Measurement and Modeling Mutual Capacitance of Electrical Wiring and Humans
Bibliographic record
Abstract
In a recent series of electric field sensing experiments, a theremin was used to measure the mutual capacitance between a human being and a length of electrical wiring. The instrument, based on the LM555 circuit, measures the deflections in capacitance due to the proximity of a human. The measurements are repeatable, and the difference in capacitance for a person at 0.5 m with a person at 1 m is consistent with the difference computed, assuming the human acts as a ground plane for the wiring. Much of the current literature in electric field sensing focuses on measures and models of mutual capacitance for humans interacting with plate conductors [J. R. Smith, Electric field imaging, Ph.D. dissertation, Mass. Inst. Technol., Cambridge, MA, 1999; N. Karlsson and J. O. Jarrhed, A capacitive sensor for the detection of humans in a robot cell, in Proc. IEEE IMTC Rec., May 18-20, 1993 pp. 164-166.], especially fingers near touch screens [D. Wiebe, A. Machynia, K. Mazur, and J. Epp, Human-computer interface device based on electric field sensing, Ph.D. dissertation, Univ. Manitoba, Winnipeg, MB, Canada, 2004]. The present investigation considers conducting wires to allow the development of portable rapidly deployable human proximity sensing systems that exploit existing electrical infrastructure in buildings. The experiment described here demonstrates that sensing with wires is possible at ranges on the order of a meter and provides evidence that modeling the person as a ground plane of finite extent provides a rough estimate of the change in mutual capacitance
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".