High Prevalence of Antimicrobial Resistance Genes in Multidrug-Resistant Extended-Spectrum Beta-Lactamase-Producing Clinical Isolates after COVID-19 Pandemic, North Iran.

Background: Amid the COVID-19 pandemic, the surge in hospital admissions and widespread use of broad-spectrum antibiotics have heightened the risk of hospital-acquired infections from multidrug-resistant (MDR) organisms, particularly . It is imperative to implement stringent measures to curb the spread of antimicrobial resistance in hospitals and devise robust treatment strategies for patients grappling with such infections. To confront this challenge, a comprehensive study was undertaken to examine MDR extended-spectrum beta-lactamase (MDR-ESBL)-producing isolates from patients with nosocomial infections following the COVID-19 pandemic in Northern Iran.

Power Optimized Smartwatch-Earbuds Multimodal System for Monitoring Activities of Daily Living.

Activities of daily living is an important entity to monitor for promoting healthy lifestyle for chronic disease patients, children and the healthy population. This paper presents a smartwatch and earbuds inertial sensors based multi-modal power efficient end-to-end mobile system for continuous, passive and accurate detection of broad daily activity classes. We collected various posture, stationary and moving activity data from 40 diverse subjects using earbuds and smartwatch and develop the novel power optimized end-to-end operational system consisting of i) optimized device sampling rates and Bluetooth packet transfer rates, ii) data buffering mechanism, iii) background services, and iv) optimized model size, and demonstrating 93% macro recall score in detecting various activities.

MM-HAR: Multi-Modal Human Activity Recognition Using Consumer Smartwatch and Earbuds.

Human Activity Recognition (HAR) is one of the important applications of digital health that helps to track fitness or to avoid sedentary behavior by monitoring daily activities. Due to the growing popularity of consumer wearable devices, smartwatches, and earbuds are being widely adopted for HAR applications. However, using just one of the devices may not be sufficient to track all activities properly.

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.

SpeechSpiro: Lung Function Assessment from Speech Pattern as an Alternative to Spirometry for Mobile Health Tracking.

Respiratory illnesses are common in the United States and globally; people deal with these illnesses in various forms, such as asthma, chronic obstructive pulmonary diseases, or infectious respiratory diseases (e.g., coronavirus).

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.

Toward Early Severity Assessment of Obstructive Lung Disease Using Multi-Modal Wearable Sensor Data Fusion During Walking.

Early detection of chronic diseases helps to minimize the disease impact on patient's health and reduce the economic burden. Continuous monitoring of such diseases helps in the evaluation of rehabilitation program effectiveness as well as in the detection of exacerbation. The use of everyday wearables i.

Adapting to Noise in Speech Obfuscation by Audio Profiling Using Generative Models for Passive Health Monitoring.

Passive health monitoring has been introduced as a solution for continuous diagnosis and tracking of subjects' condition with minimal effort. This is partially achieved by the technology of passive audio recording although it poses major audio privacy issues for subjects. Existing methods are limited to controlled recording environments and their prediction is significantly influenced by background noises.

CoughMatch - Subject Verification Using Cough for Personal Passive Health Monitoring.

Automatic cough detection using audio has advanced passive health monitoring on devices such as smart phones and wearables; it enables capturing longitudinal health data by eliminating user interaction and effort. One major issue arises when coughs from surrounding people are also detected; capturing false coughs leads to significant false alarms, excessive cough frequency, and thereby misdiagnosis of user condition. To address this limitation, in this paper, a method is proposed that creates a personal cough model of the primary subject using limited number of cough samples; the model is used by the automatic cough detection to verify whether the identified coughs match the personal pattern and belong to the primary subject.

CoughGAN: Generating Synthetic Coughs that Improve Respiratory Disease Classification.

Despite the prevalence of respiratory diseases, their diagnosis by clinicians is challenging. Accurately assessing airway sounds requires extensive clinical training and equipment that may not be easily available. Current methods that automate this diagnosis are hindered by their use of features that require pulmonary function tests.

Estimation of the Lung Function Using Acoustic Features of the Voluntary Cough.

Spirometry test, a measure of the patient's lung function, is the gold standard for diagnosis and monitoring of chronic pulmonary diseases. Spirometry is currently being done in hospital settings by having the patients blow the air out of their lungs forcefully and into the spirometer's tubes under the supervision and constant guidance of clinicians. This test is expensive, cumbersome and not easily applicable to every-day monitoring of these patients.

A Comprehensive Approach for Classification of the Cough Type.

Identifying the presence of sputum in the lung is essential in detection of diseases such as lung infection, pneumonia and cancer. Cough type classification (dry/wet) is an effective way of examining presence of lung sputum. This is traditionally done through physical exam in a clinical visit which is subjective and inaccurate.

Opportunistic Environmental Sensing with Smartphones: a Critical Review of Current Literature and Applications.

Purpose Of Review: This review sought to summarize recent literature and applications of passive, or opportunistic, mobile sensing in the fields of exposure science in built environment settings; highlight innovative opportunistic sensing systems; and analyze their functionality, significant features, and limitations.

Recent Findings: Fifty-two papers related to opportunistic environmental sensing from 2009 or later were related to this review, of which 27 were included. An array of applications have emerged in environmental monitoring, employing anywhere from one to six of the phone's on-board sensors.

A dynamic data source selection system for smartwatch platform.

A novel data source selection algorithm is proposed for ambulatory activity tracking of elderly people. The algorithm introduces the concept of dynamic switching between the data collection modules (a smartwatch and a smartphone) to improve accuracy and battery life using contextual information. We show that by making offloading decisions as a function of activity, the proposed algorithm improves power consumption and accuracy of the previous work by 7 hours and 5% respectively compared to the baseline.

Can smartwatches replace smartphones for posture tracking?

This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch's ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches' ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings.