MERIT: Multimodal Wearable Vital Sign Waveform Monitoring
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916489952493568 |
|---|---|
| author | Tang, Yongyang Chen, Zhe Li, Ang Zheng, Tianyue Lin, Zheng Xu, Jia Lv, Pin Sun, Zhe Gao, Yue |
| author_facet | Tang, Yongyang Chen, Zhe Li, Ang Zheng, Tianyue Lin, Zheng Xu, Jia Lv, Pin Sun, Zhe Gao, Yue |
| contents | Cardiovascular disease (CVD) is the leading cause of death and premature mortality worldwide, with occupational environments significantly influencing CVD risk, underscoring the need for effective cardiac monitoring and early warning systems. Existing methods of monitoring vital signs require subjects to remain stationary, which is impractical for daily monitoring as individuals are often in motion. To address this limitation, we propose MERIT, a multimodality-based wearable system designed for precise ECG waveform monitoring without movement restrictions. Daily activities, involving frequent arm movements, can significantly affect sensor data and complicate the reconstruction of accurate ECG signals. To mitigate motion impact and enhance ECG signal reconstruction, we introduce a deep independent component analysis (Deep-ICA) module and a multimodal fusion module. We conducted experiments with 15 subjects. Our results, compared with commercial wearable devices and existing methods, demonstrate that MERIT accurately reconstructs ECG waveforms during various office activities, offering a reliable solution for fine-grained cardiac monitoring in dynamic environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00392 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MERIT: Multimodal Wearable Vital Sign Waveform Monitoring Tang, Yongyang Chen, Zhe Li, Ang Zheng, Tianyue Lin, Zheng Xu, Jia Lv, Pin Sun, Zhe Gao, Yue Systems and Control Hardware Architecture Cardiovascular disease (CVD) is the leading cause of death and premature mortality worldwide, with occupational environments significantly influencing CVD risk, underscoring the need for effective cardiac monitoring and early warning systems. Existing methods of monitoring vital signs require subjects to remain stationary, which is impractical for daily monitoring as individuals are often in motion. To address this limitation, we propose MERIT, a multimodality-based wearable system designed for precise ECG waveform monitoring without movement restrictions. Daily activities, involving frequent arm movements, can significantly affect sensor data and complicate the reconstruction of accurate ECG signals. To mitigate motion impact and enhance ECG signal reconstruction, we introduce a deep independent component analysis (Deep-ICA) module and a multimodal fusion module. We conducted experiments with 15 subjects. Our results, compared with commercial wearable devices and existing methods, demonstrate that MERIT accurately reconstructs ECG waveforms during various office activities, offering a reliable solution for fine-grained cardiac monitoring in dynamic environments. |
| title | MERIT: Multimodal Wearable Vital Sign Waveform Monitoring |
| topic | Systems and Control Hardware Architecture |
| url | https://arxiv.org/abs/2410.00392 |