Wearable Low-cost Motion Trackers for Dynamic Stability Parameters – Calibration based on Artificial Intelligence

Background: Low-cost inside‑out wearable trackers can be deployed at scale to measure body motion. However, errors in the estimated sensor position and orientation can propagate into coordinate transformations and affect derived metrics such as gait parameters and measures of dynamic stability.

Methods: Healthy adults walked on a treadmill at 0.5–2.0 m/s while VIVE Ultimate Tracker (VUT) and Vicon data were recorded. Data-driven calibration models were developed to correct tracker coordinates and estimate the full-body centre of mass (CoM) from a sacrum-only configuration. Agreement with Vicon was assessed using root mean square error (RMSE), mixed-effects Bland-Altman limits of agreement, mean absolute error (MAE), and intraclass correlation coefficients (ICCs).

Results: Data-driven calibration models improved coordinate-level agreement. For gait parameters, model-corrected VUT showed small errors against Vicon, with MAE of 0.24–0.71 mm for step height, 1.73–4.63 mm for step length, 0.15–0.95 mm for step width, and 0.26–0.88 mm for foot clearance across speeds. Proxy CoM-derived XCoM and MoS showed excellent agreement with Vicon. For the sacrum-only CoM pipeline, data-driven calibration reduced CoM RMSE from 103.65–104.04 mm to 7.55–8.95 mm across speeds. It also markedly reduced systematic error in CoM-derived stability outcomes, with XCoM bias decreasing from 172.92 mm to 0.29 mm and MoS bias decreasing from -75.09 mm to -3.54 mm.

Conclusion: Data-driven calibration improved the measurement utility of low-cost VUTs under treadmill walking. A sacrum-only configuration estimated CoM-derived XCoM and MoS with near-reference agreement, supporting inexpensive and relatively simple gait and stability measurement in controlled settings.