Continuous respiratory monitoring is essential for the early detection and management of respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and sleep apnea. Traditional methods, including spirometry and polysomnography, remain the gold standard but present limitations such as high costs, invasiveness, and the requirement for trained personnel. This study proposes a multi-modal respiratory monitoring system integrating bioimpedance (BIOZ) and acoustic sensing for non-invasive, continuous, and realtime respiratory assessment. The system uses the SENSIPLUS microchip (developed by Sensichips s.r.l.) for bioimpedance measurements and a MEMS (Micro Electro-Mechanical System) microphone for capturing respiration sounds. The experimental validation was conducted by comparing these methods with a spirometer as the reference standard. Results demonstrated a strong correlation between the BIOZ and the acousticderived signals with spirometric data. The Root Mean Square Error (RMSE) values with respect to a gold standard, for inspiration and expiration using bioimpedance were 0.174 s and 0.190 s, respectively, while the acoustic-based approach yielded RMSE values of 0.4136 s for inspiration and 0.2217 s for expiration. These findings highlight the feasibility of integrating bioimpedance and acoustic sensing for accurate and wearable respiratory monitoring, offering potential applications in telemedicine, personalized healthcare, remote patient monitoring, early disease detection, and smart healthcare solutions.

Multi-modal respiratory monitoring using SENSIPLUS chip bioimpedance measurements and acoustic sensors / Giannini, Lorenzo; Asquini, Rita; Contardi, Simone; Ria, Andrea; Piuzzi, Emanuele. - (2025), pp. 1-6. (Intervento presentato al convegno 2025 IEEE International Symposium on Medical Measurements and Applications - MeMeA 2025 tenutosi a Chania; Greece).

Multi-modal respiratory monitoring using SENSIPLUS chip bioimpedance measurements and acoustic sensors

Lorenzo Giannini;Rita Asquini;Emanuele Piuzzi
2025

Abstract

Continuous respiratory monitoring is essential for the early detection and management of respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and sleep apnea. Traditional methods, including spirometry and polysomnography, remain the gold standard but present limitations such as high costs, invasiveness, and the requirement for trained personnel. This study proposes a multi-modal respiratory monitoring system integrating bioimpedance (BIOZ) and acoustic sensing for non-invasive, continuous, and realtime respiratory assessment. The system uses the SENSIPLUS microchip (developed by Sensichips s.r.l.) for bioimpedance measurements and a MEMS (Micro Electro-Mechanical System) microphone for capturing respiration sounds. The experimental validation was conducted by comparing these methods with a spirometer as the reference standard. Results demonstrated a strong correlation between the BIOZ and the acousticderived signals with spirometric data. The Root Mean Square Error (RMSE) values with respect to a gold standard, for inspiration and expiration using bioimpedance were 0.174 s and 0.190 s, respectively, while the acoustic-based approach yielded RMSE values of 0.4136 s for inspiration and 0.2217 s for expiration. These findings highlight the feasibility of integrating bioimpedance and acoustic sensing for accurate and wearable respiratory monitoring, offering potential applications in telemedicine, personalized healthcare, remote patient monitoring, early disease detection, and smart healthcare solutions.
2025
2025 IEEE International Symposium on Medical Measurements and Applications - MeMeA 2025
bioimpedance; smart healthcare; sensors; IoMT; telemedicine; respiration monitoring
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Multi-modal respiratory monitoring using SENSIPLUS chip bioimpedance measurements and acoustic sensors / Giannini, Lorenzo; Asquini, Rita; Contardi, Simone; Ria, Andrea; Piuzzi, Emanuele. - (2025), pp. 1-6. (Intervento presentato al convegno 2025 IEEE International Symposium on Medical Measurements and Applications - MeMeA 2025 tenutosi a Chania; Greece).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1741353
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