scholarly journals Blink Fatigue Detection Algorithm Based on Improved Lenet-5

2019 ◽  
Author(s):  
Lei Chao ◽  
Wang Changyuan ◽  
Lin Zhi ◽  
Huang Wenbo
Keyword(s):  
Detection Algorithm ◽  
Fatigue Detection

scholarly journals Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection

2019 ◽  
Vol 11 (5) ◽  
pp. 115 ◽  
Author(s):  
Weihuang Liu ◽  
Jinhao Qian ◽  
Zengwei Yao ◽  
Xintao Jiao ◽  
Jiahui Pan
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Feature Fusion ◽  
Facial Feature ◽  
Detection Algorithm ◽  
Facial Features ◽  
Dynamic Features ◽  
Driver Fatigue ◽  
Stream Network ◽  
Fatigue Detection

Road traffic accidents caused by fatigue driving are common causes of human casualties. In this paper, we present a driver fatigue detection algorithm using two-stream network models with multi-facial features. The algorithm consists of four parts: (1) Positioning mouth and eye with multi-task cascaded convolutional neural networks (MTCNNs). (2) Extracting the static features from a partial facial image. (3) Extracting the dynamic features from a partial facial optical flow. (4) Combining both static and dynamic features using a two-stream neural network to make the classification. The main contribution of this paper is the combination of a two-stream network and multi-facial features for driver fatigue detection. Two-stream networks can combine static and dynamic image information, while partial facial images as network inputs can focus on fatigue-related information, which brings better performance. Moreover, we applied gamma correction to enhance image contrast, which can help our method achieve better results, noted by an increased accuracy of 2% in night environments. Finally, an accuracy of 97.06% was achieved on the National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset.

scholarly journals Application Analysis of Wearable Technology and Equipment Based on Artificial Intelligence in Volleyball

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xianyan Dai ◽  
Shangbin Li
Keyword(s):  
Real Time ◽  
Sports Injuries ◽  
Recognition Performance ◽  
Wearable Technology ◽  
Detection Algorithm ◽  
The Body ◽  
Self Assessment ◽  
Ecg Signals ◽  
Exercise Fatigue ◽  
Fatigue Detection

Today, while people’s satisfaction with materials is high, the pursuit of health has begun and sports are becoming increasingly important. Volleyball is a good physical and mental exercise, which helps improve the health of the body. However, excessive exercise usually leads to muscle strain and more serious accidents. Therefore, how to effectively prevent excessive fatigue and sports injuries becomes more and more important. In the past, some methods of exercise fatigue detection were mostly self-assessment through some indicators, which lacked real-time and accuracy. With the advancement of smart technology, in order to better detect sports fatigue, smart wearable technology and equipment are used in volleyball. Firstly, surface electromyography signals (sEMG) are collected through wearable technology and equipment. Secondly, the signal is preprocessed to extract features that are conducive to exercise fatigue assessment. Finally, a motion fatigue detection algorithm is designed to identify and classify features and evaluate the motion status in real-time. The simulation results show that it is feasible to collect ECG signals and EMG signals to detect exercise fatigue. The algorithm has good recognition performance, can evaluate exercise conditions in real-time, and prevent fatigue and injury during exercise.

Spatial-Temporal Feature Representation Learning for Facial Fatigue Detection

2018 ◽  
Vol 32 (12) ◽  
pp. 1856018
Author(s):  
Changyuan Wang ◽  
Ting Yan ◽  
Hongbo Jia
Keyword(s):  
Network Model ◽  
Network Structure ◽  
Short Term Memory ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Feature Representation ◽  
Video Sequences ◽  
Lstm Network ◽  
Fatigue Detection ◽  
Inter Frame

In order to reduce the serious problems caused by the operators’ fatigue, we propose a novel network model Convolutional Neural Network and Long Short-Term Memory Network (CNN-LSTM) — for fatigue detection in the inter-frame images of video sequences, which mainly consists of CNN and LSTM network. Firstly, in order to improve the accuracy of the deep network structure, the Viola–Jones detection algorithm and the Kernelized Correlation Filter (KCF) tracking algorithm are used in the face detection to normalize the size of the inter-frame images of video sequences. Secondly, we use the CNN and the LSTM network to detect the fatigue state in real time and efficiently. The fatigue-related facial features are extracted by the CNN. Then, the temporal symptoms of the whole fatigue process can be extracted by LSTM networks, the input data which is the facial feature vector can be obtained by the CNN. Thirdly, we train and test the network in a step-by-step approach. Finally, we experiment with the proposed network model. The experimental results demonstrate that the network structure can effectively detect the fatigue state, and the overall accuracy rate can rise to 82.8%.

Driver Fatigue Detection Algorithm Research Based on the Characteristics of Eyes

2014 ◽  
Vol 701-702 ◽  
pp. 30-35 ◽  
Author(s):  
Qin Wang ◽  
Lan Tang ◽  
Kun Yang
Keyword(s):  
Warning System ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Driver Fatigue ◽  
Infrared Video ◽  
The Face ◽  
Eyes Open ◽  
Texture Detection ◽  
Opening And Closing ◽  
Fatigue Detection

In driver fatigue warning system, it is a very effective method for detecting Driver fatigue state through the driver's facial expressions and body movements. The main content of this article is to detect the two basic states of the eyes opening and closing and presents the LBP texture detection operator. Firstly we get the face image sequences using infrared video and extract the eye region using ADABOOST. The SVM is used in classifying feature vector of the eyes open and closed detecting of driver fatigue. A large number of experimental results show that the proposed method has high detection accuracy and timeliness.

Eye Statement Recognition for Driver Fatigue Detection Based on Gabor Wavelet and HMM

2011 ◽  
Vol 128-129 ◽  
pp. 123-129
Author(s):  
Hai Yan Yang ◽  
Xin Hua Jiang ◽  
Lei Wang ◽  
Yong Hui Zhang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Good Accuracy ◽  
Hidden Markov ◽  
Detection Algorithm ◽  
Gabor Wavelet ◽  
Recognition Method ◽  
Driver Fatigue ◽  
Gabor Features ◽  
Fatigue Detection

Eye statement is one of the most important factors reflecting driver fatigue. A novel eye statement recognition method for driver fatigue detection based on Gabor transformation and Hidden Markov Model is proposed in this paper, in which, the eye detection algorithm is borrowed from Zafer Savas' TrackEye software, and Gabor features, i.e. the eye state features, of the eye are extracted by using Gabor wavelet. After that, by using these features, the classifier is trained by HMM (Hidden Markov Model) to distinguish the eye states including fatigue and alert, then the consecutive five frames are considered to judge whether there exists driver fatigue or not. Simulation results show that the new method has good accuracy and effectiveness.

scholarly journals Removal of ECG Artifacts Affects Respiratory Muscle Fatigue Detection (A Simulation Study)

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5663
Author(s):  
Lorenz Kahl ◽  
Ulrich G. Hofmann
Keyword(s):  
Signal Processing ◽  
Evaluation Criteria ◽  
Approximate Entropy ◽  
Detection Algorithm ◽  
Respiratory Muscle Fatigue ◽  
Ecg Signals ◽  
Detection Algorithms ◽  
Emg Signal ◽  
Fatigue Detection ◽  
Semg Signals

This work investigates elimination methods for cardiogenic artifacts in respiratory surface electromyographic (sEMG) signals and compares their performance with respect to subsequent fatigue detection with different fatigue algorithms. The analysis is based on artificially constructed test signals featuring a clearly defined expected fatigue level. Test signals are additively constructed with different proportions from sEMG and electrocardiographic (ECG) signals. Cardiogenic artifacts are eliminated by high-pass filtering (HP), template subtraction (TS), a newly introduced two-step approach (TSWD) consisting of template subtraction and a wavelet-based damping step and a pure wavelet-based damping (DSO). Each method is additionally combined with the exclusion of QRS segments (gating). Fatigue is subsequently quantified with mean frequency (MNF), spectral moments ratio of order five (SMR5) and fuzzy approximate entropy (fApEn). Different combinations of artifact elimination methods and fatigue detection algorithms are tested with respect to their ability to deliver invariant results despite increasing ECG contamination. Both DSO and TSWD artifact elimination methods displayed promising results regarding the intermediate, “cleaned” EMG signal. However, only the TSWD method enabled superior results in the subsequent fatigue detection across different levels of artifact contamination and evaluation criteria. SMR5 could be determined as the best fatigue detection algorithm. This study proposes a signal processing chain to determine neuromuscular fatigue despite the presence of cardiogenic artifacts. The results furthermore underline the importance of selecting a combination of algorithms that play well together to remove cardiogenic artifacts and to detect fatigue. This investigation provides guidance for clinical studies to select optimal signal processing to detect fatigue from respiratory sEMG signals.

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