Tug-vimu: a GoPro-based mobility assessment bridging the gap between technology and clinical practice
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".