Camera-based physiological measurement is a fast growing field of computer vision.

Humans aren't built for the cold, but have survived frigid temperatures in some amazing cases. Breakthroughs, discoveries, and DIY tips sent six days a week. Winter is not for the faint of heart. In New York City, skyscrapers turn Manhattan into a series of freezing wind tunnels. In Sapporo, Japan, the snowfall is almost 200 inches each winter. Even so, humans have developed plenty of clever ways to wait out the cold. But what would happen if instead of bundling up inside with a hot chocolate, you were left in the frigid cold--just how cold can humans get and recover?

In some remarkable cases, people have survived after their core temperature has plummeted into the 50s. The human body needs to maintain the same internal body temperature or else many vital systems fall apart. Breakthroughs, discoveries, and DIY tips sent every weekday. Whether you prefer sweltering summers or frigid winters, significant temperature changes mean only one thing to your body: bad news. Humans are homeotherms, meaning that our core body temperature stays roughly constant.

Abstract-- This report presents a heterogeneous robotic system designed for remote primary triage in mass-casualty incidents (MCIs). The system employs a coordinated air-ground team of unmanned aerial vehicles (UA Vs) and unmanned ground vehicles (UGVs) to locate victims, assess their injuries, and prioritize medical assistance without risking the lives of first responders. The UA V identify and provide overhead views of casualties, while UGVs equipped with specialized sensors measure vital signs and detect and localize physical injuries. Unlike previous work that focused on exploration or limited medical evaluation, this system addresses the complete triage process: victim localization, vital sign measurement, injury severity classification, mental status assessment, and data consolidation for first responders. Developed as part of the DARPA Triage Challenge, this approach demonstrates how multi-robot systems can augment human capabilities in disaster response scenarios to maximize lives saved. I. INTRODUCTION Robotics has long sought to augment human capabilities in hazardous scenarios. Mass-casualty incidents (MCIs), such as those resulting from natural disasters, bombings, plane crashes, or industrial chemical spills, present an opportunity for robotic systems to assist first responders. The critical first step of providing medical assistance during MCIs is primary triage: the initial process of locating victims at the site of the MCI and assessing the severity of their injuries to prioritize treatment, which is essential to optimizing survival outcomes. Traditionally, primary triage relies on human responders who may face significant risk and information overload [1], underscoring the potential for automated systems to mitigate these challenges. While prior efforts have explored the use of air-ground robotic teams for search and exploration in disaster zones [2]-[5], few systems have focused specifically on rapid triage. Existing approaches typically solve parts of the problem in isolation without integrating comprehensive triage functions. For example, air-ground teams have also been developed to find and localize objects of interest [3], [6] Authors are with the GRASP Lab, School of Engineering and Applied Sciences, University of Pennsylvania. Authors are with the Perelman School of Medicine, University of Pennsylvania. This work was supported by the DARP A Triage Challenge under grant #HR001123S0011.

Abstract--In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a non-contact way using a Frequency Modulated Continuous Wave (FMCW) radar-based system. This work also explores the ambitious goal of extending this capability to an arbitrary number of subjects and details the associated challenges, encompassing both hardware and theoretical limitations. Supported by rigorous experimental results and discussions, the paper paints a vivid picture of the system's potential to redefine vital sign monitoring. An FPGA-based implementation is also presented as proof of concept of an entirely hardware-based and portable solution to vitals monitoring, which improves upon previous works in a multitude of ways, offering 2.7x faster execution and 18.4% lesser Look-Up T able (LUT) utilization and providing over 7400x acceleration compared to its software counterpart. A promising solution to overcome these issues is radar sensing technology for HR and BR measurement, offering non-contact capabilities. This approach also extends to applications including sleep apnea detection [5], fall detection [6] and patient monitoring [7]. This work was supported by the Chips to Startup (C2S) program, Ministry of Electronics and Information Technology (MeitY), Govt. of India, IHub Mobility, IIIT Hyderabad, Kohli Center on Intelligent Systems (KCIS), IIIT Hyderabad and IHub Anubhuti-IIIT Delhi Foundation. Continuous-wave (CW) Doppler Radar systems have significantly advanced this field, addressing various technical challenges in HR and BR measurement [8] [9].

Accurate multivariate time-series prediction of vital signs and laboratory results is crucial for early intervention and precision medicine in intensive care units (ICUs). However, vital signs are often noisy and exhibit rapid fluctuations, while laboratory tests suffer from missing values, measurement lags, and device-specific bias, making integrative forecasting highly challenging. To address these issues, we propose OmniTFT, a deep learning framework that jointly learns and forecasts high-frequency vital signs and sparsely sampled laboratory results based on the Temporal Fusion Transformer (TFT). Specifically, OmniTFT implements four novel strategies to enhance performance: sliding window equalized sampling to balance physiological states, frequency-aware embedding shrinkage to stabilize rare-class representations, hierarchical variable selection to guide model attention toward informative feature clusters, and influence-aligned attention calibration to enhance robustness during abrupt physiological changes. By reducing the reliance on target-specific architectures and extensive feature engineering, OmniTFT enables unified modeling of multiple heterogeneous clinical targets while preserving cross-institutional generalizability. Across forecasting tasks, OmniTFT achieves substantial performance improvement for both vital signs and laboratory results on the MIMIC-III, MIMIC-IV, and eICU datasets. Its attention patterns are interpretable and consistent with known pathophysiology, underscoring its potential utility for quantitative decision support in clinical care.

Intraoperative monitoring and prediction of vital signs are critical for ensuring patient safety and improving surgical outcomes. Despite recent advances in deep learning models for medical time-series forecasting, several challenges persist, including the lack of standardized benchmarks, incomplete data, and limited cross-center validation. To address these challenges, we introduce VitalBench, a novel benchmark specifically designed for intraoperative vital sign prediction. VitalBench includes data from over 4,000 surgeries across two independent medical centers, offering three evaluation tracks: complete data, incomplete data, and cross-center generalization. This framework reflects the real-world complexities of clinical practice, minimizing reliance on extensive preprocessing and incorporating masked loss techniques for robust and unbiased model evaluation. By providing a standardized and unified platform for model development and comparison, VitalBench enables researchers to focus on architectural innovation while ensuring consistency in data handling. This work lays the foundation for advancing predictive models for intraoperative vital sign forecasting, ensuring that these models are not only accurate but also robust and adaptable across diverse clinical environments. Our code and data are available at https://github.com/XiudingCai/VitalBench.

We present a video-based and on-device optical cardiopulmonary vital sign measurement approach.

Researchers around the world are racing to turn pain--medicine's most subjective vital sign--into something a camera or sensor can score as reliably as blood pressure. The push has already produced PainChek--a smartphone app that scans people's faces for tiny muscle movements and uses artificial intelligence to output a pain score--which has been cleared by regulators on three continents and has logged more than 10 million pain assessments. Other startups are beginning to make similar inroads. The way we assess pain may finally be shifting, but when algorithms measure our suffering, does that change the way we treat it? This story is from the latest print issue of MIT Technology Review magazine, which is full of fascinating stories about our bodies. Someone I know became a conspiracy theorist seemingly overnight.