Tug-vimu: a GoPro-based mobility assessment bridging the gap between technology and clinical practice

Abstract

Background: Accurate mobility assessment is critical for identifying individuals at risk of falls, particularly among older adults. While gold-standard motion capture systems offer high precision, their clinical adoption is often hindered by high costs, spatial constraints, and technical complexity. Methods: This study introduces and validates TUG-VIMU, a low-cost, portable system for instrumented Timed Up and Go (TUG) testing that integrates a GoPro camera with an ArUco marker. The system fuses inertial data from the camera’s embedded sensors with pose estimation derived from ArUco tracking and video processing algorithms. A key innovation lies in the use of a velocity-adapted continuous wavelet transform for automatic, robust, and adaptive step segmentation. TUG-VIMU was evaluated in a cohort of healthy younger and older adults (n = 16 with 8 over 60, age = 45 ± 19 years), across a range of gait speeds. Results: Automatic trial and phase segmentation achieved a mean error below 0.37 s. Step event detection reached sub-50 ms accuracy, enabling reliable extraction of spatiotemporal parameters. Gait velocity at slow and preferred walking speeds was estimated with a mean error of 0.00 ± 0.02 m/s, and step length accuracy was within - 0.69 ± 1.97 cm. The combined inertial and video-based approach also enabled robust step detection during turning phases, an often overlooked challenge in gait analysis. Conclusion: TUG-VIMU demonstrated high temporal and spatial accuracy, robust gait phase detection, and reliable estimation of clinically relevant parameters. Its performance was comparable to established motion capture systems, particularly at slower walking speeds, while offering enhanced accessibility, portability, and ease of use. These findings support the potential of TUG-VIMU as a practical and scalable tool for gait assessment in clinical and community settings. Future work includes automated reporting features, open-source distribution, and validation in populations with mobility impairments such as Parkinson’s disease.