Cycle rickshaw pullers are highly vulnerable to extreme heat, yet little is known about how their physiological biomarkers respond under such conditions. This study collected real-time weather and physiological data using wearable sensors from 100 rickshaw pullers in Dhaka, Bangladesh. In addition, interviews with 12 pullers explored their knowledge, perceptions, and experiences related to climate change. We developed a Linear Gaussian Bayesian Network (LGBN) regression model to predict key physiological biomarkers based on activity, weather, and demographic features. The model achieved normalized mean absolute error values of 0.82, 0.47, 0.65, and 0.67 for skin temperature, relative cardiac cost, skin conductance response, and skin conductance level, respectively. Using projections from 18 CMIP6 climate models, we layered the LGBN on future climate forecasts to analyze survivability for current (2023-2025) and future years (2026-2100). Based on thresholds of WBGT above 31.1°C and skin temperature above 35°C, 32% of rickshaw pullers already face high heat exposure risk. By 2026-2030, this percentage may rise to 37% with average exposure lasting nearly 12 minutes, or about two-thirds of the trip duration. A thematic analysis of interviews complements these findings, showing that rickshaw pullers recognize their increasing climate vulnerability and express concern about its effects on health and occupational survivability.

Good luck hiding how you feel. Researchers from Penn State University believe they have developed a stretchy, Band-Aid-sized wearable device capable of decoding even the most advanced poker face. The device attaches to a subject's skin and uses sensors to independently detect physiological responses, such as skin temperature and perspiration, in real time. That data is then digitized and analyzed by an AI model designed to determine the type of emotional responses the wearer is experiencing. In testing, the device was able to accurately identify the correct emotional response 89 percent of the time--significantly more accurate, the researchers say, than simply observing a person's facial expression.

Around 5-10\% of newborns need assistance to start breathing. Currently, there is a lack of evidence-based research, objective data collection, and opportunities for learning from real newborn resuscitation emergency events. Generating and evaluating automated newborn resuscitation algorithm activity timelines relative to the Time of Birth (ToB) offers a promising opportunity to enhance newborn care practices. Given the importance of prompt resuscitation interventions within the "golden minute" after birth, having an accurate ToB with second precision is essential for effective subsequent analysis of newborn resuscitation episodes. Instead, ToB is generally registered manually, often with minute precision, making the process inefficient and susceptible to error and imprecision. In this work, we explore the fusion of Artificial Intelligence (AI) and thermal imaging to develop the first AI-driven ToB detector. The use of temperature information offers a promising alternative to detect the newborn while respecting the privacy of healthcare providers and mothers. However, the frequent inconsistencies in thermal measurements, especially in a multi-camera setup, make normalization strategies critical. Our methodology involves a three-step process: first, we propose an adaptive normalization method based on Gaussian mixture models (GMM) to mitigate issues related to temperature variations; second, we implement and deploy an AI model to detect the presence of the newborn within the thermal video frames; and third, we evaluate and post-process the model's predictions to estimate the ToB. A precision of 88.1\% and a recall of 89.3\% are reported in the detection of the newborn within thermal frames during performance evaluation. Our approach achieves an absolute median deviation of 2.7 seconds in estimating the ToB relative to the manual annotations.

Machine learning was applied to create a digital twin of a numerical simulation of a single-scroll jet engine. A similar model based on the insights gained from this numerical study was used to create a digital twin of a JetCat P100-RX jet engine using only experimental data. Engine data was collected from a custom sensor system measuring parameters such as thrust, exhaust gas temperature, shaft speed, weather conditions, etc. Data was gathered while the engine was placed under different test conditions by controlling shaft speed. The machine learning model was generated (trained) using a next-generation reservoir computer, a best-in-class machine learning algorithm for dynamical systems. Once the model was trained, it was used to predict behavior it had never seen with an accuracy of better than 1.8% when compared to the testing data.

Type 1 diabetes is a serious disease in which individuals are unable to regulate their blood glucose levels, leading to various medical complications. Artificial pancreas (AP) systems have been developed as a solution for type 1 diabetic patients to mimic the behavior of the pancreas and regulate blood glucose levels. However, current AP systems lack detection capabilities for exercise-induced glucose intake, which can last up to 4 to 8 hours. This incapability can lead to hypoglycemia, which if left untreated, could have serious consequences, including death. Existing exercise detection methods are either limited to single sensor data or use inaccurate models for exercise detection, making them less effective in practice. In this work, we propose an ensemble learning framework that combines a data-driven physiological model and a Siamese network to leverage multiple physiological signal streams for exercise detection with high accuracy. To evaluate the effectiveness of our proposed approach, we utilized a public dataset with 12 diabetic patients collected from an 8-week clinical trial. Our approach achieves a true positive rate for exercise detection of 86.4% and a true negative rate of 99.1%, outperforming state-of-the-art solutions.

While acute stress has been shown to have both positive and negative effects on performance, not much is known about the impacts of stress on students grades during examinations. To answer this question, we examined whether a correlation could be found between physiological stress signals and exam performance. We conducted this study using multiple physiological signals of ten undergraduate students over three different exams. The study focused on three signals, i.e., skin temperature, heart rate, and electrodermal activity. We extracted statistics as features and fed them into a variety of binary classifiers to predict relatively higher or lower grades. Experimental results showed up to 0.81 ROC-AUC with k-nearest neighbor algorithm among various machine learning algorithms.

A smart ring designed exclusively for women will do what no husband would ever dream of – tell them how to snap out of their mood. The Evie ring will monitor the wearer's menstrual cycles, sleep patterns, and other vital statistics in a bid to help her'learn how to feel her best'. Rather than provide the data in complex graphs and charts, the results will instead be simplified into'actionable insights' for the user to change their lifestyle. The Californian-based firm behind the smart ring, Movano, is aiming for it to become the first wearable to also be approved as a medical device. The Evie ring will monitor the wearer's menstrual cycles, sleep patterns, and other vital statistics in a bid to help her'learn how to feel her best' Alongside monitoring heart rate, respiration rate, and skin temperature, the ring will also track users' ovulation, periods, and menstrual symptoms.

In the context of aircraft system performance assessment, deep learning technologies allow to quickly infer models from experimental measurements, with less detailed system knowledge than usually required by physics-based modeling. However, this inexpensive model development also comes with new challenges regarding model trustworthiness. This work presents a novel approach, physics-guided adversarial machine learning (ML), that improves the confidence over the physics consistency of the model. The approach performs, first, a physics-guided adversarial testing phase to search for test inputs revealing behavioral system inconsistencies, while still falling within the range of foreseeable operational conditions. Then, it proceeds with physics-informed adversarial training to teach the model the system-related physics domain foreknowledge through iteratively reducing the unwanted output deviations on the previously-uncovered counterexamples. Empirical evaluation on two aircraft system performance models shows the effectiveness of our adversarial ML approach in exposing physical inconsistencies of both models and in improving their propensity to be consistent with physics domain knowledge.

Wearable activity trackers that monitor changes in skin temperature and heart and breathing rates, combined with artificial intelligence (AI), might be used to pick up COVID-19 infection days before symptoms start, suggests preliminary research published in the open access journal BMJ Open. The researchers base their findings on wearers of the AVA bracelet, a regulated and commercially available fertility tracker that monitors breathing rate, heart rate, heart rate variability, wrist skin temperature and blood flow, as well as sleep quantity and quality. Typical COVID-19 symptoms may take several days after infection before they appear during which time an infected person can unwittingly spread the virus. Attention has started to focus on the potential of activity trackers and smartwatches to detect all stages of COVID-19 infection in the body from incubation to recovery, with the aim of facilitating early isolation and testing of those with the infection. The researchers therefore wanted to see if physiological changes, monitored by an activity tracker, could be used to develop a machine learning algorithm to detect COVID-19 infection before the start of symptoms. Participants (1163 all under the age of 51) were drawn from the GAPP study between March 2020 and April 2021.

Wearable activity trackers that monitor changes in skin temperature and heart and breathing rates, combined with artificial intelligence (AI), might be used to pick up COVID-19 infection days before symptoms start, suggests preliminary research published in the open access journal BMJ Open. The researchers base their findings on wearers of the AVA bracelet, a regulated and commercially available fertility tracker that monitors breathing rate, heart rate, heart rate variability, wrist skin temperature and blood flow, as well as sleep quantity and quality. Typical COVID-19 symptoms may take several days after infection before they appear during which time an infected person can unwittingly spread the virus. Attention has started to focus on the potential of activity trackers and smartwatches to detect all stages of COVID-19 infection in the body from incubation to recovery, with the aim of facilitating early isolation and testing of those with the infection. The researchers therefore wanted to see if physiological changes, monitored by an activity tracker, could be used to develop a machine learning algorithm to detect COVID-19 infection before the start of symptoms.