Establishing Reference Metrics for Respiratory Exercises Through Wearable Sensors: A Comparative Study

Background: Respiratory exercises play a key role in rehabilitation programs, especially for older adults and individuals with chronic pulmonary conditions. Despite growing interest in wearable sensors for home-based care, structured reference metrics to quantitatively characterize respiratory exercises are still limited. This study aimed to provide a quantitative characterization of respiratory exercises and evaluate the level of agreement between a low-cost prototypical sensor and a commercial one. Methods: Eleven older adults (9 females; age = 72.6 ± 5.0 years; height = 1.66 ± 0.09 m; mass = 68 ± 10 kg) performed a structured respiratory exercises protocol. Algorithms were developed to identify respiratory cycles, their execution time, and parameters related to respiratory capacity, using accelerometer signals from the two wearable sensors placed on the rib cage. Results: The average respiratory cycle duration ranged from 2.8 to 4.3 s, with normalized inspiratory and expiratory peaks. Tidal volume variability was minimal, confirming consistency in breathing patterns across exercises. User comfort was high (mean VAS = 8.7). Sensor comparison confirmed strong agreement between the two sensors in detecting respiratory cycles, though some variability was observed in timing and tidal volume estimation. Conclusions: These findings suggest that even simple accelerometers can reliably capture key respiratory parameters, supporting the feasibility of using wearable sensors to monitor structured respiratory exercises performed in home-based settings.

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Publication Details

Journal
Biomechanics
Published
2025-11-05
DOI
https://doi.org/10.3390/biomechanics5040090
Primary Topic
Chronic Obstructive Pulmonary Disease (COPD) Research
Type
article
Field-Weighted Citation Impact
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article

Establishing Reference Metrics for Respiratory Exercises Through Wearable Sensors: A Comparative Study

Leandro Lucangeli, Emanuele D’Angelantonio, Valentina Camomilla, Federico Caramia
Biomechanics
Chronic Obstructive Pulmonary Disease (COPD) Research
article

Establishing Reference Metrics for Respiratory Exercises Through Wearable Sensors: A Comparative Study

Leandro Lucangeli, Emanuele D’Angelantonio, Valentina Camomilla, Federico Caramia
article en

Abstract

Background: Respiratory exercises play a key role in rehabilitation programs, especially for older adults and individuals with chronic pulmonary conditions. Despite growing interest in wearable sensors for home-based care, structured reference metrics to quantitatively characterize respiratory exercises are still limited. This study aimed to provide a quantitative characterization of respiratory exercises and evaluate the level of agreement between a low-cost prototypical sensor and a commercial one. Methods: Eleven older adults (9 females; age = 72.6 ± 5.0 years; height = 1.66 ± 0.09 m; mass = 68 ± 10 kg) performed a structured respiratory exercises protocol. Algorithms were developed to identify respiratory cycles, their execution time, and parameters related to respiratory capacity, using accelerometer signals from the two wearable sensors placed on the rib cage. Results: The average respiratory cycle duration ranged from 2.8 to 4.3 s, with normalized inspiratory and expiratory peaks. Tidal volume variability was minimal, confirming consistency in breathing patterns across exercises. User comfort was high (mean VAS = 8.7). Sensor comparison confirmed strong agreement between the two sensors in detecting respiratory cycles, though some variability was observed in timing and tidal volume estimation. Conclusions: These findings suggest that even simple accelerometers can reliably capture key respiratory parameters, supporting the feasibility of using wearable sensors to monitor structured respiratory exercises performed in home-based settings.

BiomechanicsVol. 5(4)
Rina Services (Italy) (IT), Foro Italico University of Rome (IT)
Openalex Percentile: Top 53%
Chronic Obstructive Pulmonary Disease (COPD) Research
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