A Novel Non-Intrusive Framework for Real-Time Sleep Status Detection with Single 2D LiDAR
Bibliographic record
Abstract
Sleep monitoring devices that use contact sensors or camera technology often compromise sleep quality and pose significant privacy risks.This paper investigates a novel sleep status recognition method employing 2D LiDAR technology, which enables low-cost, real-time monitoring of sleep quality while reducing privacy risks.The study systematically explores the behavior recognition normal framework based on 2D LiDAR, with a focus on point cloud data processing procedures and techniques.A comprehensive sleep status recognition framework is proposed, utilizing a single 2D LiDAR, encompassing three critical aspects: detecting target definition using DBSCAN clustering and quantitative calculation, identification of the same target object, and changes in the status of the same target object.Additionally, an experimental environment was developed for testing on subjects during afternoon naps.The results of ten experimental trials demonstrate that the proposed method is capable of effectively detecting changes in sleep states.Additionally, during object recognition, three distinct target objects were consistently identified, corresponding to the positions of the human head, waist, and legs.Further analysis of the data reveals that each experimental trial recorded 5 to 10 state changes, a frequency consistent with current public findings on sleep quality assessment.A detailed examination of the first group's data indicates minimal movement in the head (average displacement of 12.2 cm), substantial movement in the legs (average displacement of 50.6 cm), and moderate movement in the waist (average displacement of 18.2 cm).These variations in distance are not attributable to differences in LiDAR angles but align with commonly observed patterns of turning during human sleep.These findings provide valuable support for the advancement of low-cost sleep care solutions and related business opportunities.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".