Wrist-to-Back Signal Reconstruction for Real-World Gait Monitoring via Frequency-Domain Modelling
Bibliographic record
Abstract
Wearable accelerometers are commonly used for gait analysis in healthcare and rehabilitation, providing objective, continuous, real-world data. While lower-back sensors are widely used for capturing gait features, however, their long-term use is limited by discomfort and impracticality. Wrist-worn sensors are more user-friendly but less accurate for specific gait metrics. This study presents a novel, proof-ofconcept method to reconstruct lower-back accelerometer signals using only wrist-based data, via a frequency-domain transfer function approach. Using a two-day naturalistic, freeliving dataset from multiple participants wearing accelerometers on both the wrist and lower back, gait bouts were detected and clustered to extract representative gait cycles. A spectral transfer function modelled the wrist-lower back relationship. The proposed method achieved high intraindividual accuracy (Pearson correlation$=0.9141$; RMSE =0.4351) and maintained robust performance across individuals (mean Pearson correlation$=0.827$; RMSE$=0.604$), demonstrating its potential to generalize across demographic variability. Unlike prior deep learning-based models, our approach is interpretable, computationally efficient, and does not require large training datasets, making it well-suited for real-world applications. These findings highlight the feasibility of replacing Lower back sensors with wrist-worn devices, making it suitable for long-term, passive gait monitoring in clinical and consumer health applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".