Comparative Evaluation of Inertial Measurement Unit versus Vision Based Motion Capture

Accurate motion capture is critical for biomechanics research, clinical gait analysis, human–computer interaction, and the entertainment industry. Two dominant paradigms exist: marker-based systems, which use inertial measurement units affixed to the body, and marker-less, vision-based systems, which infer kinematics directly from camera data using deep-learning techniques. While both have matured rapidly, a systematic, head-to-head evaluation under identical experimental conditions is still lacking. This study benchmarks the spatiotemporal accuracy, robustness, and practical usability of state-of-the-art inertial measurement units and vision-based pipelines across a representative set of motor tasks (walking, running, squatting, and upper-limb reaches) performed by 5 healthy adults. Joint angles from inertial measurement units were reconstructed via a complementary-filter fusion of accelerometer, gyroscope, and magnetometer signals. Vision-based approaches, specifically the most advanced methods, rely on cameras that capture images in red, green, and blue color channels and employ a series of convolutional layers for reconstruction. Additionally, we have also included double, depth-based Kinect version two cameras for comparison. Results indicated that both the inertial measurement unit and vision-based pipelines estimated lower-limb joint angles with generally acceptable differences, yet the vision approach showed larger deviations, especially during out-of-plane motion and brief self-occlusions. Inertial measurement unit, by contrast, remained more consistent in those scenarios but exhibited gradual drift during prolonged recordings when magnetic updates were absent. Although the camera-only system offered quicker setup and greater participant comfort, a streamlined calibration routine narrowed the preparation gap for inertial measurement units. Vision hardware was less expensive, but its higher computational demands offset that advantage. Taken together, the study highlights a trade-off: marker-less vision prioritizes plug-and-play usability, whereas inertial measurement units deliver steadier, higher-precision tracking.