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

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
$\cdot \text{h}^{-{1}}$
to 8.0 km
$\cdot \text{h}^{-{1}}$
). 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.