Unobtrusive and wearable devices are gaining large acceptance in the continuous monitoring of physiological parameters. Among the five vital signs, respiratory rate ( ${f}_{R}$ ) can be used to detect physiological abnormalities and health status changes. The purpose of this work was to investigate the performances of a multi-sensor smart garment in estimating the ${f}_{R}$ during walking and running activities. Bespoke algorithms have been implemented to retrieve ${f}_{R}$ values from raw data. Experiments were carried out on ten male volunteers during walking and running activities at selected speeds controlled by a treadmill (i.e., from 1.6 km $cdot ext{h}^{-{1}}$ to 8.0 km $cdot ext{h}^{-{1}}$ ). Data were analysed in both frequency and time domains. In the frequency domain, ${f}_{R}$ was analyzed considering a time window of 20 s. The 97% of ${f}_{R}$ estimated by the garment agreed with the reference (i.e., flowmeter) values in the range ±3 breaths per minute (bpm). In the time domain, breath-by-breath ${f}_{R}$ analysis was carried out. The garment performance was evaluated in terms of mean absolute error (MAE), standard error (SE), mean percentage error (mean $%{E}[{i}]$ ) and by the Bland-Altman analysis. Good agreement with the reference device was testified by low MAE (<1.86 bpm), SE (<0.21 bpm), mean $%{E}[{i}]$ (<2.83 %), and by the Bland-Altman analysis (Mean of Differences = 0.22 bpm, Limits of Agreement = 6.06 bpm). Summing up, the garment based on six sensing elements and related bespoke algorithms are able to provide robust information about ${f}_{R}$ on both average and breath-by-breath bases even during physical activities. © 2001-2012 IEEE.

Respiratory monitoring during physical activities with a multi-sensor smart garment and related algorithms

Massaroni C;Bravi M;Carnevale A;Lo Presti D;Miccinilli S;Sterzi S;Formica D;Schena E
2020-01-01

Abstract

Unobtrusive and wearable devices are gaining large acceptance in the continuous monitoring of physiological parameters. Among the five vital signs, respiratory rate ( ${f}_{R}$ ) can be used to detect physiological abnormalities and health status changes. The purpose of this work was to investigate the performances of a multi-sensor smart garment in estimating the ${f}_{R}$ during walking and running activities. Bespoke algorithms have been implemented to retrieve ${f}_{R}$ values from raw data. Experiments were carried out on ten male volunteers during walking and running activities at selected speeds controlled by a treadmill (i.e., from 1.6 km $cdot ext{h}^{-{1}}$ to 8.0 km $cdot ext{h}^{-{1}}$ ). Data were analysed in both frequency and time domains. In the frequency domain, ${f}_{R}$ was analyzed considering a time window of 20 s. The 97% of ${f}_{R}$ estimated by the garment agreed with the reference (i.e., flowmeter) values in the range ±3 breaths per minute (bpm). In the time domain, breath-by-breath ${f}_{R}$ analysis was carried out. The garment performance was evaluated in terms of mean absolute error (MAE), standard error (SE), mean percentage error (mean $%{E}[{i}]$ ) and by the Bland-Altman analysis. Good agreement with the reference device was testified by low MAE (<1.86 bpm), SE (<0.21 bpm), mean $%{E}[{i}]$ (<2.83 %), and by the Bland-Altman analysis (Mean of Differences = 0.22 bpm, Limits of Agreement = 6.06 bpm). Summing up, the garment based on six sensing elements and related bespoke algorithms are able to provide robust information about ${f}_{R}$ on both average and breath-by-breath bases even during physical activities. © 2001-2012 IEEE.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/3976
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