Project Info
Inspiration
Charlotte’s mom has lived with end-stage kidney disease since she was 17 and has undergone two kidney transplants, most recently after spending three years on dialysis and being told the wait could stretch to a decade. Her last transplant required coordinating a deceased-donor kidney from out of state, underscoring how rare these organs are—and how stressful and life-limiting it is to wait without knowing when a kidney will come. Between 2014 and 2019, nearly 170 donated organs were never transplanted and hundreds more faced dangerous delays due to transportation failures. Today, over 113,000 people in the U.S. are waiting for a transplant, yet viable organs are still declined because they don’t arrive on time. Government reports confirm that the most fragile point is the final stretch between donor hospital and transplant center, where visibility drops and delays occur. OrganTrail tackles this gap by detecting transport risk in real time and leveraging routing APIs to recommend the fastest, most reliable delivery path. By strengthening the last mile, we aim to prevent avoidable organ waste and give more patients a second chance at life.
What it does
OrganTrail is a multi-component organ transport monitoring system that integrates a hardware sensor module, live dashboard, clinical data layer, and AI-enabled rerouting agent: Hardware Module Built with temperature, humidity, and shock sensors clipped inside existing organ transport boxes. LED display system signals when shock events or extreme temperatures exceed organ-specific clinical thresholds. Custom PCB design ensures portability and direct integration into existing transport boxes, sealed by a Hydrophobic ePTFE vent patch + a recessed vent cavity for the sensors. Low-cost, compact, and fully self-contained for practical deployment. Live Dashboard Displays live sensor metrics: temperature, humidity, and shock readings in real time. Includes GPS-based mapping to visualize the current location and route of the organ. Provides an at-a-glance overview of transport conditions for all monitored organs. Clinical Data Integration Incorporates organ-specific preservation guidelines and ideal storage conditions. Uses historical and clinical data to contextualize sensor readings, highlighting deviations from safe thresholds for each organ type. Error Response & Rerouting Agent Detects abnormal sensor readings and triggers immediate alerts to the transport team. Uses organ donation databases and Google Maps APIs to identify and rank the next five optimal recipient destinations. Computes the most suitable patient for rerouting based on proximity, organ compatibility, and preservation urgency. Supports proactive decision-making to minimize organ spoilage due to transport delays or inefficiencies.
How we built it
Hardware Module Arduino Uno, KY-002 shock sensor, DHT11 temperature & humidity sensor, and RGB LED continuously read sensor data. Compact, portable design clips inside existing organ transport boxes. Custom PCB and enclosure reduce bulky wiring and enable direct integration. RGB LED provides immediate visual alerts when readings exceed organ-specific safe thresholds. Live Dashboard Node.js bridge server ingests sensor readings over serial or via API, broadcasting updates via WebSocket. React dashboard visualizes real-time metrics: temperature, humidity, and shock events. Leaflet/OpenStreetMap maps display live organ location, path history, and planned route snapshots. Configurable safe ranges trigger warnings and critical alerts when exceeded. Redirect triage flow suggests nearest hospitals based on critical sensor events; operators can confirm or reject rerouting. Synthetic Data & Clinical Integration Generated realistic transport streams using global hospital locations. Researched existing clinical and research data to determine optimal preservation conditions for each organ type. Alerts and rerouting decisions are informed by organ-specific temperature, ischemic time limits, and risk thresholds. Sensor Event Logging & Monitoring Shock events logged with timestamps to capture mishandling risks. Temperature and humidity deviations tracked to ensure preservation compliance. Continuous monitoring enables real-time assessment, structured data trails, and predictive analytics for future optimization.
Challenges we ran into
Sensor Limitations: Mechanical, low-cost sensors (temperature, humidity, shock) aren’t perfectly precise or robust, making it difficult to fully validate measurements under real organ-transport conditions. Mechanical & Mounting Constraints: Without access to real organ transport boxes, we couldn’t directly test mounting, condensation behavior, or thermal gradients, which are critical for accurate monitoring. Limited Clinical Data: Clinical transport and outcome datasets are restricted, so we relied on synthetic data streams to demonstrate end-to-end behavior, workflow, and alert logic rather than real-world validation.
Accomplishments we're proud of
Compact, manufacturable PCB design for low-cost sensors that integrate directly into existing organ transport boxes. AI-informed rerouting algorithm leveraging Google Maps APIs to optimize organ turnaround and reduce spoilage risk. Real-time hardware and software dashboard displaying temperature, humidity, shock, GPS location, and alert status for live monitoring.
What we learned
Workflow matters as much as sensors – clear alerts and intuitive UX are critical; raw data alone isn’t enough. Data scarcity is real – standardized organ transport datasets are limited, making even synthetic data streams valuable. Prioritizing signals is essential – working under hardware and cost constraints taught us to focus on the most meaningful metrics for decision-making. Integration is non-trivial – combining hardware, live dashboards, clinical thresholds, and rerouting logic requires careful design to be reliable in real-time.
What's next
Environment-Optimized Hardware Explore materials and enclosure designs suited for cold, wet, and condensation-prone organ transport conditions. Custom PCB Manufacturing – Produce a single compact PCB integrating current sensors and adding accelerometer/gyroscope for richer handling data. Wireless Connectivity – Transition from serial-only communication to Bluetooth and Wi-Fi for more flexible, real-time data streaming. Real-World Testing – Iterate on sensor placement and durability inside actual transport boxes. Clinical & Operational Validation – Collaborate with hospitals, clinicians, and the UNOS to validate the workflow and leverage collected data to build predictive models for safer, optimized organ transport.
OrganTrail
On-ground organ transport logistics — multi-factor error detection (temperature, shock, humidity) and GPS tracking for road-based transplant transport.
This system is designed for ground vehicles (ambulances, transport vans) moving organs between facilities.
Treehacks 2026 - Ananya, Ashley, Charlotte, Leah
Features
- Temperature monitoring — Safe range 2–6°C for cold storage
- Shock sensing — Impact/vibration detection (g-force)
- Humidity monitoring — Relative humidity %
- GPS location & path — Live position and route tracking on map
- Destination & route — Each device shows destination; click "View route" to see path on map
- Redirect triaging — On critical errors, suggests redirect to nearby hospital; operator can confirm or reject
- Alerts — Critical/warning when readings go out of range
Redirect suggestions use hospitals.csv — run npm run process-hospitals to generate public/hospitals.json from the CSV.
Quick start
1. Dashboard (React)
npm install
npm start
Opens at http://localhost:3000
2. Bridge server (serial → WebSocket)
cd server
npm install
npm start
The bridge runs on port 4000. Without a serial port, it accepts POST requests for testing.
3. Connect hardware (COM port / serial)
Set your serial port in server/.env or as an env var:
Windows:
set SERIAL_PORT=COM3
npm start
macOS:
SERIAL_PORT=/dev/cu.usbserial-10 npm start
Linux:
SERIAL_PORT=/dev/ttyUSB0 npm start
Find your port: ls /dev/cu.* (Mac) or Device Manager → Ports (Windows).
4. Microcontroller output format
Text format (one line per reading):
Shock: 0 | Temp: 21.50C | Humidity: 45.40% | Lat: 37.7749 | Lng: -122.4194
JSON format:
{"temp":4.2,"shock":0,"humidity":45.4,"lat":37.7749,"lng":-122.4194,"deviceId":"DEV-001"}
temp— temperature in °Cshock— g-force (0 if no event)humidity— relative humidity %lat,lng— GPS coordinates (optional)deviceId— optional
5. Google Maps API (for route directions)
Add your Google Maps API key to server/.env:
GOOGLE_MAPS_API_KEY=your_api_key_here
Enable the Directions API in Google Cloud Console. The View route modal will then show driving routes from Google Maps.
6. Hospitals data (for redirect suggestions)
Process hospitals.csv into JSON for the dashboard:
npm run process-hospitals
This creates public/hospitals.json. The redirect triage uses it to suggest nearest hospitals by location.
7. Test without hardware
With the bridge running:
cd server
npm run mock
Or use curl (with GPS):
curl -X POST http://localhost:4000/api/readings \
-H "Content-Type: application/json" \
-d '{"temp":4.5,"shock":0,"humidity":48,"lat":37.7749,"lng":-122.4194}'
Analysis
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Metric
- 5
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- ExpressIn code
- HTMLIn code
- JavaScriptIn code
- ReactIn code
- Node.jsClaimed
- VercelClaimed
5 of 7 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
76 KB
Source files
21
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
charava/organ-transport
37 files · 7.1 MB · @ aca840c
Structure
Interface
3 files · 8%Screens, components and styles rendered to the user.
API & routing
5 files · 14%Request entry points: routes, handlers and controllers.
Application logic
13 files · 35%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- JavaScript68%
- CSS18%
- Markdown12%
- HTML2%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
package.json
npm · 10- @testing-library/dom
- @testing-library/jest-dom
- @testing-library/react
- @testing-library/user-event
- leaflet
- react
- react-dom
- react-leaflet
- react-scripts
- web-vitals
server/package.json
npm · 5- @serialport/parser-readline
- dotenv
- express
- serialport
- ws
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
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