research-article

Influence of torso movements on a multi-sensor garment for respiratory monitoring during walking and running activities

Authors:

Carlo Massaroni,

Joshua Di Tocco,

Riccardo Sabbadini,

Arianna Carnevale,

Daniela Lo Presti,

Emiliano Schena,

Domenico Formica,

Sandra Miccinilli,

Silvia SterziAuthors Info & Claims

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

Pages 1 - 6

https://doi.org/10.1109/I2MTC43012.2020.9128754

Published: 25 May 2020 Publication History

Abstract

Unobtrusive and wearable technologies are gaining broad acceptance in the continuous monitoring of physiological parameters. Among others, the respiratory frequency is increasingly being considered as it allows to detect physiological abnormalities and health status changes, both in clinical and occupational scenarios and in sports science.The respiratory monitoring during physical activity is still challenging because of the artifacts caused by body movements. These breathing-unrelated movements may negatively affect the signal used for respiratory monitoring.This study aimed to investigate the influence of the torso movements that occur during walking and running activities on the signals recorded by a multi-sensor garment. The garment consists of three bands positioned at the level of upper and lower thorax and abdomen. Each band embeds two conductive sensors. The torso movements were recorded by a magneto-inertial measurement unit embedded into the garment.Experimental trials were carried out on four male volunteers during walking and running activities at selected speeds controlled by a treadmill. A flowmeter was used to retrieve reference respiratory frequency values. All the signals were analyzed in the frequency domain to investigate the frequency content.Results show that movements related to the torso rotation (arms and shoulders swing) cause motion artifacts on the garment sensors’ signals. Sensors positioned on the upper thorax and abdomen were found to be much more influenced by breathing-unrelated movements.Despite the influence of the torso movements on the conductive sensors, the garment can provide robust information about the average respiratory rate even during physical activities.

References

[1]

W. Q. Mok, W. Wang, and S. Y. Liaw, “Vital signs monitoring to detect patient deterioration: An integrative literature review,” International Journal of Nursing Practice, vol. 21, pp. 91–98, 2015.

[2]

M. A. Cretikos, R. Bellomo, K. Hillman, J. Chen, S. Finfer, and A. Flabouris, “Respiratory rate: the neglected vital sign,” Medical Journal of Australia, vol. 188, no. 11, pp. 657–659, 2008.

[3]

U. Kyriacos, J. Jelsma, and S. Jordan, “Monitoring vital signs using early warning scoring systems: a review of the literature,” Journal of nursing management, vol. 19, no. 3, pp. 311–330, 2011.

[4]

K. E. Schaefer, “Respiratory pattern and respiratory response to co2,” Journal of Applied Physiology, vol. 13, no. 1, pp. 1–14, 1958.

[5]

M. A. Russo, D. M. Santarelli, and D. O’Rourke, “The physiological effects of slow breathing in the healthy human,” Breathe, vol. 13, no. 4, pp. 298–309, 2017.

[6]

C. S. Ogilvy and A. B. DuBois, “Effect of increased intracranial pressure on blood pressure, heart rate, respiration and catecholamine levels in neonatal and adult rabbits,” Neonatology, vol. 52, no. 6, pp. 327–336, 1987.

[7]

F. Q. AL-Khalidi, R. Saatchi, D. Burke, H. Elphick, and S. Tan, “Respiration rate monitoring methods: A review,” Pediatric pulmonology, vol. 46, no. 6, pp. 523–529, 2011.

[8]

C. Massaroni, A. Nicolò, D. Lo Presti, M. Sacchetti, S. Silvestri, and E. Schena, “Contact-based methods for measuring respiratory rate,” Sensors, vol. 19, no. 4, p. 908, 2019.

[9]

H. Liu, Y. Wang, and L. Wang, “A review of non-contact, low-cost physiological information measurement based on photoplethysmo-graphic imaging,” in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2012, pp. 2088–2091.

[10]

C. Massaroni, D. Lo Presti, D. Formica, S. Silvestri, and E. Schena, “Non-contact monitoring of breathing pattern and respiratory rate via rgb signal measurement,” Sensors, vol. 19, no. 12, p. 2758, 2019.

[11]

C.-T. Huang, C.-L. Shen, C.-F. Tang, and S.-H. Chang, “A wearable yarn-based piezo-resistive sensor,” Sensors and Actuators A: Physical, vol. 141, no. 2, pp. 396–403, 2008.

[12]

S. K. Kundu, S. Kumagai, and M. Sasaki, “A wearable capacitive sensor for monitoring human respiratory rate,” Japanese Journal of Applied Physics, vol. 52, no. 4S, p. 04CL05, 2013.

[13]

D. Wu, L. Wang, Y.-T. Zhang, B.-Y. Huang, B. Wang, S.-J. Lin, and X.-W. Xu, “A wearable respiration monitoring system based on digital respiratory inductive plethysmography,” in 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009, pp. 4844–4847.

[14]

C. Massaroni, C. Venanzi, A. P. Silvatti, D. Lo Presti, P. Saccomandi, D. Formica, F. Giurazza, M. A. Caponero, and E. Schena, “Smart textile for respiratory monitoring and thoraco-abdominal motion pattern evaluation,” Journal of biophotonics, vol. 11, no. 5, p. e201700263, 2018.

[15]

D. L. Presti, C. Massaroni, J. D’Abbraccio, L. Massari, M. Caponero, U. G. Longo, D. Formica, C. M. Oddo, and E. Schena, “Wearable system based on flexible fbg for respiratory and cardiac monitoring,” IEEE Sensors Journal, vol. 19, no. 17, pp. 7391–7398, 2019.

[16]

D. Lo Presti, C. Romano, C. Massaroni, J. D’Abbraccio, L. Massari, M. A. Caponero, C. M. Oddo, D. Formica, and E. Schena, “Cardio-respiratory monitoring in archery using a smart textile based on flexible fiber bragg grating sensors,” Sensors, vol. 19, no. 16, p. 3581, 2019.

[17]

Y. Retory, P. Niedzialkowski, C. de Picciotto, M. Bonay, and M. Pe-titjean, “New respiratory inductive plethysmography (rip) method for evaluating ventilatory adaptation during mild physical activities,” PloS one, vol. 11, no. 3, p. e0151983, 2016.

[18]

M. Chu, T. Nguyen, V. Pandey, Y. Zhou, H. N. Pham, R. Bar-Yoseph, S. Radom-Aizik, R. Jain, D. M. Cooper, and M. Khine, “Respiration rate and volume measurements using wearable strain sensors,” npj Digital Medicine, vol. 2, no. 1, p. 8, 2019.

[19]

H. Pontzer, J. H. Holloway, D. A. Raichlen, and D. E. Lieberman, “Control and function of arm swing in human walking and running,” Journal of Experimental Biology, vol. 212, no. 4, pp. 523–534, 2009.

[20]

C. Massaroni, J. Di Tocco, D. L. Presti, U. G. Longo, S. Miccinilli, S. Sterzi, D. Formica, P. Saccomandi, and E. Schena, “Smart textile based on piezoresistive sensing elements for respiratory monitoring,” IEEE Sensors Journal, 2019.

[21]

R. Holm, Electric contacts: theory and application. Springer Science & Business Media, 2013.

[22]

iNEMO inertial module: always-on 3D accelerometer and 3D gyroscope, STMicroelectronics, 09 2017, docID028165 Rev 7.

[23]

E. Schena, C. Massaroni, P. Saccomandi, and S. Cecchini, “Flow measurement in mechanical ventilation: A review,” Medical engineering & physics, vol. 37, no. 3, pp. 257–264, 2015.

[24]

C. Massaroni, J. Di Tocco, M. Bravi, A. Carnevale, D. L. Presti, R. Sabbadini, S. Miccinilli, S. Sterzi, D. Formica, and E. Schena, “Respiratory monitoring during physical activities with a multi-sensor smart garment and related algorithms,” IEEE Sensors Journal, 2019.

Cited By

Mozafari MLaw ATchoudem Djouaka SGreen JGoubran R(2022)Comparison of Blind Source Separation Techniques for Respiration Rate Estimation from Depth Video2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)10.1109/I2MTC48687.2022.9806591(1-5)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1109/I2MTC48687.2022.9806591

Recommendations

Torso pitch motion effects on walking gait for biped robots
Artificial Intelligence and Advanced Manufacturing (AIAM 2020)

Gait pattern generation has an important influence on the walking quality of biped robots. In most gait pattern generation method, it is usually assumed that the torso remains vertial during walking. It is very intuitive and simple. However, is the gait ...
Influence of the Foot on Ankle Joint Function during Running
The control of walking movements in the leg of the rock lobster

1. Experiments with rock lobsters walking on a treadmill were undertaken to obtain information upon the system controlling the movement of the legs. Results show that the position of the leg is an important parameter affecting the cyclic movement of the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

May 2020

2205 pages

Copyright © 2020.

Publisher

IEEE Press

Publication History

Published: 25 May 2020

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mozafari MLaw ATchoudem Djouaka SGreen JGoubran R(2022)Comparison of Blind Source Separation Techniques for Respiration Rate Estimation from Depth Video2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)10.1109/I2MTC48687.2022.9806591(1-5)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1109/I2MTC48687.2022.9806591

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents