BACKGROUND: Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea.
OBJECTIVE: The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard.
METHODS: We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device\'s efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant\'s data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods.
RESULTS: The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of -0.25 and 0.33. The RR bias was 0.018, and the LoAs were -1.89 and 1.89.
CONCLUSIONS: Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals.

The purposes of this study were to both improve the accuracy of respiratory volume (V) estimates using the respiratory magnetometer plethysmography (RMP) technique and facilitate the use of this technique.
We compared two models of machine learning (ML) for estimating [Formula: see text]: a linear model (multiple linear regression-MLR) and a nonlinear model (artificial neural network-ANN), and we used cross-validation to validate these models. Fourteen healthy adults, aged [Formula: see text] years participated in the present study. The protocol was conducted in a laboratory test room. The anteroposterior displacements of the rib cage and abdomen, and the axial displacements of the chest wall and spine were measured using two pairs of magnetometers. [Formula: see text] was estimated from these four signals, and the respiratory volume was simultaneously measured using a spirometer ([Formula: see text]) under lying, sitting and standing conditions as well as various exercise conditions (working on computer, treadmill walking at 4 and 6 km[Formula: see text], treadmill running at 9 and 12  km [Formula: see text] and ergometer cycling at 90 and 110 W).
The results from the ANN model fitted the spirometer volume significantly better than those obtained through MLR. Considering all activities, the difference between [Formula: see text] and [Formula: see text] (bias) was higher for the MLR model ([Formula: see text] L) than for the ANN model ([Formula: see text] L).
Our results demonstrate that this new processing approach for RMP seems to be a valid tool for estimating V with sufficient accuracy during lying, sitting and standing and under various exercise conditions.

To combine vocal tract measurements with dynamic MRI of the lungs to provide fundamental insights into the lung physiology during singing.
To analyze vocal fold oscillatory patterns during dynamic lung MRI, an electroglottography (EGG) system was modified to allow for simultaneous EGG measurements during MR image acquisitions. A low-pass filter was introduced to suppress residual radiofrequency (RF) coupling into the EGG signal. RF heating was tested in a gel phantom to ensure MR safety, and functionality of the EGG device was assessed in a volunteer experiment at singing frequencies from A5 to A3. In the recorded EGG signals, remaining RF interferences were removed by independent component analysis post processing, and standard EGG parameters such as fundamental frequency, contact quotient and jitter were calculated. In a second volunteer experiment, EGG recordings were compared with lung diameter measurements from 2D time-resolved trueFISP acquisitions.
RF heating measurements resulted in less than 1.2°C temperature increase in the gel phantom. EGG parameters measured during MR imaging are within the range of ideal values. In the lung measurement, both the lung diameter and the EGG recordings could be successfully performed with only minimal interference.
EGG recording is pos sible during dynamic lung MRI, and glottal activity can be studied safely at 1.5T. Magn Reson Med 76:1629-1635, 2016. © 2015 International Society for Magnetic Resonance in Medicine.