Radar-Based In-Home Monitoring System for Supporting Aging and Wellness
Bibliographic record
Abstract
This study presents a novel approach to continuous, in-home gait analysis using Multiple Input Multiple Output Frequency-Modulated Continuous Wave (MIMO FMCW) radar, with a specific focus on step length and step count detection within naturalistic environments. Traditional gait monitoring methods are often intrusive and impractical for continuous use, particularly in personal living spaces. Our system was designed to address these limitations by enabling reliable, non-invasive monitoring of gait patterns in cluttered, real-world environments. The study is the first of its kind to successfully detect and analyze varying step lengths and counts in non-linear walking paths within a small apartment setting. The results demonstrate that our radar-based system can accurately capture step length and count with minimal error, offering a promising solution for autonomous, continuous athome gait monitoring. The experimental results demonstrate the method’s robustness in detecting gait parameters in a small apartment, highlighting its potential for long-term, nonintrusive monitoring of individuals at risk of falls or other mobility issues. This study marks a significant advancement in deploying non-invasive gait analysis systems in naturalistic home settings, with promising applications in healthcare, rehabilitation, and elderly care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".