Book ChapterDOI
Driver Drowsiness Detection System Using Conventional Machine Learning
Radheswarreddy Madireddy,Dulla Sai Krishna Anudeep,S. S. Poorna,K Anuraj,M. Gokul Krishna,Ankisetty Balaji,Dammuru Jaideep Venkat +6 more
- pp 407-415
TLDR
In this paper, a non-intrusive drowsiness detection system is implemented, which alerts the driver on the onset of Drowsiness using two machine learning techniques, namely LDA and SVM.Abstract:
Forewarning drowsy drivers can reduce the number of road accidents. A non-intrusive drowsiness detection system is implemented, which alerts the driver on the onset of drowsiness. A Pi camera module attached to Raspberry Pi is used to acquire and process the live video of the driver. Haar face detector in OpenCV is used for face detection followed by 68 points of facial landmark identification. Eye and Mouth Aspect Ratios, blink rate and yawning rate are the features extracted. Drowsiness detection is done using two methodologies viz. a threshold-based one and the other, employing artificial intelligence. The machine learning techniques used are LDA and SVM. Feedback is provided as an alarm if a driver is found to be drowsy. The analysis shows that machine learning-based techniques viz. LDA and SVM outperform threshold technique for the dataset considered.read more
Citations
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Proceedings ArticleDOI
A Comparative Study of Drowsiness Detection From Eeg Signals Using Pretrained CNN Models
Budhi Veera Bharath Chandra,Chigurupati Naveen,Mahapatra Medha Sampath Kumar,Madhavarapu Srinivasa Sai Bhargav,S S Poorna,K Anuraj +5 more
TL;DR: In this paper, the EEG signals were acquired using a 14-channel wireless headset, while they were in a virtual driving environment, and the EEG signal was segmented, and pre-processed.
Journal ArticleDOI
A CNN-Based Wearable System for Driver Drowsiness Detection
TL;DR: In this paper , a lightweight convolution neural network was used to measure eye closure based on eye images captured by a wearable glass prototype, which features a hot mirror-based design that allows the camera to be installed on the glass temples.
Proceedings ArticleDOI
Throughput Analysis with Effect of Dimensionality Reduction on 5G Dataset using Machine Learning and Deep Learning Models
TL;DR: In this article , the problem is analyzed as a regression problem and hence regressor models are applied to the problem and the results show that the top performing models are consistent in performance measured using the regression metrics.
Book ChapterDOI
Comparative Analysis of Machine Learning and Deep Learning Algorithms for Real-Time Posture Detection to Prevent Sciatica, Kyphosis, Lordosis
TL;DR: In this article , the authors used Convolutional Neural Network and K-Nearest Neighbor machine learning algorithms to predict the correct sitting postures to prevent sciatica, Kyphosis, and lordosis health issues.
Book ChapterDOI
Modelling 5G Data Using Tree-Based Machine Learning Models
TL;DR: In this paper , the throughput obtained under various conditions is analyzed as a regression model in machine learning with the features as continuous variables, and it is observed that the newer tree machine learning models are performing better on the dataset than the traditional tree models.
References
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TL;DR: The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life, and exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background.
Proceedings ArticleDOI
One Millisecond Face Alignment with an Ensemble of Regression Trees
Vahid Kazemi,Josephine Sullivan +1 more
TL;DR: It is shown how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions.
Journal ArticleDOI
Driver Behavior Analysis for Safe Driving: A Survey
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Journal ArticleDOI
Blink-related momentary activation of the default mode network while viewing videos
Tamami Nakano,Tamami Nakano,Makoto Kato,Yusuke Morito,Seishi Itoi,Shigeru Kitazawa,Shigeru Kitazawa +6 more
TL;DR: The results suggest that eyeblinks are actively involved in the process of attentional disengagement during a cognitive behavior by momentarily activating the default-mode network while deactivating the dorsal attention network.
Journal ArticleDOI
A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.
TL;DR: The proposed method to detect drowsiness in drivers which integrates features of electrocardiography and electroencephalography to improve detection performance demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone.