WIRED put the latest consumer exoskeletons from Dnsys and Hypershell in a head-to-head test on a pro athletic track. Personal exoskeletons were everywhere at CES 2026 . There were ambitious designs from newcomers WiRobotics, Sumbu, Ascentiz, and Dephy, while Skip Mo/Go was back promoting its long-overdue tech trousers. Dnsys (pronounced Deen-sis), a comparatively well established name, had some new launches to tease, Hypershell was back with its top model, and Ascentiz had us sprinting across the show floor . An exoskeleton is a relatively new class of wearable device designed to enhance, support, or assist human movement, strength, posture, or even physical activity.

Day-to-day variables can also affect results. Sleep, nutrition, hydration, recovery, and even equipment can influence how well someone performs on test day. "The thing about endurance sports is that what you put in is what you get out," says McQuality. In lab testing, his team found that carbon-plated running shoes slightly improve VO2-related performance by increasing efficiency, allowing runners to sustain higher workloads before fatigue sets in. Taken together, these factors help explain why VO2 max is best viewed as a context-dependent snapshot, not a fixed measure of physical fitness. It's most useful when tracked over time, under similar conditions, and alongside other markers of performance and health.

If you find it hard to focus after a wakeful night, it's because your brain is busy trying to catch up on crucial housekeeping. Nearly everyone has experienced it--after a night of poor sleep, your brain might seem foggy, and your mind drifts off when you should be paying attention. A new MIT study reveals what happens biologically as these momentary lapses occur: Your brain is performing essential maintenance that it usually takes care of while you sleep. During a normal night of sleep, the cerebrospinal fluid (CSF) that cushions the brain helps flush away metabolic waste that has built up during the day. In a 2019 study, MIT electrical engineering and computer science professor Laura Lewis, PhD '14, and colleagues showed that the CSF flows rhythmically in and out in a way that's linked to changes in brain waves. To explore what might happen to this CSF flow in a sleep-deprived brain, Lewis, who is also a member of MIT's Institute for Medical Engineering and Science, and her colleagues tested 26 volunteers on several cognitive tasks after they'd been kept awake in the lab and when they were well-rested.

Wellbeing has become such a priceless (or in many cases pricey) endeavour that we can't seem to get enough of it. Last year, we were mainlining magnesium, consuming creatine - a muscle boosting supplement that became mainstream, and we turned to AI chatbots for help with anything from a personalised training regime to a daily meal plan. What is the multi-trillion pound industry focussing on in 2026? Several experts give us their thoughts on what's on the wellbeing agenda. If 2025 was about smashing targets at the gym, tracking runs to the second and lifting heavier and heavier weights, then this year is all about recovery.

We introduce a novel multimodal dataset and benchmark results for continuous PPG recordings during outdoor activities from 16 participants over 13.5 hours,

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.

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.

Embedded Sensing Group ESG Institute of Computer Science in V orarlberg ICV, University of St. Gallen HSG, Switzerland E-mail: leonardo.rossi@student.unisg.ch, Abstract--Triathlon training, which involves high-volume swimming, cycling, and running, places athletes at substantial risk for overuse injuries due to repetitive physiological stress. Current injury prediction approaches primarily rely on training load metrics, often neglecting critical factors such as sleep quality, stress, and individual lifestyle patterns that significantly influence recovery and injury susceptibility. We introduce a novel synthetic data generation framework tailored explicitly for triathlon. This framework generates physiologically plausible athlete profiles, simulates individualized training programs that incorporate periodization and load-management principles, and integrates daily-life factors such as sleep quality, stress levels, and recovery states. We evaluated machine learning models (LASSO, Random Forest, and XGBoost) showing high predictive performance (AUC up to 0.86), identifying sleep disturbances, heart rate variability, and stress as critical early indicators of injury risk. This wearable-driven approach not only enhances injury prediction accuracy but also provides a practical solution to overcoming real-world data limitations, offering a pathway toward a holistic, context-aware athlete monitoring. Triathlon is a demanding multi-sport discipline that combines swimming, cycling, and running.

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I used the public preview of Fitbit's new AI Health Coach and became both faster and noticeably weirder. Someone needs to say it. Someone has to speak up in defense of being mid. Most of us are, as that is the definition of being mid. I work out every day, but I have a full-time job, two kids, a dog, and a spouse.