Breath Measurement Method for Synchronized Reproduction of Biological Tones in an Augmented Reality Auscultation Training System

An educational augmented reality auscultation system (EARS) is proposed to enhance the reality of auscultation training using a simulated patient. The conventional EARS cannot accurately reproduce breath sounds according to the breathing of a simulated patient because the system instructs the breathing rhythm. In this study, we propose breath measurement methods that can be integrated into the chest piece of a stethoscope. We investigate methods using the thoracic variations and frequency characteristics of breath sounds. An accelerometer, a magnetic sensor, a gyro sensor, a pressure sensor, and a microphone were selected as the sensors. For measurement with the magnetic sensor, we proposed a method by detecting the breathing waveform in terms of changes in the magnetic field accompanying the surface deformation of the stethoscope based on thoracic variations using a magnet. During breath sound measurement, the frequency spectra of the breath sounds acquired by the built-in microphone were calculated. The breathing waveforms were obtained from the difference in characteristics between the breath sounds during exhalation and inhalation. The result showed the average value of the correlation coefficient with the reference value reached 0.45, indicating the effectiveness of this method as a breath measurement method. And the evaluations suggest more accurate breathing waveforms can be obtained by selecting the measurement method according to breathing method and measurement point.

A Differential Inertial Wearable Device for Breathing Parameter Detection: Hardware and Firmware Development, Experimental Characterization

Breathing monitoring is crucial for evaluating a patient's health status. The technologies commonly used to monitor respiration are costly, bulky, obtrusive, and inaccurate, mainly when the user moves. Consequently, efforts have been devoted to providing new solutions and methodologies to overcome these limitations. These methods have several uses, including healthcare monitoring, measuring athletic performance, and aiding patients with respiratory diseases, such as COPD (chronic obtrusive pulmonary disease), sleep apnea, etc. Breathing-induced chest movements can be measured noninvasively and discreetly using inertial sensors. This research work presents the development and testing of an inertia-based chest band for breathing monitoring through a differential approach. The device comprises two IMUs (inertial measurement units) placed on the patient's chest and back to determine the differential inertial signal, carrying out information detection about the breathing activity. The chest band includes a low-power microcontroller section to acquire inertial data from the two IMUs and process them to extract the breathing parameters (i.e., RR-respiration rate; TI/TE-inhalation/exhalation time; IER-inhalation-to-exhalation time; V-flow rate), using the back IMU as a reference. A BLE transceiver wirelessly transmits the acquired breathing parameters to a mobile application. Finally, the test results demonstrate the effectiveness of the used dual-inertia solution; correlation and Bland-Altman analyses were performed on the RR measurements from the chest band and the reference, demonstrating a high correlation (r¯ = 0.92) and low mean difference (MD¯ = -0.27 BrPM (breaths per minute)), limits of agreement (LoA¯ = +1.16/-1.75 BrPM), and mean absolute error (MAE¯ = 1.15%). Additionally, the experimental results demonstrated that the developed device correctly measured the other breathing parameters (TI, TE, IER, and V), keeping an MAE of ≤5%. The obtained results indicated that the developed chest band is a viable solution for long-term breathing monitoring, both in stationary and moving users.

New predictors of sleep efficiency

Sleep efficiency is a commonly and widely used measure to objectively evaluate sleep quality. Monitoring sleep efficiency can provide significant information about health conditions. As an attempt to facilitate less cumbersome monitoring of sleep efficiency, our study aimed to suggest new predictors of sleep efficiency that enable reliable and unconstrained estimation of sleep efficiency during awake resting period. We hypothesized that the autonomic nervous system activity observed before falling asleep might be associated with sleep efficiency. To assess autonomic activity, heart rate variability and breathing parameters were analyzed for 5 min. Using the extracted parameters as explanatory variables, stepwise multiple linear regression analyses and k-fold cross-validation tests were performed with 240 electrocardiographic and thoracic volume change signal recordings to develop the sleep efficiency prediction model. The developed model's sleep efficiency predictability was evaluated using 60 piezoelectric sensor signal recordings. The regression model, established using the ratio of the power of the low- and high-frequency bands of the heart rate variability signal and the average peak inspiratory flow value, provided an absolute error (mean ± SD) of 2.18% ± 1.61% and a Pearson's correlation coefficient of 0.94 (p < 0.01) between the sleep efficiency predictive values and the reference values. Our study is the first to achieve reliable and unconstrained prediction of sleep efficiency without overnight recording. This method has the potential to be utilized for home-based, long-term monitoring of sleep efficiency and to support reasonable decision-making regarding the execution of sleep efficiency improvement strategies.

Development of a rhesus monkey lung geometry model and application to particle deposition in comparison to humans

The exposure-dose-response characterization of an inhalation hazard established in an animal species needs to be translated to an equivalent characterization in humans relative to comparable doses or exposure scenarios. Here, the first geometry model of the conducting airways for rhesus monkeys is developed based upon CT images of the conducting airways of a 6-month-old male, rhesus monkey. An algorithm was developed for adding the alveolar region airways using published rhesus morphometric data. The resultant lung geometry model can be used in mechanistic particle or gaseous dosimetry models. Such dosimetry models require estimates of the upper respiratory tract volume of the animal and the functional residual capacity, as well as of the tidal volume and breathing frequency of the animal. The relationship of these variables to rhesus monkeys of differing body weights was established by synthesizing and modeling published data as well as modeling pulmonary function measurements on 121 rhesus control animals. Deposition patterns of particles up to 10 µm in size were examined for endotracheal and and up to 5 µm for spontaneous breathing in infant and young adult monkeys and compared to those for humans. Deposition fraction of respirable size particles was found to be higher in the conducting airways of infant and young adult rhesus monkeys compared to humans. Due to the filtering effect of the conducting airways, pulmonary deposition in rhesus monkeys was lower than that in humans. Future research areas are identified that would either allow replacing assumptions or improving the newly developed lung model.