by camcorders. Our DNN augmented Hourglass network for monocular 2D pose estimation with a novel 3D pose generator subnetwork, which synthesized information from all available views to predict accurate 3D pose. We validated the results against the marker-based motion capture system as a reference and examined the method performance under different lifting conditions. The average Euclidean distance between the estimated 3D pose and reference (3D pose error) on the whole dataset was 14.72 ± 2.96 mm. Repeated measures ANOVAs showed lifting conditions can affect the method performance e.g. 60° asymmetry angle and shoulder height lifting showed higher 3D pose error compare to other lifting conditions. The results demonstrated the capability of the proposed method for 3D pose estimation with high accuracy and without limitations of marker-based motion capture systems. The proposed method may be utilized as an on-site biomechanical analysis tool.     

The use of basis functions in modelling joint articular surfaces: application to the knee joint

This article introduces a new method to represent bone surface geometry for simulations of joint contact. The method uses the inner product of two basis functions to provide a mathematical representation of the joint surfaces. This method guarantees a continuous transition in the direction of the surface normals, an important property for computation of joint contact. Our formulation handles experimental data that are not evenly distributed, a common characteristic of digitized data of musculoskeletal morphologies. The method makes it possible to represent highly curved surfaces, which are encountered in many anatomical structures. The accuracy of this method is demonstrated by modeling the human knee joint. The mean relative percentage error in the representation of the patellar track surface was 0.25% (range 0-1.56%) which corresponded to an absolute error of 0.17mm (range 0-0.16mm).     

The efficacy of a video-based marker-less tracking system for gait analysis

Most clinical gait analyses are conducted using motion capture systems which track retro-reflective markers that are placed on key landmarks of the participants. An alternative to a three-dimensional (3D) motion capture, marker-based, optical camera system may be a marker-less video-based tracking system. The aim of our study was to investigate the efficacy of the use of a marker-less tracking system in the calculation of 3D joint angles for possible use in clinical gait analysis. Ten participants walked and jogged on a treadmill and their kinematic data were captured with a marker and marker-less tracking system simultaneously. The hip, knee and ankle angles in the frontal, sagittal and transverse planes were computed. Root Mean Square differences (RMS_diff) between corresponding angles for each participant’s support phase were calculated and averaged to derive the mean within-subject RMS_diff. These within-subject means were averaged to obtain the mean between-subject RMS_diff for the relevant joint angles in the two gait conditions (walking and jogging). The RMS_diff between the two tracking systems was less than 1° for all rotations of the three joint angles of the hip and knee. However, there were slightly larger differences in the ankle joint angles. The results of this study suggest a potential application in gait analysis in clinical settings where observations of anatomical motions may provide meaningful feedback.     

Estimation of spinal joint centers from external back profile and anatomical landmarks

Defining a subject-specific model of the human body is required for motion analysis in many fields, such as in ergonomics and clinical applications. However, locating internal joint centers from external characteristics of the body still remains a challenging issue, in particular for the spine. Current methods mostly require a set of rarely accessible (3D back or trunk surface) or operator dependent inputs (large number of palpated landmarks and landmarks-based anthropometrics). Therefore, there is a need to provide an alternative way to estimate joint centers only using a limited number of easily palpable landmarks and the external back profile. Two methods were proposed to predict the spinal joint centers: one using only 6 anatomical landmarks (ALs) (2 PSIS, T8, C7, IJ and PX) and one using both 6 ALs and the external back profile. Regressions were established using the X-ray based 3D reconstructions of 80 subjects and evaluated on 13 additional subjects of variable anthropometry. The predicted location of joint centers showed an average error 9.7 mm (±5.0) in the sagittal plane for all joints when using the external back profile. Similar results were obtained without using the external back profile, 9.5 mm (±5.0). Compared to other existing methods, the proposed methods offered a more accurate prediction with a smaller number of palpated points. Additional methods have to be developed for considering postures other than standing, such as a sitting position.