Complete Cardiorespiratory Monitoring via Wearable Ultra Low Power Ultrasound
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Date
2023Type
- Conference Paper
ETH Bibliography
yes
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Abstract
The continuous monitoring of heart (HR) and respiration (RR) rates is crucial for long-term health assessment and physical activity tracking. HR can be extracted reliably by wearable sensors based on electrocardiography (ECG) and featuring multi-electrode setups. In turn, photoplethysmography (PPG) offers similar HR detection accuracy levels with minimal setups but is affected by motion artifacts. Furthermore, while PPG and ECG may also be utilized for indirect RR assessment, they typically result in moderate accuracy. In this context, ultrasound (US) is a valid tool for complete cardiorespiratory monitoring with a single sensor. In fact, instead of probing electro-optical signals near the skin’s surface, its penetrating ability allows accurate tracking of both ventilation and heartbeat frequencies. However, existing solutions based on the US do not typically offer concurrent HR and RR detection or are not available in wearable form factors and power budgets. In this paper, we demonstrate full cardiorespiratory assessment (HR and RR measurements) by means of a single chest-worn wearable ultra low power (ULP) US probe. The proposed methodology, based on the frequency analysis of sequences of A-mode scans, enables a reliable HR and RR assessment with less than 3.9% and 4.6% errors when compared to ECG (for HR measurements) and manual counting (for RR measurements), respectively. The proposed approach does not require precise positioning of the probe on the chest, offering robustness with respect to user misplacements of the sensor. The system consumes only 17 mW, thereby enabling long-term monitoring. Show more
Publication status
publishedExternal links
Book title
2023 IEEE International Ultrasonics Symposium (IUS)Pages / Article No.
Publisher
IEEEEvent
Subject
Heart rate; Respiration rate; Energy efficiency; Ultrasonics; Wearable healthcareOrganisational unit
03996 - Benini, Luca / Benini, Luca
Funding
193813 - PEDESITE: Personalized Detection of Epileptic Seizure in the Internet of Things (IoT) Era (SNF)
ETH-C-01 21-2 - ListenToLight: a Smart Compact Optoacoustic System (ETHZ)
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