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TissueDB/Simulators/Cricothyrotomy Simulator (D'Auria)

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Anatomical reference diagram of the larynx — thyroid cartilage, cricothyroid ligament, cricoid cartilage and trachea — with the cricothyrotomy and tracheostomy access points labelled (a reference diagram, not a photograph of the device).
Anatomical reference: laryngeal structures (1) thyroid cartilage, (2) cricothyroid ligament, (3) cricoid cartilage, (4) trachea, with (A) cricothyrotomy and (B) tracheostomy access points labelled. Image by PhilippN, CC BY-SA 3.0 via Wikimedia Commons; based on Gray's Anatomy plate 951.

The D'Auria Cricothyrotomy Simulator is a low-cost cricothyrotomy neck trainer — built from widely available materials (a cardboard trachea, compliant-foam cartilage rings, a bicycle-inner-tube skin, and 3D-printed laryngeal cartilages) and fitted with conductive-foil landmark sensors — with a Cyber-Physical-System (CPS) overlay that adds automatic, real-time scoring of the emergency cricothyrotomy technique.[1] On the trainer the trainee performs the standard emergency cricothyrotomy — palpating and immobilising the larynx to locate the cricothyroid membrane, incising the skin vertically and then the membrane horizontally (about 1 cm), inserting a tracheal hook into the cricoid cartilage, spreading the opening with a hemostat, and passing an endotracheal tube — while the trainer's landmark sensors record each instrument contact and the overlay flags whether the technique is performed correctly and in the right order. The tissue-simulating substrate and its sensors are the University of Washington build; see Cricothyrotomy Simulator (White UW). ⚑ Open for review: this page and the White UW page describe one device from one source (D'Auria & Persia 2014) — whether to merge them (this scoring overlay folded into White UW as a subsection) or keep them split is routed to Catherine; do not merge without her ruling.

Field Details
Features and Basic Operation The University of Washington trainer carries six conductive-foil contact sensors at the neck landmarks (part of its base build) — only the midline cricothyroid-membrane zone is the correct incision site — read by an Arduino Uno that matrix-scans the foils and debounces each contact over 20 ms, with an 8×8 LED matrix showing contact state; the recorded contact stream is low-pass filtered at 10 Hz. D'Auria & Persia's contribution is the software layer: an Activity Detection Engine matches the time-stamped contact sequence against expert-defined "good" and "bad" activity models (temporal stochastic automata, detected with the tMagic algorithm) to give immediate correct/incorrect feedback; in a reported 100-doctor trial the engine achieved 81% precision and 98% recall on activity recognition.
Current Development Status Evaluated in a peer-reviewed engineering study; the scoring engine's recognition accuracy and user satisfaction were measured, not clinical skill transfer.
Estimated Build Time and Cost <US$50
Specialized Tools and Equipment Scalpel, tracheal hook, and hemostat — the three instruments wired into the contact-sensing circuit — plus an endotracheal tube inserted at the final step (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. ⚑ Open for review: the source models laryngotracheal cartilage (trachea/larynx), not bronchial tissue — the trachea-class link is routed to Felipe for the whole cricothyrotomy cluster.


Structural Parts

Part Name Qty Material Cost Notes
Arduino Uno 1 Microcontroller board (Atmel Atmega 328) — Part of the UW base build; runs D'Auria & Persia's Activity Detection Engine firmware, 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 (part of the UW base build); 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 matrix display — Part of the UW base build; shows sensor-contact state and procedural-step feedback in real time.


Build Instructions

Phase 1: Prepare the UW Hardware Substrate

  1. Build or acquire the UW BioRobotics cricothyrotomy trainer per Cricothyrotomy Simulator (White UW) 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, the bicycle inner-tube skin layer, the six conductive-foil sensors, and the Arduino Uno + 8×8 LED matrix.
  2. Confirm that the cartilage landmarks are palpable through the skin layer and that the cardboard trachea accommodates an endotracheal tube.

Phase 2: Confirm the Sensor Map

  1. Confirm the six conductive-foil sensor zones are in place on the UW hardware 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.
  2. Confirm each foil is wired to an Arduino Uno digital input using a matrix-scanning topology.

Phase 3: Configure the Activity Detection Engine (the D'Auria overlay)


Arduino Uno R3 microcontroller board
Arduino Uno R3 microcontroller board (Atmel ATmega328 microprocessor). D'Auria & Persia (2014) describe using this board class — part of the UW base build — to matrix-scan the six conductive-foil sensors and drive the LED feedback display. Image by Mr Revolution, CC BY 3.0.
  1. Load the Arduino Uno with firmware implementing the Activity Detection Engine, and set a 20 ms debounce interval on each sensor input to reject contact chatter. The recorded contact stream is low-pass filtered at 10 Hz in later processing, in line with general human reaction time.


8x8 LED dot-matrix display module
8×8 LED dot-matrix display module (illuminated). D'Auria & Persia (2014) describe using this display class to render sensor-contact state and procedural-step indicators in real time. Image by Shahbaz75, CC BY-SA 4.0.
  1. Map the 8×8 LED matrix outputs to the six sensor zones and to procedural-state indicators. The Activity Detection Engine 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.
  2. 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: the Activity Detection Engine advances through procedural states in correct order when the six steps are performed in sequence — pass/fail



References

[2][3][4][5]

  1. ↑ 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), 13–15 August 2014, pp. 514–519. DOI: 10.1109/IRI.2014.7051932.
  2. ↑ 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. Springer, Cham, 2015, pp. 182–198. DOI: 10.1007/978-3-319-25936-9_12.
  3. ↑ 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.
  4. ↑ 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].
  5. ↑ 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.




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Keywords cricothyrotomy, emergency airway, automatic skill assessment, cyber-physical system, conductive foil sensors, Arduino, activity detection, procedural skill training, low-cost simulator, D'Auria, TissueDB
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Authors Arturopelayo
License CC-BY-SA-4.0
Language English (en)
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Created April 10, 2026 by Arturo Pelayo
Last edit June 22, 2026 by Arturo Pelayo
Cite as Arturopelayo (2026). "TissueDB/Simulators/Cricothyrotomy Simulator (D'Auria)". Appropedia. Retrieved June 24, 2026.
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