Employing an incentive spirometer to calibrate tidal volumes estimated from a smartphone camera Article uri icon

abstract

  • A smartphone-based tidal volume (VT) estimator was recently introduced by our research group, where an Android application provides a chest movement signal whose peak-to-peak amplitude is highly correlated with reference VT measured by a spirometer. We found a Normalized Root Mean Squared Error (NRMSE) of 14.998%25 ± 5.171%25 (mean SD) when the smartphone measures were calibrated using spirometer data. However, the availability of a spirometer device for calibration is not realistic outside clinical or research environments. In order to be used by the general population on a daily basis, a simple calibration procedure not relying on specialized devices is required. In this study, we propose taking advantage of the linear correlation between smartphone measurements and Vt to obtain a calibration model using information computed while the subject breathes through a commercially-available incentive spirometer (IS). Experiments were performed on twelve (N = 12) healthy subjects. In addition to corroborating findings from our previous study using a spirometer for calibration, we found that the calibration procedure using an IS resulted in a fixed bias of —0.051 L and a RMSE of 0.189 ± 0.074 L corresponding to 18.559%25 ± 6.579%25 when normalized. Although it has a small underestimation and slightly increased error, the proposed calibration procedure using an IS has the advantages of being simple, fast, and affordable. This study supports the feasibility of developing a portable smartphone-based breathing status monitor that provides information about breathing depth, in addition to the more commonly estimated respiratory rate, on a daily basis. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
  • A smartphone-based tidal volume (VT) estimator was recently introduced by our research group, where an Android application provides a chest movement signal whose peak-to-peak amplitude is highly correlated with reference VT measured by a spirometer. We found a Normalized Root Mean Squared Error (NRMSE) of 14.998%25 ± 5.171%25 (mean %2b SD) when the smartphone measures were calibrated using spirometer data. However, the availability of a spirometer device for calibration is not realistic outside clinical or research environments. In order to be used by the general population on a daily basis, a simple calibration procedure not relying on specialized devices is required. In this study, we propose taking advantage of the linear correlation between smartphone measurements and Vt to obtain a calibration model using information computed while the subject breathes through a commercially-available incentive spirometer (IS). Experiments were performed on twelve (N = 12) healthy subjects. In addition to corroborating findings from our previous study using a spirometer for calibration, we found that the calibration procedure using an IS resulted in a fixed bias of —0.051 L and a RMSE of 0.189 ± 0.074 L corresponding to 18.559%25 ± 6.579%25 when normalized. Although it has a small underestimation and slightly increased error, the proposed calibration procedure using an IS has the advantages of being simple, fast, and affordable. This study supports the feasibility of developing a portable smartphone-based breathing status monitor that provides information about breathing depth, in addition to the more commonly estimated respiratory rate, on a daily basis. © 2016 by the authors; licensee MDPI, Basel, Switzerland.

publication date

  • 2016-01-01