Validation and Examination of Upper Extremity Kinematics in Typically Developing Children During the Box and Blocks Functional Test using Marker-based and Markerless Technology

Joint kinematics of upper extremity (UE) impairments in a pediatric population are often difficult to examine using marker-based motion capture. As a result of the cost and availability of tools such as marker-based motion capture in clinical settings, clinicians use functional tasks to examine improvement in movement quality. However, some of these tasks, such as the Box and Block test (BBT), which is examined in this study, rely on scoring to assess motor improvement. This scoring method can be misleading due to the possibility of movement compensation to improve scores. Therefore, finding kinematic correlations that can lead to improved BBT scores could improve the quality of functional assessments by providing discrete measures for clinicians. Understanding human motion using marker-based motion capture has been the accepted standard in biomechanics. However, it is not without its drawbacks, especially in upper extremity examination due to complex anatomical positioning. The introduction of markerless motion capture software could drastically alter how human biomechanics is analyzed in various settings. Additionally, avoiding possible errors due to clothing and skin movement could greatly improve reported results. Therefore, examining similarities in UE joint kinematics between accepted marker-based and markerless software could introduce markerless motion capture as a method for examining complex kinematics. This study aims to examine UE joint kinematics in a typically developing pediatric population while they complete the BBT, as well as validate Theia3D (Theia Markerless Inc., Kingston, ON, Canada). Marker-based motion capture was used to capture UE kinematics during the BBT. This study was performed on typically developing children aged 7, 9, and 11. Average and peak joint angles were determined, as well as hand segment velocity and path length. Significant correlations to BBT scores were found in peak shoulder flexion (FLEX) angle (r = -0.556, p-value = 0.009), peak (r = -0.479, p-value = 0.028), and average (ρ = -0.535, p-value = 0.012) wrist extension (EXT) angle, average mediolateral (ML) hand segment velocity (r = 0.494, p-value = 0.023), and path length (r = -0.522, p-value = 0.015). Additionally, significant differences between BBT scores (p-value = 0.005), peak shoulder FLEX (p-value = 0.024), and peak shoulder abduction (ABD) angle (p-value = 0.022) were found between the 7- and 11-year-old age groups. Peak elbow FLEX angle was significantly different (p-value = 0.049) between 9- and 11-year-old age groups. These results show that the BBT score could be related to the shoulder and wrist angle, as well as hand segment velocity and path length for typically developing children. Furthermore, root mean square deviation (RMSD) values less than 6° existed in all joint angles. Intraclass correlation coefficients (ICCs) greater than 0.75 were found in shoulder ABD (ICC = 0.79), forearm pronation (ICC = 0.81), wrist EXT (ICC = 0.75), and radial deviation (ICC = 0.87). Additionally, validation results between the marker-based and markerless systems show that there are differences in pose estimations and joint calculations based on rotation sequences. Overall, UE joint kinematics are shown to have correlations to BBT scores, so scores alone may not be indicative of movement quality in other patient populations. Markerless motion capture shows many benefits, however, it should be noted that, due to the complexity of upper extremity motion analysis, understanding what joint rotation sequences align the best with task-specific motions is important. Master of Science Human motion is commonly analyzed using marker-based motion capture, which consists of fitting participants with retroreflective markers that can be seen by specialized cameras. However, due to equipment costs, difficult implementation, and the occurrence of markers shifting on skin or being concealed by clothing, markerless motion capture is beginning to be introduced into biomechanics research and could be used in hospitals, clinical settings, and for outdoor examination due to its versatility. The software uses machine learning software that can determine skin landmarks in videos from several cameras to develop a 3D skeleton. Markerless motion capture could be beneficial in examining patients with neuromotor disorders or injuries due to being able to capture abnormal or quick movement which often accompanies many neurological disorders that affect motor function. Additionally, observing movement in children is a challenge due to markers being too close together on smaller limbs. Due to cost and obtainability, clinicians tend to use functional tests to examine improvements in motor function by a scoring system relevant to the specific test, such as the Box and Block test (BBT) which will be used in this study. However, there is the possibility of the patient's ability to adapt to the test to improve their score without improving general motor function. Therefore, it is important to find a relationship between upper limb movement and BBT scores. This study aims to find correlations between upper limb movement and Box and Block test scores as well as differences between 7-, 9-, and 11-year-old age groups and compare marker-based motion capture and the Theia3D (Theia Markerless Inc., Kingston, ON, Canada) markerless motion capture software. Joint assessment is completed with motion capture, which uses reflective markers on specific landmarks on the skin surface. Markerless motion capture is collected simultaneously with marker-based motion capture to assess similarities. The entire procedure was also completed 2 times within 1 visit. The results showed meaningful comparisons between the BBT scores and shoulder and wrist angle, and hand velocity. BBT scores and shoulder angles were shown to be different between the 7- and 11-year-old age groups. Elbow angles were shown to be different between the 9- and 11-year-old age groups. Additionally, comparisons between the marker-based and markerless results showed that all resulting joint angle data captured by each system were similar. Markerless measurement comparisons showed similarities between both sessions as well. These results show that there are ways to provide discrete measurements in clinical settings to examine movement quality. Comparisons between both motion analysis systems show the need to determine task-specific analyses to obtain meaningful results concerning the upper limbs, due to the inherent joint complexity and differing methods of completing the same task.