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Respiratory Monitoring During Physical Activities With a Multi-Sensor Smart Garment and Related Algorithms

2019; IEEE Sensors Council; Volume: 20; Issue: 4 Linguagem: Inglês

10.1109/jsen.2019.2949608

1558-1748

Carlo Massaroni, Joshua Di Tocco, Marco Bravi, A Carnevale, Daniela Lo Presti, Riccardo Sabbadini, Sandra Miccinilli, Silvia Sterzi, Domenico Formica, Emiliano Schena,

Advanced Sensor and Energy Harvesting Materials

Unobtrusive and wearable devices are gaining large acceptance in the continuous monitoring of physiological parameters. Among the five vital signs, respiratory rate (fR) 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 fR during walking and running activities. Bespoke algorithms have been implemented to retrieve fR 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·h -1 to 8.0 km·h -1 ). Data were analysed in both frequency and time domains. In the frequency domain, fR 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 B[i] and-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.