Posture Detection Using Sounds and Temperature: LMS-Based Approach to Enable Sensory Substitution
Bibliographic record
Abstract
Sensors are used to determine a variety of health and security parameters. Pressure mats and camera analysis are frequently used to determine a person's chair posture. Measurement of parameters can be derived from a variety of different sensor types, which may already be present in an environment. This paper presents the use of the concept of “sensory substitution,” where a sensor designed to measure Quantity X is used to measure Quantity Y. The concept is used to enable alternative sensing techniques for medical and security applications, such as determining chair occupancy, postural shift times, and even current postural state, without using cameras or pressure sensors. Specifically, the postural state of a subject is determined in a laboratory study using various arrays of both temperature sensors and acoustic sensors. This method can be used standalone, can augment other sensors, or can validate data from pressure or visual-based systems. These alternative sensors could also be used for other aspects of smart infrastructure. The system was tested with different types and styles of furniture, including chairs, armrests, cushions, and beds. A least mean square algorithm was deployed to remove noises enabling acoustic posture detection. Using a thresholding algorithm, postural changes are then identified using audio. In a controlled environment, over 90% of postural change events were detected. Thus, the paper shows two alternative instrumentation and measurement methods to determine occupancy, postural change timings, and even posture states of a person in a chair.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".