https://github.com/chewzzz1014/csc4700-embedded-collision-detection

https://github.com/chewzzz1014/csc4700-embedded-collision-detection

adxl345 arduino collision-detection embedded embedded-systems esp32 fastapi iot machine-learning sklearn thinkspeak

Last synced: about 14 hours ago
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• Host: GitHub
• URL: https://github.com/chewzzz1014/csc4700-embedded-collision-detection
• Owner: chewzzz1014
• Created: 2025-01-04T04:38:37.000Z (15 days ago)
• Default Branch: master
• Last Pushed: 2025-01-15T02:23:34.000Z (4 days ago)
• Last Synced: 2025-01-15T03:06:15.051Z (4 days ago)
• Topics: adxl345, arduino, collision-detection, embedded, embedded-systems, esp32, fastapi, iot, machine-learning, sklearn, thinkspeak
• Language: Jupyter Notebook
• Homepage:
• Size: 96.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# csc4700-embedded-collision-detection

- Perform binary classification (Collision / No collision) using vehicle acceleration data (x,y,z axis acceleration) collected using accelerometer sensor within 3-seconds timeframe. 

- Entries collected within same timeframe is identified by an unique `batch_id`

- 5 entries of data within 3-seconds timeframe form a complete journey. All entries in the same journey will always have same `is_collision` value.

- Display real-time data collected and prediction made on ThinkSpeak dashboard (Note: ThinkSpeak has limitation of 15 seconds between requests sent)

# How to launch Trained-Model-Hosting Server

1. Activate virtual env (if any)

```

source venv/Scripts/activate

```

2. Install required packages (for first time user)

```

pip install -r requirements.txt

```

3. Run server

```

uvicorn server:app --host 0.0.0.0 --port 8000

```

## Hardware & Software Used

- ADXL345 Accelerometer

- ESP-32 Wifi Module

- ThinkSpeak (Dashboard & store dataset collected)

- Google Colab (ML)

## TODO

Code

- [x] Collect accelerometer data and upload to ThinkSpeak

- [x] Preprocess dataset downloaded from ThinkSpeak (2 channel, train data channel and test data channel)

- [x] Train and evaluate classification model

- [x] Host trained model using FastAPI

- [ ] Use trained model to make real-time prediction

- [ ] Display data and graphs on ThinkSpeak dashboard

Non-Code

- [x] tinkercard architectural diagram

- [x] flowchart for methodology

- [x] architectural diagram

- [ ] report and presentation slide