Soft capacitive sensors for proximity, touch, pressure and shear measurements
Bibliographic record
Abstract
Sensors are devices that convert a physical stimulus into an electrical signal. Mechanical stimuli such as touch, pressure, strain and shear are very important for a plethora of applications. A lot of these application areas, including consumer electronics, sports, health care and robotics, require the sensor to be soft, stretchable and even transparent. In this thesis we demonstrate three capacitive sensors that are each an evolution of the preceding version. The first sensor is a flexible, transparent, proximity and touch sensor based on mutual capacitance technology - the conventional technology used in most touch-screen devices. The novelty in this research is the sensor’s ability to operate while being deformed. This is important for applications where the device is expected to experience a bend or stretch while being interacted with such as in a wearable device and smart clothing. The second sensor in this thesis adds the ability to detect pressure and strain to enable its use in further applications. The sensor uses both mutual capacitance and overlap capacitance to detect the range of stimuli mentioned. The dielectric has cylindrical air gaps that enhance the pressure sensitivity. A 4 X 4 array structure is implemented that demonstrates the detection and differentiation of the different stimuli. However, for artificial skin applications, the ability to sense shear is extremely valuable, for example for helping robots grasp objects. The third sensor developed in this thesis is able to detect proximity and light touch similar to the previous iteration, but with 10X increase in pressure sensitivity (1.3% change in capacitance per kPa applied pressure, compared to 0.13% change for the second sensor) and the ability to detect localized shear (2.2% change in capacitance per kPa of shear stress). The novelty is a patterned dielectric architecture with pillars and sliding supports that enable the top surface of the sensor to slide and buckle like real skin and therefore enable the detection of localized shear. All the sensors use readily available materials (silicone, carbon black and/or polyacrylamide), along with conventional molding and bonding techniques and should be easy to produce in large quantities at low cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.006 | 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".