Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study

Abstract

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.

Keywords: COPD; air; breathing; design; develop; development; humidity; medical device; pulmonary; pulmonary volume; relative humidity sensor; respiratory; sensor; sensors; tidal volume; ventilation; wearable; wearables.

Figure 1

Commercial face masks typically used (A) in intensive care units with the (B) ventilation connector including respiratory rate, humidity, and pressure measurement sensors and (C) at home by patients with obstructive sleep apnea. All products are manufactured by ResMed Ltd.

Figure 2

Humidity sensor adapter connected to the obstructive sleep apnea face mask to reduce the amount of trapped moisture: (A) outside view of the airflow adapter with the reduction and humidity sensor, (B) inside view of the airflow adapter with the reduction and humidity sensor, and (C) airflow adapter with the reduction and humidity sensor and the heater connected.

Figure 3

(A) Schematics of the proposed health care Internet of Things architecture and (B) our prototype. BLE: Bluetooth Low Energy; OSA: obstructive sleep apnea.

Figure 4

Commercial pneumotach (PNT) used for reference baseline measurements: (A) PNT controller and heater and (B) PA-1 PNT amplifier. The photos were taken from Hans Rudolph website [61].

Figure 5

Testing procedures, including the (A) global measurement setup and (B) face mask connected to the heater and relative humidity sensor adapter.

Figure 6

Respiratory parameters calculations from the (A) commercial pneumotach (PNT) recordings and (B) relative humidity sensor–based prototype.

Figure 7

The Bland-Altman plot and calculations comparing the values from the relative humidity sensor with those from the pneumotach for the (A) deep breathing area and (B) respiratory rate. The blue line indicates the bias, and the dotted lines indicate the limits of agreement.

Figure 8

Box plots comparing the calculated (A) deep breathing area (DBA) and (B) respiratory rate values from the relative humidity sensor prototype with those from the pneumotach for all participants in the test data set.

Figure 9

The plot shows the root mean square error (RMSE) of the (A) deep breathing area (DBA) and (B) respiratory rate (RR) values, comparing our relative humidity sensor (RHS) prototype against the commercial pneumotach (PNT).

Figure 10

Two examples of breathing pattern anomalies during the deep breathing area and respiratory rate measurements using (A) the commercial pneumotach device and (B) our own relative humidity–based sensing device. The maximum point shows the value of the inspiratory reserve volume, while the minimum represents the expiratory reserve volume during deep breathing measurements; during a normal breathing pattern, the graph shows the tidal volume.