RRDetection: Respiration Rate Estimation Using Earbuds During Physical Activities.

In modern times, earbuds have become both popular and essential accessories for people to use with a wide range of devices in their everyday lives. Moreover, the respiration rate is a crucial vital sign that is sensitive to various pathological conditions. Many earbuds now come equipped with multiple sensing capabilities, including inertial and acoustic sensors. These sensors can be used by researchers to passively monitor users' vital signs, such as respiration rates. While current earbud-based breath rate estimation algorithms mostly focus on resting conditions, recent studies have demonstrated that respiration rates during physical activities can predict cardio-respiratory fitness for healthy individuals and pulmonary conditions for respiratory patients. To address this gap, we propose a novel algorithm called RRDetection that leverages the motion sensors in ordinary earbuds to detect respiration rates during light to moderate physical activities.

Detecting Physiological Responses Using Multimodal Earbud Sensors.

Continuous stress exposure negatively impacts mental and physical well-being. Physiological arousal due to stress affects heartbeat frequency, changes breathing pattern and peripheral temperature, among several other bodily responses. Traditionally stress detection is performed by collecting signals such as electrocardiogram (ECG), respiration, and skin conductance response using uncomfortable sensors such as a chestband. In this study, we use earbuds that passively measure photoplethysmography (PPG), core body temperature, and inertial measurements. We have conducted a lab study exposing 18 participants to an evaluated speech task and additional tasks aimed at increasing stress or promoting relaxation. We simultaneously collected PPG, ECG, impedance cardiography (ICG), and blood pressure using laboratory grade equipment as reference measurements. We show that the earbud PPG sensor can reliably capture heart rate and heart rate variability. We further show that earbud signals can be used to classify the physiological responses associated with stress with 91.30% recall, 80.52% precision, and 85.12% F1-score using a random forest classifier with leave-one-subject-out cross-validation. The accuracy can further be improved through multi-modal sensing. These findings demonstrate the feasibility of using earbuds for passively monitoring users' physiological responses.

Mechanistic Insights into the Link between Gut Dysbiosis and Major Depression: An Extensive Review.

Depression is a highly common mental disorder, which is often multifactorial with sex, genetic, environmental, and/or psychological causes. Recent advancements in biomedical research have demonstrated a clear correlation between gut dysbiosis (GD) or gut microbial dysbiosis and the development of anxiety or depressive behaviors. The gut microbiome communicates with the brain through the neural, immune, and metabolic pathways, either directly (via vagal nerves) or indirectly (via gut- and microbial-derived metabolites as well as gut hormones and endocrine peptides, including peptide YY, pancreatic polypeptide, neuropeptide Y, cholecystokinin, corticotropin-releasing factor, glucagon-like peptide, oxytocin, and ghrelin). Maintaining healthy gut microbiota (GM) is now being recognized as important for brain health through the use of probiotics, prebiotics, synbiotics, fecal microbial transplantation (FMT), etc. A few approaches exert antidepressant effects via restoring GM and hypothalamus-pituitary-adrenal (HPA) axis functions. In this review, we have summarized the etiopathogenic link between gut dysbiosis and depression with preclinical and clinical evidence. In addition, we have collated information on the recent therapies and supplements, such as probiotics, prebiotics, short-chain fatty acids, and vitamin B12, omega-3 fatty acids, etc., which target the gut-brain axis (GBA) for the effective management of depressive behavior and anxiety.

RRMonitor: A Resource-Aware End-to-End System for Continuous Monitoring of Respiration Rate Using Earbuds.

Respiration rate is considered as a critical vital sign, and daily monitoring of respiration rate could provide helpful information about any acute condition in the human body. While researchers have been exploring mobile devices for respiration rate monitoring, passive and continuous monitoring is still not feasible due to many usability challenges (e.g., active participation) in existing approaches. This paper presents an end-to-end system called RRMonitor that leverages the movement sensors from commodity earbuds to continuously monitor the respiration rate in near real-time. While developing the systems, we extensively explored some key parameters, algorithms, and approaches from existing literature that are better suited for continuous and passive respiration rate monitoring. RRMonitor can passively track the respiration rate with a mean absolute error as low as 1.64 cycles per minute without requiring active participation from the user.

Device Invariant Deep Neural Networks for Pulmonary Audio Event Detection Across Mobile and Wearable Devices.

Mobile and wearable devices are being increasingly used for developing audio based machine learning models to infer pulmonary health, exacerbation and activity. A major challenge to widespread usage and deployment of such pulmonary health monitoring audio models is to maintain accuracy and robustness across a variety of commodity devices, due to the effect of device heterogeneity. Because of this phenomenon, pulmonary audio models developed with data from one type of device perform poorly when deployed on another type of device. In this work, we propose a framework incorporating feature normalization across individual frequency bins and combining task specific deep neural networks for model invariance across devices for pulmonary event detection. Our empirical and extensive experiments with data from 131 real pulmonary patients and healthy controls show that our framework can recover up to 163.6% of the accuracy lost due to device heterogeneity for four different pulmonary classification tasks across two broad classification scenarios with two common mobile and wearable devices: smartphone and smartwatch.Clinical relevance- The methods presented in this paper will enable efficient and easy portability of clinician recommended pulmonary audio event detection and analytic models across various mobile and wearable devices used by a patient.

A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection.

Cough is a major symptom of respiratory-related diseases. There exists a tremendous amount of work in detecting coughs from audio but there has been no effort to identify coughs from solely inertial measurement unit (IMU). Coughing causes motion across the whole body and especially on the neck and head. Therefore, head motion data during coughing captured by a head-worn IMU sensor could be leveraged to detect coughs using a template matching algorithm. In time series template matching problems, K-Nearest Neighbors (KNN) combined with elastic distance measurement (esp. Dynamic Time Warping (DTW)) achieves outstanding performance. However, it is often regarded as prohibitively time-consuming. Nearest Centroid Classifier is thereafter proposed. But the accuracy is comprised of only one centroid obtained for each class. Centroid-based Classifier performs clustering and averaging for each cluster, but requires manually setting the number of clusters. We propose a novel self-tuning multi-centroid template-matching algorithm, which can automatically adjust the number of clusters to balance accuracy and inference time. Through experiments conducted on synthetic datasets and a real-world earbud-based cough dataset, we demonstrate the superiority of our proposed algorithm and present the result of cough detection with a single accelerometer sensor on the earbuds platform.Clinical relevance- Coughing is a ubiquitous symptom of pulmonary disease, especially for patients with COPD and asthma. This work explores the possibility and and presents the result of cough detection using an IMU sensor embedded in earables.

Strength evolution laws in curing of solvent-welded polymers.

This paper reports the scaling laws to describe the time-evolution behavior of solvent-mediated strength at the interface between two identical thermoplastic polymers below the glass-transition temperature. Our results suggest that the evolution scales as sqrt[t], where t is the curing time. It depends on the time evolution of interfacial stiffness and toughness, each of which scales as sqrt[t]. Employing a combination of experiments and continuum scale simulations, we show that the evolution of strength, stiffness, and toughness is controlled by pure diffusion. It can therefore be treated as a Gaussian process. While the "saturation of strength," which describes the transition of strength evolution into a steady state, does not strictly follow any power-law type behavior, a simple exponential law accurately characterizes both evolution and saturation of strength. This suggests that the longer timescale nonlinear processes (that are overdetermined by the power-law type scaling laws) diminish rapidly in approaching a steady state. Furthermore, the kinetics of the evolution processes is well captured by the dissolution of polymer particles. While dissolution involves a different timescale, it strongly correlates with the solvent-welding process upon normalization. The correlation highlights the equivalence of the dissolution and solvent-joining processes and offers an easier route to determining strength at arbitrary curing times. Additionally, the dissolution rate of polymer particles is shape dependent and governed by the surface-to-volume ratio.

Correlation of Subclinical Hypothyroidism With Polycystic Ovary Syndrome (PCOS).

Aim The correlation of subclinical hypothyroidism (SCH) and polycystic ovary syndrome (PCOS) is a still insufficiently explored entity. The aim of this study was to determine the correlation between SCH and PCOS along with the impact of SCH on metabolic and hormonal parameters in women with PCOS. Methodology This cross-sectional study was conducted at the Gynecology Outpatient Department of Ziauddin Hospital Kemari, Karachi, Pakistan, from June 2019 to December 2019. A total of 90 diagnosed cases of PCOS were enrolled in the study. A non-probability consecutive sampling technique was used. After taking informed consent, participants were evaluated through clinical interviews, a questionnaire, and anthropometric measurements. The participants underwent the following assessments, i.e., transabdominal ultrasonography, hormonal profile (free testosterone, follicle-stimulating hormone, luteinizing hormone), and fasting blood sugar. Participants were divided into two groups based on thyroid-stimulating hormone (TSH) into the euthyroid group and subclinical hypothyroid (SCH) group. The Mann-Whitney test was used for comparing the two groups. Results Our results showed a significant difference in weight, body mass index (BMI), insulin, homeostatic model assessment of insulin resistance (HOMA-IR), and TSH were found in the SCH group as compared to the euthyroid group. A significant correlation of TSH with waist-hip ratio (WHR), weight, body mass index (BMI), insulin, and the homeostatic model assessment of insulin resistance (HOMA-IR) in PCOS patients. Conclusion This study showed a significant correlation of subclinical hypothyroidism with polycystic ovary syndrome. We found subclinical hypothyroidism may aggravate the insulin resistance; therefore, PCOS patients must be screened with a thyroid profile.

Comparative Evaluation of the Embrasure Wire versus Arch Bar Maxillomandibular Fixation in the Management of Mandibular Fractures: Are Arch Bars Replaceable?

The aim of this prospective study was to appraise the role of embrasure wiring in the treatment of mandibular fractures over the arch bar as adjunctive techniques of maxillomandibular fixation (MMF). This study was conducted on 40 patients who were surgically treated for mandibular fractures with accessory use of MMF (embrasure: group A vs. arch bars: group B). All patients were evaluated for demographic data, etiology, and location of fracture. Characteristically, the complications, including wire injury, infection, and malocclusion, were recorded. The data were analyzed using Student's t -test and chi-square test as appropriate. Statistical significance was set at p < 0.05). In this study, data from 40 patients were included. In group A (embrasure wiring), time required for placement of MMF was significantly less than (7.85 ± 0.81 minutes) that in group B, and also there was less incidence of wire prick to the operator in group A than in group B ( p < 0.05). However, in terms of wire prick and malocclusion, no statistically significant difference was noted in groups A and B ( p > 0.05). Patient treated with embrasure wiring intermaxillary fixation had better outcomes especially in terms of time of placement and less incidence of wire prick injury when compared with arch bar.