Optimal Head-mounted IMU Placement for Heart Rate Detection Using Ballistography
Bibliographic record
Abstract
Continuous and accurate heart rate monitoring is pivotal for the early detection of cardiovascular conditions. Traditional heart rate sensing methods, such as the use of adhesive electrocardiogram (ECG) electrodes, often fall short in terms of comfort and convenience for everyday wear and continuous monitoring. Ballistocardiography (BCG), a non-invasive alternative, offers a promising solution by measuring the body's mechanical reactions to the ejected blood by the heart during cardiac cycles. This paper introduces a novel heart detection method utilizing IMU-based BCG applied to the head, focusing on optimizing sensor placement on the head for improved accuracy. Noteworthy is that the detection algorithm needs to be robust against noise and movement artifacts; therefore, the choice of sensor location and signal processing techniques become key factors. This pilot study explores various IMU mounting locations on the head and signal processing techniques to improve the feasibility and accuracy of head-mounted wearable devices employing BCG.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".