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Documentation:FIB book/Motion Capture Methods in Injury Biomechanics

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Summary

1.0 Background

In biomechanics, motion capture is the process of recording and analyzing human movement to generate precise, digital data about a subject’s motion. Data is often recorded via camera systems tracking markers put in specific locations on a subject. Motion capture has many applications in biomechanics. It can be used to precisely measure joint angles, velocities, and dynamic movements, acquire data in gait and posture, and, when integrated with force plates or pressure sensors, it can help assess how forces interact with the body [1]. In clinical settings, it is essential for rehabilitation programs: adjusting treatment plans based on movement patterns, and integrating with electromyography (EMG) [1]. It can also be used in research and design of new equipment [1]. For example, motion capture can measure complex movements very precisely and be used to gain a deeper understanding of how different parts of the body interact during movement. These can then identify abnormal movement patterns that may contribute to injury, or to design equipment with necessary interventions to correct these patterns[2]. Motion capture has extensive applications in kinematics and inverse kinematics.

  • Kinematics:
    • record and track 3D trajectories of how body segments and joints move through space over time [3].
    • calculate segment positions, orientations, and joint angles [3].
  • Inverse Kinematics:
    • align biomechanical models of human data with motion data [3].
    • allows for computation of precise joint rotations, solving for best estimates of rigid body segment positions, and rotations and translations of limb segments from models constructed by motion capture [3] [4].
Figure 1: Optoelectronic motion capture markers on a study participant[5]

In this literature review, we will be looking over the various methods that motion capture is used in the field of injury biomechanics. We will give a high level surface overview of current biomechanics motion capture methods and cover two main applications of motion capture in biomechanics: tissue tests and crash tests. Tissue tests involve marking body regions on cadavers to determine mechanical responses, helping researchers, accident reconstructionists, and physicians understand how internal body structures respond to scenarios, as well as provide accurate human anatomy details. Motion capture in crash testing is a primary tool for quantifying human response and assessing the biofidelity of anthropometric test devices (ATDs). This review will also discuss the advantages and disadvantages of motion capture in biomechanics, as well as controversy related to injury biomechanics motion capture. Finally, we will explore future applications of motion capture in biomechanics, such as high speed cameras in training sets for markerless motion capture and estimate poses in cars and tissues.

1.1 Passive Markers

There are two types of markers in biomechanics: passive and active. Passive markers bounce infrared light back to optoelectronic cameras, as opposed to emitting light on their own [6]. Their accuracy depends on the quality of camera optics, such as resolution, lens quality, calibration, and tracking software algorithms [6]. When placed accurately and paired with high quality, properly calibrated cameras, passive markers enable 3D motion reconstruction with high-frame-rate, high-resolution kinematic data for researchers [6] [7]. Markers can be made lightweight and in different sizes, and offer increased usage flexibility over active markers [8]. However, they are not without limitations. Passive markers move relative to underlying bones due to skin and muscle motion, introducing inaccuracies in data [7]. They also have clothing and environment constraints - the best conditions to use them are in controlled lab environments with appropriate lighting (poor lighting affects the signal output) and line of sight [7]. This often limits their ability to capture realistic sport environments, often focused on specific movements that can be done in a lab [7].

1.2 Active Markers

Conversely, active markers are typically infrared LEDs that emit light, and often require a local power supply [9] [10]. These light sources are then picked up by surrounding cameras [10]. For tracking, cameras triangulate the position in space [9]. They also eliminate unwanted reflections and are better at long distances [8]. Like passive markers, active markers also move relative to underlying bones due to skin and muscle motion, introducing inaccuracies in data [7]. Another limitation is that active markers are directional; if LEDs are oriented away from cameras, they can’t be detected [9].  The requirement of a power source can also lead to needing wiring, which can cause difficulties with some desired movements and add another factor that needs consideration in trials [10].  Both active and passive markers are used for tracking 3D positions of anatomical landmarks on subjects, which can be used to provide biomechanical data for everything from analyzing sports performance to estimating joint positions and angles [11].

1.3 Three-Camera Systems

There are two main types of camera systems in biomechanics motion capture: 3 camera systems and multi-camera systems. Three-camera systems are arranged in rigid stereophotogrammetric configurations and are able to function as their own unit [12]. This makes them more portable than multi-camera systems and much easier to deploy [13]. One example is the Optitrack system. which is also pre-calibrated, reducing setup time and operator variability [13]. Three-camera systems are more prone to registration and tracking errors, and are also less accurate (one camera occluded leads to 0.2-0.5mm error, multiple cameras occluded lead to 0.6-1.6mm errors) [14]. Therefore, they are often used in labs with space constraints, and for experiments with less complex setups and smaller capture volumes [13].

1.4 Multi-Camera Systems

Multi-camera systems have significantly improved 3D spatial accuracy and redundancy as they decrease chance of camera occlusion [14]. They also have much higher precision due to having more cameras in the system [14]. Multi-camera systems are expensive and require a larger amount of space to deploy and they can also be harder to calibrate due to the number of cameras. An example of a multi camera system is the Vicon MX3. Multi-camera systems are most often used in biomechanics labs needing more accuracy, such as clinical spaces [14].

1.5 SAEJ211-1/2 in Biomechanics Motion Capture

The SAEJ211 specifications detail how to measure things in impact tests, such as coordinate systems, dummy force polarities, and also what instrument characteristics are sufficient for tests [15]. It ensures that all researchers measure data the same way [15]. For motion capture, the SAEJ211 specifications allow researchers to define and standardize their global coordinate system, data acquisition, and orientations [15]. For example, how to transform pelvic and femur kinematics from the local coordinate system to the sled’s reference frame [16]. SAEJ211 has two main subsections applicable to motion capture in biomechanics. SAEJ211-1 covers standards for electronic instrumentation [17]. This includes sensors, data channels, and signal processing [18]. The specifications guide ensures uniformity in instrumentation practice, calibration, measurement guidelines, and standardized collection of impact data [18]. It also guides how to calibrate sensors, set proper sampling rates and filters, and synchronize different data sources [18]. SAEJ211-2 covers photographic instrumentation standards [17]. It gives performance criteria for optical data channels, such as high-speed cameras and imaging systems [19]. The specifications ensure standardized setup, calibration, and validation of imaging systems so they reliably extract kinematic variables that correspond in time and space to sensor/impact data [19].

2.0 Cadaveric Tissue Testing

In injury biomechanics, one of the most common applications of motion capture is marking body regions on cadavers to determine mechanical responses that cannot ethically be tested in living humans. This process helps researchers, accident reconstructionists, and physicians understand how internal structures respond in different scenarios and provides accurate details of human anatomy that would otherwise be estimated or unknown.

2.1 Motion Capture for Tissue Testing

Most injury biomechanics applications with motion tracking can use passive or active markers, which each have their benefits and drawbacks. One study using passive markers compared the relationship between load stress on the Achilles tendon and the angle of the ankle joint for the purpose of verifying the accuracy of current assumptions used in tendon research[20]. To do this, seven passive markers were used to detect the position of the distal tibia, malleoli, calcaneus, and the first, second, and fifth metatarsal heads. An 8-camera VICON system was used, capturing images at a sampling rate of 200 Hz to capture accurate 3D data for all markers. Another study used passive markers on the cervical spine to qualitatively analyze the range of motion (ROM) to better inform surgical recommendations related to disc injury[21]. Three markers were attached to each cervical vertebra and captured with 3D tracking with a VICON camera setup.

Another study used motion capture to test the success of knee surgery on fresh frozen cadaveric knee segments using a gait simulator for knees[22]. In this study, three OptoTrak Certus (active) marker clusters were placed and fixed with pins on the distal femur, proximal tibia, and the motion simulator. Using motion capture, it was possible to compare the mechanical differences before and after the surgery took place to better inform surgeons and physicians about their recommendations to patients. One study used active motion capture in an attempt to better understand individual motions, coupled motions, and stiffness of the spine and ribs under various loading conditions[23]. The T12 end was rigidly fixed, while the T1 end was free to move, so only the T1 location was marked, tracked by three OptoTrak Certus markers. The OptoTrak Certus system operates at a maximum frame rate calculated by , where N is the number of sensors; since both studies used three sensors, the frame rate was likely 920 Hz[24].

As seen in the studies discussed here, both passive and active marker-based systems are used in several applications depending on the needs and resources of the study design. One of the recurring patterns seen within all these studies is the use of commercial toolkits to speed up and enhance data collection. While it is possible to process 3D kinematic data individually, the availability of cameras integrated with kinematic processing software (like VICON or OptoTrak) show the need and importance for the types of research discussed above.

2.2 Limitations of Motion Capture for Tissue Testing

Invasive tissue studies remove some common limitations of motion capture in living and moving humans, including artifacts from soft tissue movement over unmoving bones and need for study to be done outside the lab. Despite this, there are still other factors that limit accuracies within a study. Some tests used to evaluate tissues are done at slow or medium frame rates (20-30 fps) because they simulate slower movements like ROM or gait. Still, others require very high frame rates (around 1000-2000 fps), like most impact or crash testing where movement of tissues like brain displacement[25] or strain rate in the aorta[26] are of great importance; these require very high speed cameras. Cameras of this quality are relatively new, consumer-level and priced cameras allowing for 1000 fps weren’t available on the market until the mid-2010s[27]. SAE J211 standards are updated often, although they will always fall at least a little bit behind the current technology; in 2017, the regulation only stated that cameras must acquire data at 500 fps[28]. The ever-changing regulations and vast history of studies using technology that are now below regulation standards limits the quality of results from research that simply follows the regulation, and makes comparison of new data with higher quality technology difficult.

Using cadaveric specimens for research includes many advantages but also brings limitations[29]. Cadaveric specimens are strongly biased towards the elderly population, which has several issues within research from a lens of diversity and accuracy. They also pose difficulty in re-creating biofidelic responses due to rigor mortis, preservation techniques, and lack of muscle tone. While cadavers give the advantage of studying and instrumenting internal structures invasively, some instrumentation is still limited to protect biofidelic responses of undisturbed tissue, as much as it is possible.

3.0 Crash Testing

Motion capture systems are critical in crash testing as the primary tool for quantifying human response and assessing the biofidelity of ATDs. They enable researchers to precisely capture the three-dimensional motion of the surrogate’s body segments, such as head, thorax, and pelvis, throughout impact events, allowing comparison of ATD behaviour against post-mortem human surrogate (PMHS) response. Such measurements form the basis for developing and validating injury criteria, improving restraint systems, and refining human body computational models used in occupant safety research.

3.1 Motion Capture for Crash Testing

High-speed optical motion capture systems have become a fundamental tool for quantifying human body motion in crash testing. These systems provide the precision and temporal resolution required to characterize the motion of skeletal structures in six degrees of motion under dynamic loading conditions. In one study, motion capture markers were used invasively to quantify the detailed kinematics of PMHSs during simulated frontal impacts[30]. Figure 5 shows the initial position of PMHS used in the experiment[30]. A 16-camera Vicon MX system operating at 1000 Hz recorded the trajectories of orthogonal arrays of four retroreflective markers that were rigidly fixed to specific bony landmarks[30]. Markers were attached to the head, T1, T8, L2, L4 vertebrae, pelvis, bilateral 4th and 8th ribs, and sternum, allowing researchers to reconstruct the 3D skeletal motion of each segment[30]. Combined with the accelerometer measurements, the dynamic neck loads are estimated for further analysis[30].

Figure 5: PMHS Initial Position

Similarly, Ash et al. employed a comparable methodology using four-target retroreflective marker clusters to measure both translational and rotational motion of skeletal segments[31]. These clusters were surgically attached to the skull (head), selected vertebrae (T1, T8, L2, L4), pelvis, and bilateral acromia to capture upper-body kinematics during frontal impact tests[31]. A 16-camera, 1000 Hz Vicon MX optoelectronic system tracked the 3D trajectories of all clusters throughout the impact[31]. The resulting marker data were transformed using rigid-body kinematic assumptions to compute segmental displacements and rotations relative to the vehicle buck coordinate system[31]. This methodology provided a detailed quantitative basis for assessing biofidelity by comparing the skeletal motion of PMHS with the response of ATDs under equivalent test conditions. Motion capture systems are therefore invaluable in crash testing, as they provide precise, time-resolved kinematic data that allow researchers to quantify skeletal motion, estimate internal loads, and evaluate how closely ATDs replicate the complex biomechanics of the human body.

3.2 Limitations of Motion Capture for Crash Testing

Despite their precision and widespread use, motion capture systems introduce several limitations that must be considered when interpreting crash-test kinematic data. The accuracy of three-dimensional reconstruction depends on both marker geometry and camera calibration. In the methodology described by Lopez-Valdés et al., the four-marker arrays used for skeletal tracking exhibited a static reconstruction error with a standard deviation of approximately 1.4 mm[30]. While this level of precision is generally acceptable for biomechanical analysis, it still introduces uncertainty into fine-scale motion estimates, particularly when calculating inter-vertebral displacements or small angular rotations. The same study also noted practical challenges associated with integrating motion capture systems into full-scale sled tests. The physical setup required for the Vicon optical array constrained the placement of on-board high-speed video cameras, requiring two additional off-board high-speed video cameras to assess the potential head contacts[30]. It is crucial to consider potential alignment discrepancies between motion capture and video data when multiple speed cameras are used. Together, these limitations highlight the trade-off between high-fidelity kinematic measurement and experimental flexibility in crash testing. While optical motion capture provides detailed quantitative data, its accuracy and visibility constraints necessitate complementary measurement techniques, such as accelerometers, angular rate sensors, and high-speed video, to achieve a comprehensive understanding of occupant dynamics.

4.0 In-Vivo Human Motion Capture

Figure 2: Sagittal Plane Knee MRI Showing Soft Tissues[32]

In-vivo motion capture provides researchers with the ability to accurately capture realistic living human movements. However, conducting motion capture studies on in-vivo patients requires a complex understanding of tissue structures. Tissues such as skin, tendons and ligaments are prone to movement independent from the joint location, causing a discrepancy between the movement of markers and precise joint locations. This phenomenon is referred to as soft tissue artifact (STA).

4.1 Limitations of Motion Capture for In-Vivo Human Motion

Research from the Institute of Biomechanics and Orthopedics in Germany compares the kinematics of the knee using in-vivo marker-based motion capture, as well as high-speed dual fluoroscopic imaging system and magnetic resonance imaging for validation[33]. This study found that measuring knee flexion-extension angle using marker-based motion capture results in errors of -3.24.3° during walking, and -5.85.4° during running[33]. This correlates with an error of 78% during walking and 43% during running, resulting from the lack of accuracy due to the tissue interface between the skin markers and underlying bone. Figure 2 illustrates the complexity of joints, ligaments, and muscle tissues surrounding the knee through magnetic resonance imaging, showing that without an MRI, accurately tracking the bone contact location becomes difficult. The high errors observed in this study show that STA plays a significant role in precise knee joint mechanics. Despite this, marker-based motion capture remains a prominent technique in musculoskeletal injury research.

A similar study was conducted to compare marker-based motion capture with dual fluoroscopy (DF), an X-ray technique, to examine the significance of STA in hip-joint tracking[34]. The study tracked 11 subjects while conducting six activities, from standing, to walking at flat, small, and large inclines, as well as both internal and external hip rotation. The results of this study were a soft tissue artifact offset of up to 5.4 cm on the greater trochanter, as well as a hip joint angle measuring 1.9° more extended, 0.6° more adducted, and 5.8° more internally rotated than when tracked with dual fluoroscopy. This shows that STA is not only significant in knee kinematics, but in other joints as well. The study also found STA differed by activity, subject, and direction, which shows the difficulties in reducing STA errors in motion capture.

5.0 Discussion

Overall, the use of motion capture technology has contributed greatly to biomechanical research by providing a tool to movement, through kinematic data obtained during both ordinary human motions and injury-inducing motions. Motion capture does not require sensors, which eliminates the need for invasive adjustments to cadaver and ATD anthropometry. Other benefits of motion capture in injury biomechanics include contributions to tissue tests to accurately analyze injury mechanisms, crash tests to assess human injury risk and ATD biofidelity, and in-vivo biomechanical testing to track realistic human movement.

For tissue testing, cadavers in combination with marker-based motion capture systems are effective, as they can be instrumented invasively to capture biomechanical responses to impact forces and accelerations. However, there are significant ethical concerns regarding approval and reporting standards for using cadaver tissues. The core issues include:

  • Lack of Uniformity: Reporting on ethical approval and the source of cadaveric specimens varies widely across different journals, regions, and research settings[35].
  • Informed Consent: There is not a "gold standard" for practices regarding informed consent for body donation, only suggestions, and many donation forms fail to meet these recommended standards[36].
  • Tissue Misuse: There are very clear and strict regulations for use of living tissue and humans, however, there is no overarching legal framework for how PMHS tissues should be used[37].

Motion capture also plays a significant role in crash testing. In addition to tracking instrumented ATDs to enforce vehicle standards such as SAEJ211-½, motion capture has been used by researchers to instrument cadavers to quantify motion, and estimate loads in critical regions such as the neck, pelvis, and spine. However, crash testing is often subject to funding and resource limitations: crash tests are expensive and usually government-funded through organizations like NHSTA and Transport Canada. Furthermore, exiting crash test research largely exclude the average sized female from regulations to assess the protection of vehicle occupants[38] , demonstrating the need for more inclusive vehicle regulations and a gap in the literature regarding motion capture systems applied to female anatomies.

In-vivo motion capture is used in biomechanics research to capture a realistic human response, however, one of the biggest questions in the field of motion capture today is whether marker-based motion capture is able to accurately capture bone movement, due to the interference of soft-tissue artifacts such as muscle and skin tissues. Even when motion capture data is collected with a perfectly calibrated camera and an optimized frame rate, there is still a significant uncertainty in precise joint location due to the tissue interface (STA) between the skin markers and underlying bone, as discussed in Section 4. To address the controversy of whether marker-based motion capture can accurately track joint location and angles, bone pins, external fixators, and percutaneous tracking devices have been proposed for further research into STA[33]. However, these techniques can be invasive, and can even change the motion pattern of a subject, which will produce biomechanically inaccurate results, even if joint locations and angles are precisely determined. From the studies above, it’s clear that marker-based motion capture is inadequate for capturing accurate in-vivo measurements. However despite this, it still remains a key technique in biomechanics for tracking joint motion. Until further research into reducing the effect of soft tissue on joint locations is performed, studies using marker-based motion capture to evaluate precise joint angles without using other tracking methods for validation should be met with skepticism.

6.0 Future Work

Although there is a significant body of research on marker-based motion capture, there still remain gaps in the literature. Calibration can be improved for crash testing to reduce positional errors, which will enable crash tests to accurately determine vertebral deflection to assess injury criteria. Furthermore, research is also needed to address the controversy of whether marker-based motion capture can accurately track in-vivo joint angles due to the soft tissue artifact. Further work into techniques to reduce soft tissue artifacts, either through additional instrumentation or validation with other methods, is critical for the future of motion capture in biomechanical research.

The future of motion capture in injury biomechanics lies in markerless motion capture. Markerless motion capture does not use markers; instead, it uses pose and object detection algorithms to track moving objects in video footage. This would be useful for injury biomechanics work, as high-speed video footage is already available to train more specific models for ATDs or vehicles. As the majority of research in this field is currently focused on whole-body movements, no direct applications to tissue measurements were found at this time. One of the main companies on the market currently resides in Canada and is called Theia [39]. There is a lot of work in the field of medical imaging and automatic landmark detection with machine learning [40]. One study utilized digitally reconstructed radiographs to derive kinematics [41]. This method was found to be comparable to the gold standard manual bead tracking [41], but did not utilize machine learning. In addition, the CT images did not capture natural movement; the specimen was either stationary or study personnel moved the lower leg specimen through the motions of gait [41]. These methods did not assess injury tolerance. In the realm of markerless motion capture and accident reconstruction, one study used a combination approach to track ATD head movement during a frontal impact [42]. Their estimation of the ATD's head was weighted between the prediction model, region-based matching, and patch-based matching [42]. Pose estimation in this case was used to find a transformation that minimized errors; region-based matching minimized the projected surface of the model and the object extracted in the image; patch-based matching finds matching regions between consecutive frames for prediction and compares the current image with a generated one to minimize drift [42]. They used this method to track the ATD head during two conditions: 1) ATD head collision with the car's hood and 2) ATD head collision with the interior of the car during an oblique frontal impact [42]. It was found that the marker-based system tracked a single point on the head, whereas their method tracked the full head pose, allowing subsequent calculations such as head rotation and penetration depth [42].

Given the current state of technology, joint center accuracy in markerless motion capture is not sufficiently robust for clinical applications, and there is a lack of datasets available for biomechanical research for pose identification [43]. This gap can be filled by creating labelled datasets, but it would require millions of labeled images, with time and money needed to create those. This would make the application of pose estimation for bystander accident reconstruction difficult and driver vehicle kinematics nearly impossible with its occlusions. More research needs to be done to create increasingly diverse labelled datasets that could be applied to tissue mechanics and accident reconstruction research, such as datasets made from video specific to these fields, as current object detection algorithms are made from more general datasets (ex. COCO dataset)[44].

References

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  38. ↑ Svedberg, Wanna; Linder, Astrid (June 2019). "Review of average sized male and female occupant models in European regulatory safety assessment tests and European laws: Gaps and bridging suggestions". Accident Analysis & Prevention. 127: 156–162 – via Science Direct.
  39. ↑ "Theia Markerless". Retrieved November 30th, 2025. Check date values in: |access-date= (help)
  40. ↑ Erickson, B.J. (March 2017). "Machine Learning for Medical Imaging". Radio Graphics. 37: 505–515.
  41. ↑ 41.0 41.1 41.2 Perez, K. N. (November 2024). "Cadaveric validation of markerless tracking using weightbearing computed tomography versus conventional computed tomography imaging techniques". Medical Engineering & Physics. 134: 104252–104252.
  42. ↑ 42.0 42.1 42.2 42.3 42.4 Mannen, E. M.; Anderson, J. T.; Arnold, P. M.; Friis, E. M. (April 2015). [10.1016/j.jbiomech.2015.03.021. "Mechanical analysis of the human cadaveric thoracic spine with intact rib cage"] Check |url= value (help). Journal of Biomechanics. 48: 2060–2066.
  43. ↑ "Optotrack Certus User Guide" (PDF). November 2014. Retrieved November 30th, 2025. Check date values in: |access-date= (help)
  44. ↑ "Common Objects in Context". Retrieved November 30th, 2025. Check date values in: |access-date= (help)


External Links

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