移动端实时疲劳驾驶检测

Driver fatigue detection based on eye state analysis

An Implementation of Fatigue Driving Detect, Huawei Cloud Track, 18th Challenge Cup

🧠 + 🚗 Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system

fatigue detect rknn/onnx model deploy in rk3568 npu（ROCK 3A）

Neural architecture search framework based on reinforcement learning:"A Novel Approach to Detecting Muscle Fatigue Based on sEMG by Using Neural Architecture Search Framework"

An improved multiscale sample entropy algorithm to extract signal features: "Analysis of fatigue in the biceps brachii by using rapid refined composite multiscale sample entropy"

This is the repository of the journal paper: Neural networks for fatigue crack propagation predictions in real-time under uncertainty - Giannella et al.

Real-time driver drowsiness and distraction detection system. EU regulation C(2023)4523 compliant gaze zone detection.

Implementation of a system to detect fatigue using EAR (Eye Aspect Ratio).

Human Facial Fatigue Detection through 3D ResNet with Non-local Attention

demo repository for CODE-AI-2022 project. Contains pre-build and pre-release version of project code.

疲劳闭眼检测demo

Fatigue detection using blinking frequency and duration

DFDS is a raspberry pi system that detects driver's fatigue and alerts driver through audio visual ques .The system also sends signal to android app which sends distress message to a predefined contact

Non-invasive ECG monitoring system for astronauts that extracts heart rate, breathing rate, QT interval, and heartbeat duration from a single signal source. Uses specialized filtering circuits to detect fatigue states in real-time while maintaining mobility. Validated with exercise-induced exhaustion trials.

A real-time driver fatigue detection system using computer vision. This project monitors eye movements to detect drowsiness and alerts the driver with a visual and audible warning when fatigue is detected. Built with OpenCV, dlib, and Python, it's designed for enhancing driver safety.

Driver Behavior Collection Dataset

[WIP] - SUTD 2020 10.009 The Digital World 1D Project

No-Nap Drive is a real-time driver drowsiness detection system using computer vision and deep learning. It extracts Eye Aspect Ratio (EAR) via MediaPipe, analyzes temporal patterns using a 3-class LSTM model (Alert, Drowsy, Critical), and triggers severity-based alerts to help prevent microsleep-related accidents.