Continuous In-Home Gait Analysis Using FMCW Radar in Naturalistic Environments
Bibliographic record
Abstract
Gait analysis is one of the most useful predictors of disease in older adults, but it is not always practical for physicians to monitor. The aim of this paper was to create a system that could continuously and reliably monitor gait patterns of varying step lengths and speeds within cluttered environments, enabling around-the-clock monitoring within personal living spaces. This novel study uses Multiple Input Multiple Output Frequency-Modulated Continuous Wave (MIMO FMCW) radar to track non-linear movement in cluttered environments designed to replicate a living space within a home. A Subjects Tracker and Association (STA) algorithm was proposed to distinguish direct signals with multipath effects and remove ghost signals created by clutter. Six participants were instructed to walk along designated paths with varied step lengths (30 cm, 60 cm, and 80 cm), and our findings supported the system’s ability to capture walking speed, step count, and step length. The system was successful in accurately tracking gait parameters within the naturalistic settings, offering a potential solution to autonomous, continuous in-home gait analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".