Respiratory rate monitoring is crucial for many diseases, correlated or not with the lungs, like chronic bronchitis and obstructive apnea, and it is central for the study of sleep stages. Notably for sleep-related diseases, it is important to develop a non-intrusive method to monitor the respiratory rate. This single-subject study investigates the feasibility of using a pressure-sensor mattress to avoid cables and discomfort, for both the patient and the staff, typical of other devices. A pressure-sensor mattress generates a 2D matrix of pressure signals: in this work, those signals are analysed by a processing pipeline to detect the best signal in the matrix. The aim is to find the best signal to exploit for measuring the respiratory rate. Criteria have been identified, resulting in a metric to order the signals. The respiratory rate is then determined by another processing pipeline acting on the stream from this specific sensor. Many complications made the data gathered from all but one subject unusable: nevertheless, the results show that the approach is effective and the respiratory rate can reliably be measured with a commercial pressure-sensor mattress.

Sarteschi, A., Sorrenti, D. (2023). Respiratory Rate Estimation via Sensor Pressure Mattress: a single subject evaluation. In Proceedings of the 2nd Workshop on Artificial Intelligence for Human-Machine Interaction 2023 co-located with the 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) (pp.15-27). CEUR-WS.

Respiratory Rate Estimation via Sensor Pressure Mattress: a single subject evaluation

Sorrenti D. G.
2023

Abstract

Respiratory rate monitoring is crucial for many diseases, correlated or not with the lungs, like chronic bronchitis and obstructive apnea, and it is central for the study of sleep stages. Notably for sleep-related diseases, it is important to develop a non-intrusive method to monitor the respiratory rate. This single-subject study investigates the feasibility of using a pressure-sensor mattress to avoid cables and discomfort, for both the patient and the staff, typical of other devices. A pressure-sensor mattress generates a 2D matrix of pressure signals: in this work, those signals are analysed by a processing pipeline to detect the best signal in the matrix. The aim is to find the best signal to exploit for measuring the respiratory rate. Criteria have been identified, resulting in a metric to order the signals. The respiratory rate is then determined by another processing pipeline acting on the stream from this specific sensor. Many complications made the data gathered from all but one subject unusable: nevertheless, the results show that the approach is effective and the respiratory rate can reliably be measured with a commercial pressure-sensor mattress.
slide + paper
Non-contact sensors; Pressure-sensor mattress; Respiratory rate measurement;
English
2nd Workshop on Artificial Intelligence for Human-Machine Interaction 2023 co-located with the 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) - November 6, 2023
2023
Saibene, A; Corchs, S; Fontana, S; Solé-Casals, J
Proceedings of the 2nd Workshop on Artificial Intelligence for Human-Machine Interaction 2023 co-located with the 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023)
2023
3576
15
27
https://ceur-ws.org/Vol-3576/
none
Sarteschi, A., Sorrenti, D. (2023). Respiratory Rate Estimation via Sensor Pressure Mattress: a single subject evaluation. In Proceedings of the 2nd Workshop on Artificial Intelligence for Human-Machine Interaction 2023 co-located with the 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) (pp.15-27). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/475803
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