Abstract
The multi-sensor Integrated Measurement System (IMS) is an indigenous activity monitor developed with grant support from the NIH's Exposure Biology Program, which is part of the Genes and Environment Initiative. The IMS has a piezoelectric respiration sensor secured at the level of the abdomen. This sensor's raw signal is used to estimate respiration variables to quantify the internal exposure (inhalation) to environmental pollutants. PURPOSE: To examine preliminary calibration analyses of the IMS respiration sensor for predicting breathing rate (BR) and volume (BV). METHODS: Fifty adults (32.6 ± 9.9 yrs) wore the IMS and a portable metabolic unit (criterion) while performing one of two activity routines (7 activities each and 7 min/activity). These consisted of both ambulatory and simulated free-living activities. The raw sensor signal was 'filtered' to eliminate tissue artifact and then used to estimate BR and BV. BR was estimated by first classifying individual signal frequencies (Hz) into specific ranges. The range with the highest correlation to the original signal was used to estimate BR using post-spectral analyses. BV was estimated using multiple linear regression analysis with estimated breathing rate and the 10th, 25th, 50th, 75th, and the 90th percentiles of the sensor signal for each minute as the predictor variables. RESULTS: The overall BR estimate had a root mean square error of 4.1 breaths/min. BR during light (computer work and filing papers) and moderate (cycling, walking over-ground and on a treadmill at different speeds and grades) activities were slightly overestimated while that for vigorous intensity activities (treadmill running, tennis, and basketball) were underestimated. BV estimates (18.9 to 43.9 L/min) had a mean absolute percent error of 26.6% and a root mean square error of 8.6 L/min. BV during light and vigorous activities were over and underestimated, respectively. CONCLUSION: Preliminary analyses suggest that the IMS respiration sensor has the potential to provide reasonable estimates of BR and BV. Although prediction errors were large for BV, the coefficient of determination for the mean predictions was high (R2= 0.96). This suggests that the errors are more systematic than random in nature and may be corrected by improving the prediction model. Supported by: NIH UO1 CA130783
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
