Feasibility Assessment of Wearable Respiratory Monitors for Ambulatory Inhalation Topography

Development of a real-time RGB-D visual feedback-assisted pulmonary rehabilitation system

Background

Following surgery, perioperative pulmonary rehabilitation (PR) is important for patients with early-stage lung cancer. However, current inpatient programs are often limited in time and space, and outpatient settings have access barriers. Therefore, we aimed to develop a background-free, zero-contact thoracoabdominal movement-tracking model that is easily set up and incorporated into a pre-existing PR program or extended to home-based rehabilitation and remote monitoring. We validated its effectiveness in providing preclinical real-time RGB-D (colour-depth camera) visual feedback.

Methods

Twelve healthy volunteers performed deep breathing exercises following audio instruction for three cycles, followed by audio instruction and real-time visual feedback for another three cycles. In the visual feedback system, we used a RealSense™ D415 camera to capture RGB and depth images for human pose-estimation with Google MediaPipe. Target-tracking regions were defined based on the relative position of detected joints. The processed depth information of the tracking regions was visualised on a screen as a motion bar to provide real-time visual feedback of breathing intensity. Pulmonary function was simultaneously recorded using spirometric measurements, and changes in pulmonary volume were derived from respiratory airflow signals.

Results

Our movement-tracking model showed a very strong correlation (r = 0.90 ± 0.05) between thoracic motion signals and spirometric volume, and a strong correlation (r = 0.73 ± 0.22) between abdominal signals and spirometric volume. Displacement of the chest wall was enhanced by RGB-D visual feedback (23 vs 20 mm, P = 0.034), and accompanied by an increased lung volume (2.58 vs 2.30 L, P = 0.003).

Conclusion

We developed an easily implemented thoracoabdominal movement-tracking model and reported the positive impact of real-time RGB-D visual feedback on self-promoted external chest wall expansion, accompanied by increased internal lung volumes. This system can be extended to home-based PR.

Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.

A Survey on Wearable Sensors for Mental Health Monitoring

Mental illness, whether it is medically diagnosed or undiagnosed, affects a large proportion of the population. It is one of the causes of extensive disability, and f not properly treated, it can lead to severe emotional, behavioral, and physical health problems. In most mental health research studies, the focus is on treatment, but fewer resources are focused on technical solutions to mental health issues. The present paper carried out a systematic review of available literature using PRISMA guidelines to address various monitoring solutions in mental health through the use of wearable sensors. Wearable sensors can offer several advantages over traditional methods of mental health assessment, including convenience, cost-effectiveness, and the ability to capture data in real-world settings. Their ability to collect data related to anxiety and stress levels, as well as panic attacks, is discussed. The available sensors on the market are described, as well as their success in providing data that can be correlated with the aforementioned health issues. The current wearable landscape is quite dynamic, and the current offerings have enough quality to deliver meaningful data targeted for machine learning algorithms. The results indicate that mental health monitoring is feasible.

The effect of wearable-delivered sleep interventions on sleep outcomes among adults: A systematic review and meta-analysis of randomized controlled trials

The aims of the review were to (i) evaluate the effectiveness of wearable-delivered sleep interventions on sleep outcomes among adults, and (ii) explore the effect of factors affecting total sleep time. Eight databases were searched to identify relevant studies in English from inception until December 23, 2021. The Cochrane Risk of Bias tool version 2.0 and Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria were used to assess the risk of bias and certainty of the evidence, respectively. Twenty randomized controlled trials (RCTs) were included, involving 1608 adults across nine countries. Wearable-delivered sleep interventions elicited significant improvement of 1.96 events/h for the oxygen desaturation index and 3.13 events/h for the respiratory distress index. Meta-analyses found that wearable-delivered sleep interventions significantly decreased sleep disturbance (Hedges' g [g] = -0.37, 95% confidence interval [CI]: -0.59, -0.15) and sleep-related impairment (g = -1.06, 95% CI: -1.99, -0.13) versus the comparators. The wearable-delivered sleep interventions may complement usual care to improve sleep outcomes. More rigorous RCTs with a long-term assessment in a wide range of populations are warranted.

Mission Chain Driven Unmanned Aerial Vehicle Swarms Cooperation for the Search and Rescue of Outdoor Injured Human Targets

A novel cooperative strategy for distributed unmanned aerial vehicle (UAV) swarms with different functions, namely the mission chain-driven unmanned aerial vehicle swarms cooperation method, is proposed to allow the fast search and timely rescue of injured human targets in a wide-area outdoor environment. First, a UAV-camera unit is exploited to detect the suspected human target combined with improved deep learning technology. Then, the target location information is transferred to a self-organizing network. Then, the special bio-radar-UAV unit was released to recheck the survivals through a respiratory characteristic detection algorithm. Finally, driven by the location and vital sign status of the injured, a nearby emergency-UAV unit will perform corresponding medical emergency missions, such as dropping emergency supplies. Experimental results show that this strategy can identify the human targets autonomously from the outdoor environment effectively, and the target detection, target sensing, and medical emergency mission chain is completed successfully relying on the cooperative working mode, which is meaningful for the future search-rescue mission of outdoor injured human targets.

Concurrent Evolution of Biomechanical and Physiological Parameters With Running-Induced Acute Fatigue

Understanding the influence of running-induced acute fatigue on the homeostasis of the body is essential to mitigate the adverse effects and optimize positive adaptations to training. Fatigue is a multifactorial phenomenon, which influences biomechanical, physiological, and psychological facets. This work aimed to assess the evolution of these three facets with acute fatigue during a half-marathon. 13 recreational runners were equipped with one inertial measurement unit (IMU) on each foot, one combined global navigation satellite system-IMU-electrocardiogram sensor on the chest, and an Android smartphone equipped with an audio recording application. Spatio-temporal parameters for the running gait, along with the heart rate, its variability and complexity were computed using validated algorithms. Perceived fatigability was assessed using the rating-of-fatigue (ROF) scale at every 10 min of the race. The data was split into eight equal segments, corresponding to at least one ROF value per segment, and only level running parts were retained for analysis. During the race, contact time, duty factor, and trunk anteroposterior acceleration increased, and the foot strike angle and vertical stiffness decreased significantly. Heart rate showed a progressive increase, while the metrics for heart rate variability and complexity decreased during the race. The biomechanical parameters showed a significant alteration even with a small change in perceived fatigue, whereas the heart rate dynamics altered at higher changes. When divided into two groups, the slower runners presented a higher change in heart rate dynamics throughout the race than the faster runners; they both showed similar trends for the gait parameters. When tested for linear and non-linear correlations, heart rate had the highest association with biomechanical parameters, while the trunk anteroposterior acceleration had the lowest association with heart rate dynamics. These results indicate the ability of faster runners to better judge their physiological limits and hint toward a higher sensitivity of perceived fatigue to neuromuscular changes in the running gait. This study highlights measurable influences of acute fatigue, which can be studied only through concurrent measurement of biomechanical, physiological, and psychological facets of running in real-world conditions.

Feasibility of Using the Hexoskin Smart Garment for Natural Environment Observation of Respiration Topography

Background: Limited research has been done to measure ambulatory respiratory behavior, in particular those associated with tobacco use, in the natural environment due to a lack of monitoring techniques. Respiratory topography parameters provide useful information for modeling particle deposition in the lung and assessing exposure risk and health effects associated with tobacco use. Commercially available Wearable Respiratory Monitors (WRM), such as the Hexoskin Smart Garment, have embedded sensors that measure chest motion and may be adapted for measuring ambulatory lung volume. Methods: Self-reported "everyday" and "some days" Hookah and Cigarette smokers were recruited for a 3-day natural environment observation study. Participants wore the Hexoskin shirt while using their preferred tobacco product. The shirt was calibrated on them prior to, during, and after the observation period. A novel method for calculating the calibration parameters is presented. Results: NH = 5 Hookah and NC = 3 Cigarette participants were enrolled. Calibration parameters were obtained and applied to the observed chest motion waveform from each participant to obtain their lung volume waveform. Respiratory topography parameters were derived from the lung volume waveform. Conclusion: The feasibility of using the Hexoskin for measuring ambulatory respiratory topography parameters in the natural environment is demonstrated.