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Respiratory Monitoring During Physical Activities With a Multi-Sensor Smart Garment and Related Algorithms
73
Citations
16
References
2019
Year
Fitness TrackingWearable SystemMedical MonitoringPhysical ActivityEngineeringWearable SensorMulti-sensor Smart GarmentPhysical ActivitiesWearable TechnologyBespoke AlgorithmsWearable SensorsHuman MonitoringHealth Monitoring (Structural Health Monitoring)Health Monitoring (Biomedical Engineering)Continuous MonitoringKinesiologyBioimpedance SensorsPatient MonitoringApplied PhysiologyHuman MotionHealth SciencesPhysiological ParametersWearable ElectronicsExercise PhysiologyHealth MonitoringHuman MovementRespiratory Monitoring
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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> to 8.0 km·h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ). 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 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> 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 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> 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 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> on both average and breath-by-breath bases even during physical activities.
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