Detection and Identification of Irregularities in Human Heart Rate

Rajesh, N.; Ramachandra, A C; A, Prathibha

doi:10.1109/conit51480.2021.9498296

Cited by 2 publications

(2 citation statements)

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“…Leveraging insights from [ 25 ], our approach acquires accurate key point coordinates with MTCNN. Subsequently, these key point coordinates are used to extract images of the eye and mouth regions.…”

Section: Principles and Methodsmentioning

confidence: 99%

“…Addressing challenges related to dramatic lighting changes and significant driver head movements during driving, Nowara [ 24 ] demonstrated that narrowband near-infrared (NIR) video recordings can mitigate external light variations and yield reliable heart rate estimates. Rajesh [ 25 ] utilized the Pan–Tompkins method for R-peak detection to identify irregularities in human heart rate (DIIHR), achieving an average accuracy of 96.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

Kong,

Xie,

Niu

et al. 2024

Sensors

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Existing vision-based fatigue detection methods commonly utilize RGB cameras to extract facial and physiological features for monitoring driver fatigue. These features often include single indicators such as eyelid movement, yawning frequency, and heart rate. However, the accuracy of RGB cameras can be affected by factors like varying lighting conditions and motion. To address these challenges, we propose a non-invasive method for multi-modal fusion fatigue detection called RPPMT-CNN-BiLSTM. This method incorporates a feature extraction enhancement module based on the improved Pan–Tompkins algorithm and 1D-MTCNN. This enhances the accuracy of heart rate signal extraction and eyelid features. Furthermore, we use one-dimensional neural networks to construct two models based on heart rate and PERCLOS values, forming a fatigue detection model. To enhance the robustness and accuracy of fatigue detection, the trained model data results are input into the BiLSTM network. This generates a time-fitting relationship between the data extracted from the CNN, allowing for effective dynamic modeling and achieving multi-modal fusion fatigue detection. Numerous experiments validate the effectiveness of the proposed method, achieving an accuracy of 98.2% on the self-made MDAD (Multi-Modal Driver Alertness Dataset). This underscores the feasibility of the algorithm. In comparison with traditional methods, our approach demonstrates higher accuracy and positively contributes to maintaining traffic safety, thereby advancing the field of smart transportation.

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Section: Principles and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%