Principal Component Analysis of Whole-Body Kinematics Using Markerless Motion Capture During Static Balance Tasks

3D motion capture data into a kinematic composite score for assessing musculoskeletal impairments

Biomechanical analysis is essential for understanding and monitoring musculoskeletal impairments, with implications for clinical diagnostics and research. Current clinical methods provide isolated joint measures or qualitative observations, failing to capture motion complexity. While 3D biomechanical testing is comprehensive, its application is hindered by data volume, making it challenging to derive clinically relevant conclusions. Approaches to distill motion often neglect time-series data or are dependent on population size. To address these gaps, this study introduces the Kinematic Composite Score (K-Score), a metric that distills high-dimensional motion while preserving individual variability. The objective of this research is to outline the methodology of the K-Score algorithm, highlight its strengths, limitations, and applications. We conducted a comparative study of the K-Score Algorithm against (1) the conventional isolated kinematic measures, and (2) traditional Principal Component Analysis. The analysis was conducted with a cohort of chronic low back pain (LBP) patients, who exhibit tremendous movement heterogeneity. The K-Score outperformed traditional isolated metrics in differentiating overall motion of LBP patients from healthy controls (K-Score: controls = 94.16 ± 2.64, LBP = 85.82 ± 7.73, p < 0.001). The K-Score also demonstrated significant differences in overall motion between male and female participants, where females with LBP demonstrated higher scores than males (p < 0.001). Importantly, the K-Score was not sensitive to BMI (p = 0.49), age (p = 0.14), height (p = 0.11), or sample size. In conclusion, the K-Score addresses key limitations of traditional approaches by encapsulating full-body, time-series data within a single score that is adaptable across motion capture systems and activities, making it a powerful tool for clinical biomechanics research.

Validation of a video-based pose estimation algorithm for the assessment of balance error scoring system in single limb stance test

BACKGROUND

Single Limb Stance Test (SLST) is a reliable and validated test to estimate balance performance. However, assessment of the SLST performance, e.g. by using the Balance Error Scoring System (BESS), can be time-consuming and subjective. To deliver effective balance interventions, a reliable and accessible balance assessment method is imperative.

RESEARCH QUESTION

Can video-based pose estimation be effectively utilized to validate the BESS assessment for SLST, as compared to both human observation and marker-based assessment methods?

METHODS

60-second eyes-closed SLST trials were recorded using an iPad camera and a marker-based motion capture system. Mediapipe was applied to estimate the whole-body kinematics from the video recordings. The kinematic data were processed by threshold-based error detection algorithms to calculate the corresponding BESS total and sub-scores. To validate the video-based BESS assessment, the results were compared to human and marker-based motion capture system BESS assessments using repeated measures ANOVA and correlation coefficients (CC).

RESULTS

There was no significant difference in the BESS total score between the assessment methods and the correlation between assessment methods was good with CC's ranging from 0.69 to 0.77. However, a significant difference in BESS forefoot and heel lifting sub-scores between the video-based and the human or marker-based assessments was found because Mediapipe failed to capture the detail of the foot motion.

SIGNIFICANCE

Video-based pose estimation is a reliable and accessible method to assess SLST performance. It can be used to examine and speed up SLST assessment using the BESS total score. However, future research and development in capturing foot motion is needed.

Design of a Freely Accessible Web Application (Instrument for the Measurement of Balance in Primary Education, IMEP) for the Assessment of Static and Dynamic Balance in Children Aged 6-9 Years Based on Force Platforms

Background: The proper development of balance is essential in the acquisition of a correct physical condition, as well as in the evolutionary follow-up at early ages, and its periodic evaluation is very relevant in the educational environment. Objectives: The objective of this research was to design an accessible web application for static and dynamic balance assessment, based on a force platform and motion analysis software. Methods: The Single leg balance test (SLB), Tandem balance test (TBT), and Y balance test (YBT) were performed on a sample of 75 children aged 6 to 9 years. Results: The results show that static balance is more complex at an older age, greater standing height, and with eyes closed (p < 0.001). Regarding the center of pressure (COP), its variability was greater in girls owing to a lower Total Force (TF) at the time of the test (p < 0.05). Parallel observation with the Kinovea software has made it possible to elaborate a scale from 1 to 10 points for integration into an open-access web application (IMEP) to assess static and dynamic balance. Conclusions: The creation of an ad hoc application for primary school teachers and students has been possible by using validated devices obtaining a rating scale, which facilitate the monitoring of students' functional evolution and offers the possibility of scheduling physical education sessions with a preventive approach as well as a focus on improving physical condition.

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

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