Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor
This work presents a study on users’ attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/20/8047 |
| _version_ | 1797469925230510080 |
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| author | Pragya Sharma Zijing Zhang Thomas B. Conroy Xiaonan Hui Edwin C. Kan |
| author_facet | Pragya Sharma Zijing Zhang Thomas B. Conroy Xiaonan Hui Edwin C. Kan |
| author_sort | Pragya Sharma |
| collection | DOAJ |
| description | This work presents a study on users’ attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user’s baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively |
| first_indexed | 2024-03-09T19:29:51Z |
| format | Article |
| id | doaj.art-8d6f6dcdf0db46f38bbdc6f82d83e12d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:29:51Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8d6f6dcdf0db46f38bbdc6f82d83e12d2023-11-24T02:31:00ZengMDPI AGSensors1424-82202022-10-012220804710.3390/s22208047Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency SensorPragya Sharma0Zijing Zhang1Thomas B. Conroy2Xiaonan Hui3Edwin C. Kan4School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USASchool of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USASchool of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USASchool of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USASchool of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USAThis work presents a study on users’ attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user’s baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectivelyhttps://www.mdpi.com/1424-8220/22/20/8047attention detectionradio frequencyvigilancevital signswearable sensor |
| spellingShingle | Pragya Sharma Zijing Zhang Thomas B. Conroy Xiaonan Hui Edwin C. Kan Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor Sensors attention detection radio frequency vigilance vital signs wearable sensor |
| title | Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor |
| title_full | Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor |
| title_fullStr | Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor |
| title_full_unstemmed | Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor |
| title_short | Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor |
| title_sort | attention detection by heartbeat and respiratory features from radio frequency sensor |
| topic | attention detection radio frequency vigilance vital signs wearable sensor |
| url | https://www.mdpi.com/1424-8220/22/20/8047 |
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