Which method should we use to determine the hip joint center location in individuals with a high amount of soft tissue?

BACKGROUND: This study investigated the most accurate method for estimating the hip joint center position in clinical 3D gait analysis for young individuals with high amounts of soft tissue. We compared position estimates of five regression-based and two functional methods to the hip joint center position obtained through 3D free-hand ultrasound. METHODS: For this purpose, the data of 14 overweight or obese individuals with a mean age of 13.6 (SD 2.1 yrs) and a BMI of 36.5 (SD 7.1 kg/m2, range 26-52 kg/m2) who underwent standard clinical 3D gait analysis were used. The data of each participant were processed with five regression-based and two functional methods and compared to the hip joint center identified via 3D free-hand ultrasound. FINDINGS: The absolute location errors to 3D free-hand ultrasound for each anatomical plane and the Euclidean distances served as outcomes next to their effects on gait variables. The data suggest that regression-based methods are preferable to functional methods in this population, as the latter demonstrated the highest variability in accuracy with large errors for some individuals. INTERPRETATION: Based on our findings we recommend using the regression method presented by Hara et al. due to its superior overall accuracy of <9 mm on average in all planes and the lowest impact on kinematic and kinetic output variables. We do not recommend using the Harrington equations (single and multiple) in populations with high amounts of soft tissue as they require pelvic depth as input, which can be massively biased when a lot of soft tissue is present around the pelvis.

Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data post-processing and analysis.

Markerless motion capture has recently attracted significant interest in clinical gait analysis and human movement science. Its ease of use and potential to streamline motion capture recordings bear great potential for out-of-the-laboratory measurements in large cohorts. While previous studies have shown that markerless systems can achieve acceptable accuracy and reliability for kinematic parameters of gait, they also noted higher inter-trial variability of markerless data. Since increased inter-trial variability can have important implications for data post-processing and analysis, this study compared the inter-trial variability of simultaneously recorded markerless and marker-based data. For this purpose, the data of 18 healthy volunteers were used who were instructed to simulate four different gait patterns: physiological, crouch, circumduction, and equinus gait. Gait analysis was performed using the smartphone-based markerless system OpenCap and a marker-based motion capture system. We compared the inter-trial variability of both systems and also evaluated if changes in inter-trial variability may depend on the analyzed gait pattern. Compared to the marker-based data, we observed an increase of inter-trial variability for the markerless system ranging from 6.6% to 22.0% for the different gait patterns. Our findings demonstrate that the markerless pose estimation pipelines can introduce additionally variability in the kinematic data across different gait patterns and levels of natural variability. We recommend using averaged waveforms rather than single ones to mitigate this problem. Further, caution is advised when using variability-based metrics in gait and human movement analysis based on markerless data as increased inter-trial variability can lead to misleading results.

Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait.

Markerless motion capturing has the potential to provide a low-cost and accessible alternative to traditional marker-based systems for real-world biomechanical assessment. However, before these systems can be put into practice, we need to rigorously evaluate their accuracy in estimating joint kinematics for various gait patterns. This study evaluated the accuracy of a low-cost, open-source, and smartphone-based markerless motion capture system, namely OpenCap, for measuring 3D joint kinematics in healthy and pathological gait compared to a marker-based system. 21 healthy volunteers were instructed to walk with four different gait patterns: physiological, crouch, circumduction, and equinus gait. Three-dimensional kinematic data were simultaneously recorded using the markerless and a marker-based motion capture system. The root mean square error (RMSE) and the peak error were calculated between every joint kinematic variable obtained by both systems. We found an overall RMSE of 5.8 (SD: 1.8 degrees) and a peak error of 11.3 degrees (SD: 3.9). A repeated measures ANOVA with post hoc tests indicated significant differences in RMSE and peak errors between the four gait patterns (p ¡ 0.05). Physiological gait presented the lowest, crouch and circumduction gait the highest errors. Our findings indicate a roughly comparable accuracy to IMU-based approaches and commercial markerless multi-camera solutions. However, errors are still above clinically desirable thresholds of two to five degrees. While our findings highlight the potential of markerless systems for assessing gait kinematics, they also underpin the need to further improve the underlying deep learning algorithms to make markerless pose estimation a valuable tool in clinical settings.