Semi-Automated Quantitative Gait Analysis Using VR Motion Tracking Data and Power BI: A Pilot Study

Abstract

This paper presents a pilot semi-automated framework for quantitative gait analysis based on motion data acquired from commercially available virtual reality tracking devices. The objective of the study is to evaluate the feasibility of using VR motion tracking technology for experimental quantitative assessment of human gait characteristics and to demonstrate a methodological workflow capable of transforming raw positional data into analytically interpretable outputs.
The proposed approach combines motion data acquisition using HTC Vive Tracker devices, signal preprocessing, Savitzky–Golay filtering, derivation of kinematic parameters, and semi-automated segmentation of characteristic gait-related motion segments. An important component of the framework is analytical visualisation and signal interpretation implemented in the Power BI environment, enabling efficient comparison of repetitive movement patterns and extraction of selected motion characteristics.
Pilot results indicate that commercially available VR tracking devices can provide sufficiently stable and interpretable motion data for experimental quantitative gait analysis. The proposed framework enables identification of characteristic gait segments, extraction of movement-related characteristics, and establishes a methodological basis for future comparative studies of motor behaviour. Although the presented approach does not represent a clinical diagnostic tool, it demonstrates a promising direction for further applications at the intersection of virtual reality, motion analytics, and movement assessment.