Markless Gait Analysis Using Single-Camera: Validation Of a Novel System Against 3D Motion Capture

TThe assessment of gait performance using quantitative measures can yield crucial insights into an individual's health status. Recently, computer vision-based human pose estimation has emerged as a promising solution for markerless gait analysis, as it allows for the direct extraction of gait parameters from videos. This study aimed to compare the lower extremity kinematics and spatiotemporal gait parameters obtained from a single-camera-based markerless method with those acquired from a marker-based motion tracking system across a healthy population. Additionally, we investigated the impact of camera viewing angles and distances on the accuracy of the markerless method. Our findings demonstrated a robust correlation and agreement (Rxy > 0.75, Rc > 0.7) between the markerless and marker-based methods for most spatiotemporal gait parameters. We also observed strong correlations (Rxy > 0.8) between the two methods for hip flexion/extension, knee flexion/extension, hip abduction/adduction, and hip internal/external rotation. Statistical tests revealed significant effects of viewing angles and distances on the accuracy of the identified gait parameters. While the markerless method offers an alternative for general gait analysis, particularly when marker use is impractical, its accuracy for clinical applications remains insufficient and requires substantial improvement.

Future investigations should explore the potential of the markerless system to measure gait parameters in pathological gaits.

Introduction overview

Walking is one of the most common locomotion in daily living activities and provides vital information about an individual’s health status (Camomilla et al., 2017). Through gait analysis, physicians or therapists can systematically diagnose gait-related disorders and injuries, such as ankle sprains, knee osteoarthritis or hip joint impairments (Baker, 2006, Fritz et al., 2014, Gu et al., 2018). Kinematic gait analysis, a subcategory of gait analysis, involves measuring a range of spatiotemporal and kinematic variables, such as step cycle time, stride length, speed, and lower extremity joint angles, to quantitatively evaluate the degree of abnormality in walking (Chen et al., 2016). Quantitative evaluations of gait performance are more reliable than visual observations alone due to their repeatability and objectivity (Leardini et al., 2017).

To date, motion capture methods used in gait analysis can be classified into two categories: marker-based methods and markerless methods. Marker-based methods, such as motion tracking systems, measure joint kinematic parameters by directly attaching markers to anatomical landmarks on human body (Reissner et al., 2019). Although these motion tracking systems can track body motion with great precision and accuracy, they have several limitations, such as being time-consuming for experiment setup and data post-processing, requiring expertise for correct marker placement, and potentially altering one’s natural body movement patterns (Carse et al., 2013). In a few prior studies, Inertial Measurement Units (IMUs) have also been adopted for the measurement of lower extremity kinematics (Beravs et al., 2011). These IMU sensors are portable, lightweight, less obtrusive, and do not require a controlled environment. Yet, gyroscope-based orientation updates are prone to errors caused by gyro integration drifts, and these errors tend to accumulate over time (Fan et al., 2017).

Conclusions

In conclusion, this study highlighted the potential of a single-camera-based markerless method against a marker-based motion tracking system for gait analysis. The findings indicated a robust correlation and agreement in measuring various spatiotemporal gait parameters and lower extremity joint angle trajectories. The study also revealed that camera viewing angles and distances significantly affect measurement accuracy, recommending specific angles and distances for optimal results. While the...

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Acknowledgement

This manuscript is based upon work supported by the National Science Foundation under Grant # 2013451.

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