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.

Analysis of head and chest movements that correspond to gaze directions during walking.

In this paper, we analyze the relationship between head and chest movements and gaze direction in both walking and non-walking conditions. In a different approach from existing studies, we aim to analyze behavior when humans intentionally gaze at a certain target from two perspectives: (1) the relationship between gaze and body movements and (2) the effects of walking on body motion. We performed three experiments: fixed target scenes (Experiment 1), moving target scenes (Experiment 2) and more realistic gazing scenes (Experiment 3). The experimental results showed a linear relationship between the head and chest directions and gaze directions regardless of walking, non-walking situations, or target movements, and stronger gaze-head correlations than gaze-chest correlations. Further, we found effects of walking that constrained rotational body movements, and that body parts with larger moments were easily affected by walking. These results suggest that the findings of existing studies in non-walking situations may be applicable to walking situations directly or with simple modifications.

Developed wearable miniature sensor to diagnose initial perturbations of cardiorespiratory system.

The progress of microelectromechanical systems tends to fabricate miniature motion sensors that can be used for various purposes of biomedical systems, particularly on-body applications. A miniature wireless sensor is developed that not only monitors heartbeat and respiration rate based on chest movements but also identifies initial problems in the cardiorespiratory system, presenting a healthy measure defined based on height and length of the normal distribution of respiration rate and heartbeat. The obtained results of various tests are compared with two commercial sensors consisting of electrocardiogram sensor as well as belt sensor of respiration rate as a reference (gold standard), showing that the root-mean-square errors obtain <2.27 beats/min for a heartbeat and 0.93 breaths/min for respiration rate. In addition, the standard deviation of the errors reaches <1.26 and 0.63 for heartbeat and respiration rates, separately. According to the outcome results, the sensor can be considered an appropriate candidate for in-home health monitoring, particularly early detection of cardiovascular system problems.

Towards the Development of a Mobile Phonopneumogram: Automatic Breath-Phase Classification Using Smartphones.

Correct labeling of breath phases is useful in the automatic analysis of respiratory sounds, where airflow or volume signals are commonly used as temporal reference. However, such signals are not always available. The development of a smartphone-based respiratory sound analysis system has received increased attention. In this study, we propose an optical approach that takes advantage of a smartphone's camera and provides a chest movement signal useful for classification of the breath phases when simultaneously recording tracheal sounds. Spirometer and smartphone-based signals were acquired from N = 13 healthy volunteers breathing at different frequencies, airflow and volume levels. We found that the smartphone-acquired chest movement signal was highly correlated with reference volume (ρ = 0.960 ± 0.025, mean ± SD). A simple linear regression on the chest signal was used to label the breath phases according to the slope between consecutive onsets. 100% accuracy was found for the classification of the analyzed breath phases. We found that the proposed classification scheme can be used to correctly classify breath phases in more challenging breathing patterns, such as those that include non-breath events like swallowing, talking, and coughing, and alternating or irregular breathing. These results show the feasibility of developing a portable and inexpensive phonopneumogram for the analysis of respiratory sounds based on smartphones.

Respiratory rate detection algorithm based on RGB-D camera: theoretical background and experimental results.

Both the theoretical background and the experimental results of an algorithm developed to perform human respiratory rate measurements without any physical contact are presented. Based on depth image sensing techniques, the respiratory rate is derived by measuring morphological changes of the chest wall. The algorithm identifies the human chest, computes its distance from the camera and compares this value with the instantaneous distance, discerning if it is due to the respiratory act or due to a limited movement of the person being monitored. To experimentally validate the proposed algorithm, the respiratory rate measurements coming from a spirometer were taken as a benchmark and compared with those estimated by the algorithm. Five tests were performed, with five different persons sat in front of the camera. The first test aimed to choose the suitable sampling frequency. The second test was conducted to compare the performances of the proposed system with respect to the gold standard in ideal conditions of light, orientation and clothing. The third, fourth and fifth tests evaluated the algorithm performances under different operating conditions. The experimental results showed that the system can correctly measure the respiratory rate, and it is a viable alternative to monitor the respiratory activity of a person without using invasive sensors.