TissueDB/Simulators/Cricothyrotomy Simulator (D'Auria)
General Information
The D'Auria Cricothyrotomy Simulator is a Cyber-Physical-System (CPS) overlay that adds automatic, real-time technique scoring to the University of Washington BioRobotics cricothyrotomy trainer. Conductive-foil sensors on the trainer's neck landmarks feed an Arduino and an 8×8 LED display, and an Activity Detection Engine matches the trainee's timed instrument-contact sequence against expert-defined good and bad models to flag correct versus incorrect technique. The tissue-simulating substrate is the White UW trainer — see TissueDB/Simulators/White UW Cricothyrotomy Simulator.
| Field | Details |
|---|---|
| General Information | An add-on layer that instruments an existing White UW trainer for automatic scoring; it adds no tissue hardware of its own. Source: D'Auria & Persia, IEEE IRI 2014. |
| Features and Basic Operation | The trainee performs the standard six-step cricothyrotomy on the trainer — palpate the cricothyroid membrane; incise the skin; incise the membrane (about 1 cm); insert the tracheal hook into the cricoid; insert the hemostat; then insert the endotracheal tube. Six conductive-foil sensors time each instrument contact (20 ms debounce, 10 Hz low-pass filter), an 8×8 LED matrix shows contact state, and the Activity Detection Engine matches the timed sequence against expert-defined "good" and "bad" activity models to give immediate correct/incorrect feedback. |
| Current Development Status | Peer-reviewed (IEEE IRI 2014; companion paper, Springer, 2015); the reported trial measured the engine's activity-recognition performance and user satisfaction only — clinical skill transfer to operative practice was not tested. |
| Estimated Build Time and Cost | Not reported in source., Under US$50 |
| Specialized Tools and Equipment | Scalpel, tracheal hook, and hemostat — each wired into the contact-sensing circuit — plus an endotracheal tube (the procedure instruments named by D'Auria & Persia, 2014). |
| Version | As described in D'Auria & Persia (2014). |
| Development Team Contact Information | Daniela D'Auria and Fabio Persia, Department of Electrical Engineering and Information Technology, University of Naples Federico II, Italy. Contact addresses given in the source paper: daniela.dauria4@unina.it, fabio.persia@unina.it. |
Tissues
| Tissue | Qty | Material | Cost | Notes |
|---|---|---|---|---|
| Skin | 1 segment | Bicycle inner tube segment | — | Skin layer concealing the sensor zones, so the cricothyroid membrane must be located by palpation. Detailed on the White UW page. |
| Cartilage (thyroid and cricoid) | 1 set | 3D-printed ABS plastic, with a mobile cricothyroid joint | — | Rigid laryngeal landmarks; carry the cricoid and lower-ring sensor zones. Detailed on the White UW page. |
| Cartilage (tracheal rings) | — | Compliant foam strips | — | Soft rings simulating the tracheal cartilage; carry foil sensors. Ring count and foam grade not specified in source. Detailed on the White UW page. |
| Trachea | 1 tube | Thin cardboard tube | — | Airway wall and lumen; carries the posterior and lateral sensor zones. Detailed on the White UW page. |
Structural Parts
| Part Name | Qty | Material | Cost | Notes |
|---|---|---|---|---|
| Arduino Uno | 1 | Microcontroller board (Atmel Atmega 328) | — | Runs the Activity Detection Engine, reads the six foil sensors by matrix scanning, and drives the LED display. |
| Conductive foil sensors | 6 | Conductive foil (grade not specified in source) | — | Contact electrodes at the six neck landmarks; only the midline cricothyroid-membrane zone is the correct incision site. The landmark map is in the build steps. |
| 8×8 LED matrix | 1 | LED array | — | Shows sensor-contact state and procedural-step feedback in real time. |
| Wiring harness | 1 set | Conductor wire | — | Connects the six foil sensors to the Arduino inputs. |
Build Instructions
Phase 1: Prepare the UW Hardware Substrate
- Build or acquire the UW BioRobotics cricothyrotomy trainer per TissueDB/Simulators/White UW Cricothyrotomy Simulator because the CPS overlay requires a pre-assembled hardware substrate with 3D-printed thyroid and cricoid cartilages, the cardboard tracheal tube, compliant foam tracheal rings, and the bicycle inner-tube skin layer.
- Confirm that the cartilage landmarks are palpable through the skin layer and that the cardboard trachea accommodates an endotracheal tube.
Phase 2: Apply Conductive Foil Sensors
- Cut six conductive foil strips sized to cover the six sensor zones per D'Auria & Persia (2014) Figure 2: (A) posterior tracheal wall, (B) right and (D) left lateral trachea and cricothyroid membrane, (C) midline cricothyroid membrane (correct incision site), (E) cricoid cartilage, and (F) lower tracheal cartilaginous ring.
- Bond each foil strip to the corresponding anatomical location on the UW hardware beneath the bicycle inner-tube skin layer where applicable because the sensors must register instrument contact at each procedural step without being visible to the trainee.
- Connect each foil strip to an Arduino Uno digital input using a matrix-scanning topology.
Phase 3: Configure the Activity Detection Engine
- Load the Arduino Uno with firmware implementing the Activity Detection Engine. Configure a 20 ms debounce interval on each sensor input to reject contact chatter and apply a 10 Hz low-pass filter to the sensor stream.
- Map the 8×8 LED matrix outputs to the six sensor zones and to procedural-state indicators. The ADE matches the time-stamped contact sequence against expert-defined "good" and "bad" activity models (temporal stochastic automata, detected with the tMagic algorithm); the specific model definitions are not published in source.
- Validate the overlay by performing a dry run of the six-step procedure and confirming that each step triggers the expected LED response.
Checkpoint: CPS Verification
- Sensor response: instrument contact on each of the six foil strips (A–F) registers on the microcontroller and updates the LED display — pass/fail
- Debounce behaviour: no spurious activations from momentary instrument brush — pass/fail
- Sequence detection: ADE advances through procedural states in correct order when the six steps are performed in sequence — pass/fail
References
- ↑ D'Auria D, Persia F. "Automatic evaluation of medical doctors' performances while using a cricothyrotomy simulator." 2014 IEEE 15th International Conference on Information Reuse and Integration (IRI 2014), Redwood City, CA, 13–15 August 2014. DOI: 10.1109/IRI.2014.7051932.
- ↑ D'Auria D, Persia F. "A Framework for Real-Time Evaluation of Medical Doctors' Performances While Using a Cricothyrotomy Simulator." In: Data Management Technologies and Applications. DATA 2014. Communications in Computer and Information Science, vol. 178. Springer, Cham, 2015. DOI: 10.1007/978-3-319-25936-9_12.
- ↑ White L, Bly R, D'Auria D, Aghdasi N, Bartell P, Cheng L, Hannaford B. "Cricothyrotomy simulator with computational skill assessment for procedural skill training in the developing world." Journal of Otolaryngology — Head and Neck Surgery, 2014. Cited in D'Auria & Persia 2014 as ref [12]; venue as cited; full text not accessed.
- ↑ White L, Bly R, D'Auria D, Aghdasi N, Bartell P, Cheng L, Hannaford B. "Cricothyrotomy Simulator with Computational Skill Assessment for Procedural Skill Training in the Developing World." AAO-HNSF Annual Meeting and OTO Expo, Vancouver, BC, September 2013. DOI: 10.1177/0194599813495815a83 (abstract). Cited in D'Auria & Persia 2014 as ref [11].
- ↑ White L, D'Auria D, Bly R, Bartell P, Aghdasi N, Jones C, Hannaford B. "Cricothyrotomy simulator training for the developing word." In: 2012 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, October 2012. Cited in D'Auria & Persia 2014 as ref [13]; hardware design precursor.
| Authors | Arturopelayo |
|---|---|
| License | CC-BY-SA-4.0 |
| Cite as | Arturopelayo (2026). "TissueDB/Simulators/Cricothyrotomy Simulator (D'Auria)". Appropedia. Retrieved June 4, 2026. |