Breath-by-Breath Measurement of Respiratory Frequency and Tidal Volume with a Multiple-Camera Motion Capture System During Cycling Incremental Exercise

This study evaluates the performance of a 32-marker motion capture (MoCap) system in estimating respiratory frequency (fR) and tidal volume (VT) during cycling exercise. Fourteen well-trained cyclists performed an incremental step test on a cycle ergometer, while simultaneously recording a raw flow signal with a reference metabolic cart (COSMED) and respiratory-induced torso movements with twelve optoelectronic cameras registering the position of 32 markers affixed to the torso. fR and VT were calculated from both systems on a breath-by-breath basis. The MoCap system showed a strong correlation with the COSMED system when measuring fR and VT (r2 = 0.99, r2 = 0.87, respectively) during exercise. For fR, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 0.79 breaths/min and 2.1%, respectively. For VT, MoCap consistently underestimated values compared to COSMED, showing a bias (MOD ± LOA) of -0.11 ± 0.42 L and MAPE values of 8%. These findings highlight the system's capabilities for real-time respiratory monitoring in athletic environments.

Authors

Institutions

Publication Details

Journal
Sensors
Published
2025-04-19
DOI
https://doi.org/10.3390/s25082578
Citations
3
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
2.02
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Breath-by-Breath Measurement of Respiratory Frequency and Tidal Volume with a Multiple-Camera Motion Capture System During Cycling Incremental Exercise

Chiara Romano, Ana Luiza de Castro Lopes, Emiliano Schena, Massimo Sacchetti et al.
3 citations
Sensors
Non-Invasive Vital Sign Monitoring
2.02
article

Breath-by-Breath Measurement of Respiratory Frequency and Tidal Volume with a Multiple-Camera Motion Capture System During Cycling Incremental Exercise

Chiara Romano, Ana Luiza de Castro Lopes, Emiliano Schena, Massimo Sacchetti, Sergio Silvestri, Amanda Piaia Silvatti, Carlo Massaroni, Karine Jacon Sarro, Andrea Nicolò, Mariangela Pinnelli, Pietro Cerveri
article en
3 citations

Abstract

This study evaluates the performance of a 32-marker motion capture (MoCap) system in estimating respiratory frequency (fR) and tidal volume (VT) during cycling exercise. Fourteen well-trained cyclists performed an incremental step test on a cycle ergometer, while simultaneously recording a raw flow signal with a reference metabolic cart (COSMED) and respiratory-induced torso movements with twelve optoelectronic cameras registering the position of 32 markers affixed to the torso. fR and VT were calculated from both systems on a breath-by-breath basis. The MoCap system showed a strong correlation with the COSMED system when measuring fR and VT (r2 = 0.99, r2 = 0.87, respectively) during exercise. For fR, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 0.79 breaths/min and 2.1%, respectively. For VT, MoCap consistently underestimated values compared to COSMED, showing a bias (MOD ± LOA) of -0.11 ± 0.42 L and MAPE values of 8%. These findings highlight the system's capabilities for real-time respiratory monitoring in athletic environments.

SensorsVol. 25(8)
Universidade Federal de Viçosa (BR), Università Campus Bio-Medico (IT), Universidade Estadual de Campinas (UNICAMP) (BR), Foro Italico University of Rome (IT), University of Pavia (IT), Campus Bio Medico University Hospital (IT), Politecnico di Milano (IT)
Life below water
Openalex Percentile: Top 14%
Non-Invasive Vital Sign Monitoring
2.02
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.