Smart Steering Wheel for Improving Driver’s Safety Using Internet of Things

Raja, S. Pravinth; Prince, P. Blessed; Lovesum, S. P. Jeno

doi:10.1007/s42979-022-01636-6

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The MQ3 sensor is commonly used to measure drivers' alcohol concentration when discussing vehicle-based counter measures. For example, Carranza et al [34], Vignesh et al [35], and Pravinth Raja [16] utilize the MQ3 sensor in their studies to detect alcohol levels. For first-time usage, the sensor must be fully warmed up for 24-48 hours to ensure maximum accuracy.…”

Section: Figure 3 System Block Diagrammentioning

confidence: 99%

“…Recent advancements in vehicle technology offer new possibilities for minimizing the occurrence of drunk driving [14], including driving performance monitoring systems, driver monitoring systems, and passive alcohol detection systems, as well as the application of the Internet of Things [15,16]. While the driving performance and driver monitoring systems are aimed at detecting dangerous behaviors that lead to road accidents, the passive alcohol detection system employs sensors built into a vehicle to passively identify whether the driver is intoxicated.…”

Section: Introductionmentioning

confidence: 99%

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Preventing Impaired Driving Using IoT on Steering Wheels Approach

Abdul Razak,

Yogarayan,

Ullah

2024

HighTech. Innov. J.

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To drive safely, one must be attentive, coordinated, have good judgment, and be able to respond quickly to changing conditions. In certain countries, improving safety may depend largely on reducing the number of impaired drivers on the road. Therefore, solutions are required to reduce the risk that is posed on the road by drivers who have been consuming alcohol while driving. Previous research has proposed the use of sensors for detecting driver impairment caused by alcohol intoxication. However, relying on a gas sensor alone may not be appropriate for detection. To reduce drunk driving, this study proposes an Internet of Things (IoT)-based tool that measures heart rate and analyzes the breath of a driver for traces of alcohol. The tool represents a vehicle that is made up of a DC motor. In the circumstance that the tool detects a higher than resting heart rate in the driver as well as an amount of alcohol in the driver’s breath sample, the tool will immediately power down the DC motor and send an SMS to the registered emergency contact with the driver’s precise position using the GPS module. The initial prototype demonstrates the tool as a potential aftermarket accessory for vehicles. The implication of this paper is that the designed tool might be of practical use to researchers in their attempts to determine and obtain information on alcohol intoxication. Doi: 10.28991/HIJ-2024-05-02-012 Full Text: PDF

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Section: Figure 3 System Block Diagrammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preventing Impaired Driving Using IoT on Steering Wheels Approach

Abdul Razak,

Yogarayan,

Ullah

2024

HighTech. Innov. J.

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Smart Highway for Riders to Forecast Weather and Accidents

et al. 2023

2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)

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A Multi Feature Fusion Search for Facial Expression Recognition Based on Classical Extraction Methods

Appati,

Wunake

2024

Preprint

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The ability to recognize emotional expressions from faces has become an essential component of human-computer interaction (HCI). Recently Oriented FAST and rotated BRIEF (ORB) and Local Binary Patterns (LBP) was used to overcome the limitations of DNN excessive hardware specifications requirements, considering the low hardware specifications used in real-world scenarios. There still exists drawbacks with LBP and ORB, in that LBP is not as resistant to image noise. LBP descriptors are invariant to changing lighting conditions and partial occlusion. Also, when a fixed threshold is utilized under challenging lighting conditions, the ORB algorithm is constrained by its incapacity to extract feature points. We propose a Multi Feature Fusion For Facial Expression Recognition using the algorithms Scale Invariant Feature Transform (SIFT), Histogram Oriented Gradient (HOG), ORB, and LBP. This study proposes a combinatorial blending of least three of these algorithms by looking at the merits of one over the other, also to obtain a novel technique out of the combinatorial schemes, and still obtain better performance of the recognition rates. The proposed method was evaluated on the Extended Cohn Kanade (CK+) and Japanese Famele Facial Expression (JAFFE), and the 2013 Facial Expression Recognition (FER2013) datasets. Based on the merits of our proposed feature extraction schemes, this study explored their respective feature extractions to obtain their individual extracted features from the descriptors. The individual features were then fused together to obtain our multi fused feature, the fused features were then passed onto the classifier for training of our models and image recognitions tasks. This study showed that the proposed algorithm performed well compared to existing state of the art.

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Smart Steering Wheel for Improving Driver’s Safety Using Internet of Things

Cited by 3 publications

References 13 publications

Preventing Impaired Driving Using IoT on Steering Wheels Approach

Preventing Impaired Driving Using IoT on Steering Wheels Approach

Smart Highway for Riders to Forecast Weather and Accidents

A Multi Feature Fusion Search for Facial Expression Recognition Based on Classical Extraction Methods

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