Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks
The measurement and analysis of vital signs are a subject of significant research interest, particularly for monitoring the driver’s physiological state, which is of crucial importance for road safety. Various approaches have been proposed using contact techniques to measure vital signs. However, al...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/23/9457 |
| _version_ | 1797399604011991040 |
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| author | Amal El Abbaoui David Sodoyer Fouzia Elbahhar |
| author_facet | Amal El Abbaoui David Sodoyer Fouzia Elbahhar |
| author_sort | Amal El Abbaoui |
| collection | DOAJ |
| description | The measurement and analysis of vital signs are a subject of significant research interest, particularly for monitoring the driver’s physiological state, which is of crucial importance for road safety. Various approaches have been proposed using contact techniques to measure vital signs. However, all of these methods are invasive and cumbersome for the driver. This paper proposes using a non-contact sensor based on continuous wave (CW) radar at 24 GHz to measure vital signs. We associate these measurements with distinct temporal neural networks to analyze the signals to detect and extract heart and respiration rates as well as classify the physiological state of the driver. This approach offers robust performance in estimating the exact values of heart and respiration rates and in classifying the driver’s physiological state. It is non-invasive and requires no physical contact with the driver, making it particularly practical and safe. The results presented in this paper, derived from the use of a 1D Convolutional Neural Network (1D-CNN), a Temporal Convolutional Network (TCN), a Recurrent Neural Network particularly the Bidirectional Long Short-Term Memory (Bi-LSTM), and a Convolutional Recurrent Neural Network (CRNN). Among these, the CRNN emerged as the most effective Deep Learning approach for vital signal analysis. |
| first_indexed | 2024-03-09T01:43:31Z |
| format | Article |
| id | doaj.art-e609bb9d1bc045668b01cf0579937c6e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:43:31Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e609bb9d1bc045668b01cf0579937c6e2023-12-08T15:26:04ZengMDPI AGSensors1424-82202023-11-012323945710.3390/s23239457Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural NetworksAmal El Abbaoui0David Sodoyer1Fouzia Elbahhar2COSYS-LEOST, University Gustave Eiffel, F-59650 Villeneuve d’Ascq, FranceCOSYS-LEOST, University Gustave Eiffel, F-59650 Villeneuve d’Ascq, FranceCOSYS-LEOST, University Gustave Eiffel, F-59650 Villeneuve d’Ascq, FranceThe measurement and analysis of vital signs are a subject of significant research interest, particularly for monitoring the driver’s physiological state, which is of crucial importance for road safety. Various approaches have been proposed using contact techniques to measure vital signs. However, all of these methods are invasive and cumbersome for the driver. This paper proposes using a non-contact sensor based on continuous wave (CW) radar at 24 GHz to measure vital signs. We associate these measurements with distinct temporal neural networks to analyze the signals to detect and extract heart and respiration rates as well as classify the physiological state of the driver. This approach offers robust performance in estimating the exact values of heart and respiration rates and in classifying the driver’s physiological state. It is non-invasive and requires no physical contact with the driver, making it particularly practical and safe. The results presented in this paper, derived from the use of a 1D Convolutional Neural Network (1D-CNN), a Temporal Convolutional Network (TCN), a Recurrent Neural Network particularly the Bidirectional Long Short-Term Memory (Bi-LSTM), and a Convolutional Recurrent Neural Network (CRNN). Among these, the CRNN emerged as the most effective Deep Learning approach for vital signal analysis.https://www.mdpi.com/1424-8220/23/23/9457vital signsCW radarheart and respiration ratephysiological statetemporal neural networksBi-LSTM |
| spellingShingle | Amal El Abbaoui David Sodoyer Fouzia Elbahhar Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks Sensors vital signs CW radar heart and respiration rate physiological state temporal neural networks Bi-LSTM |
| title | Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks |
| title_full | Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks |
| title_fullStr | Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks |
| title_full_unstemmed | Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks |
| title_short | Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks |
| title_sort | contactless heart and respiration rates estimation and classification of driver physiological states using cw radar and temporal neural networks |
| topic | vital signs CW radar heart and respiration rate physiological state temporal neural networks Bi-LSTM |
| url | https://www.mdpi.com/1424-8220/23/23/9457 |
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