Browse Topic: Medical, health, and wellness
Using an inexpensive electrode coated with DNA, MIT researchers have designed disposable diagnostics that could be adapted to detect a variety of diseases, including cancer or infectious diseases such as influenza and HIV.
Researchers at Cornell University, working with collaborators, have created an extremely small neural implant that can sit on a grain of salt. Despite its size, the device can wirelessly transmit brain activity data from a living animal for more than a year.
Researchers at the University of California, Irvine, and New York’s Columbia University have embedded transistors in a soft, conformable material to create a biocompatible sensor implant that monitors neurological functions through successive phases of a patient’s development.
This study evaluates whether a statewide layered medical-drone architecture can improve time-critical EMS logistics in Florida by delivering blood products, AEDs, and critical support devices. We define Time-To-Clinical-Support (TTCS) as the interval from incident recognition to first effective therapy and use Florida EMS benchmark intervals, county-level population and centroid distance data, and p-median hub placement to model system performance. Scenario analysis compares 20-, 40-, and 60-hub deployments and estimates order-of-magnitude effects on AED TTCS and survival gains under explicit assumptions for availability, cruise speed, dispatch overhead, and bystander uptake. The results indicate that a mid-scale network may reduce delay sufficiently to produce meaningful clinical benefit, provided it is integrated with EMS dispatch, medical direction, cold-chain controls, and hurricane-resilient infrastructure. Regulatory pathway constraints, incomplete county-level OHCA data, and uncertainty in mission availability remain the primary limitations on precision and external validity.
This paper investigates the use of full-body vibrotactile cueing to augment operator perception during swarm teleoperation tasks. Piloted simulations are conducted in a virtual reality (VR) flight simulation environment using a quadcopter swarm model and a nonlinear dynamic inversion (NDI) flight control architecture. A scaled version of the ADS-33 slalom Mission Task Element (MTE) is implemented to evaluate swarm formation maintenance and obstacle avoidance under four experimental conditions: Good Visual Environment (GVE), Degraded Visual Environment (DVE), and each of these conditions augmented with haptic feedback. Haptic cues are delivered through vibrotactile vests and sleeves to convey information on formation deformation and gate proximity. Experimental results involving human participants indicate that haptic feedback improves formation maintenance and increases operators’ situational awareness of follower drone positions without increasing perceived mental workload. While haptic cues provided modest assistance in gate localization, visual conditions remained the dominant factor influencing obstacle avoidance performance. Overall, the results indicate that full-body haptic feedback provides an effective modality for augmenting operator perception and supporting swarm supervision tasks, particularly in visually degraded environments.
In response to the 42nd (2025) Annual VFS Student Design Competition, the Graduate Student Design Team from the University of Maryland introduces Wyvern, a novel hydrogen-powered electric compound rotor-craft engineered for maximum loiter and operational safety. Named after a mythical dragon that defies convention by not breathing fire, Wyvern only breathes water vapor by forgoing hydrocarbon combustion in favor of the quiet and clean power of hydrogen. This design reflects not only an aeronautical solution to an engineering challenge but a greater aspiration to reshaping how practical and clean vertical flight can be achieved.
Rotorcraft pilots operating in degraded visual environments encounter significant challenges during hover flight, where the absence of critical visual cues increases the risk of spatial disorientation. At low altitudes and in obstacle-rich environments, even minor losses in situational awareness can have severe consequences. Understanding the visual cues that support stable hover in good visual environments, and how their absence impacts performance and cognitive workload, is essential for mitigating these risks. This study examined key human factors in hover flight, focusing on the role of peripheral vision and microtextures in supporting pilot performance. It evaluated whether naturally relied-upon visual cues in good visual environment conditions can be artificially replicated to restore visual dominance in simulated degraded visual environments. Analysis included flight performance metrics, control inputs, physiological workload indicators, subjective assessments, and pilot feedback. The findings contribute to improved understanding of visual cueing and pilot adaptation in degraded conditions.
With the rapid development of automated driving and the increasing adoption of “zero-gravity” seats, the crash safety of highly reclined occupants has become a critical issue. The current THOR dummy, designed for frontal impacts in the standard upright posture, exhibits limitations when directly applied to reclined seating configurations, including insufficient spinal flexion capability and excessive posterior pelvic rotation. In this study, the thoracolumbar spine kinematics of the THUMS human body model, reconstructed against post-mortem human subject (PMHS) tests, were analyzed. A two-segment linear fitting was employed to characterize a “dummy-like” spinal flexion response, yielding a virtual rotational hinge located near the thoracolumbar joint of the original THOR model. The characteristic rotation angle obtained from THUMS showed a strong linear correlation with the flexion moment of the T12–L1 vertebrae. Based on this relationship, the rotational joint of the THOR dummy was unlocked during impact and assigned a torsional stiffness of 600 Nm/rad. Additional modifications were implemented in the hip region to enhance model applicability. Comparative simulations demonstrated that the modified THOR model achieved closer agreement with PMHS responses than both the Hybrid III and the baseline open-source THOR models. In particular, the posterior pelvic tilt was reduced from approximately 20° in the baseline THOR to about 10° in the modified version. These results indicate that incorporating PMHS-based thoracolumbar flexion characteristics together with targeted hip modifications significantly improves the biofidelity of the THOR dummy for reclined-occupant crash scenarios, providing a solid foundation for future dummy development and safety assessment.
A soft, thread-like implantable bioelectronic device is designed to sense and stimulate tissues with minimal invasiveness. Roughly a quarter of a millimeter in diameter, the NeuroString fiber can incorporate hundreds to thousands of independent electronic channels capable of detecting neurochemicals, monitoring muscle contractions, recording single-neuron activity, or delivering targeted stimulation.
A Detroit-based startup says its device can analyze brain activity to help figure out whether a driver is impaired. The impaired driver-detection business has been heating up since even before NHTSA announced in 2024 that it was working what would eventually be a mandate that vehicles be able to detect impaired drivers and mitigate the danger they represent to the motoring public.
Electric Vehicles (EVs) are rapidly transforming the automotive landscape, offering a cleaner and more sustainable alternative to internal combustion engine vehicles. As EV adoption grows, optimizing energy consumption becomes critical to enhancing vehicle efficiency and extending driving range. One of the most significant auxiliary loads in EVs is the climate control system, commonly referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a substantial portion of the battery's energy—especially under extreme weather conditions—leading to a noticeable reduction in vehicle range. This energy demand poses a challenge for EV manufacturers and users alike, as range anxiety remains a key barrier to widespread EV acceptance. Consequently, developing intelligent climate control strategies is essential to minimize HVAC power consumption without compromising passenger comfort. These strategies may include predictive thermal management, cabin pre-conditioning, zonal climate control, and integration with renewable energy sources. By implementing such energy-efficient solutions, EVs can achieve better range performance, improved user satisfaction, and greater environmental benefits. Modern EV climate control systems increasingly rely on intelligent features such as Auto mode, which dynamically adjusts fan speed, airflow direction, and temperature settings based on real-time cabin and ambient conditions. By leveraging sensor data and adaptive control algorithms, Auto mode optimizes thermal comfort while minimizing unnecessary energy expenditure. This automated regulation plays a crucial role in reducing HVAC-related power consumption, thereby contributing to overall range improvement and enhancing system efficiency without compromising passenger comfort. This study focuses on the development of three distinct Auto mode calibration levels for each set condition, designed to achieve the same cabin temperature with varying dynamic responses and energy consumption profiles. In Auto mode, the cabin temperature is regulated through intelligent control of compressor speed, blower speed, and evaporator temperature. While all Auto levels can maintain the desired setpoint, the time required to reach this temperature and the system’s responsiveness to sudden thermal loads can vary significantly. This study introduces three distinct calibration profiles, each engineered to achieve the same cabin temperature under different dynamic conditions and energy consumption levels. These profiles allow users to choose between faster thermal response or reduced power usage, effectively enabling a trade-off between immediate comfort and extended driving range
Scientists used a “smart” shirt equipped with an electrocardiogram to track participants’ heart-rate recovery after exercise and developed a tool for analyzing the data to predict those at higher or lower risk of heart-related ailments.
Indian passenger car accident data indicates that approximately 44% of crashes are frontal impacts (Refer fig 1). Among the injuries sustained in these crashes, lower leg injuries are notably critical, contributing to nearly 25% of driver occupant injuries (Refer fig 2). To evaluate such injuries, the Bharat New Car Assessment Program (BNCAP) includes lower leg injury metrics as part of the Frontal Offset Deformable Barrier (ODB64) test. While the overall injury performance is assessed at the vehicle level, BNCAP also monitors vehicle interior intrusions—particularly pedal intrusions—as key contributors to lower limb injury severity. A major challenge in frontal crashes is the intrusion of the vehicle's front-end structure into the occupant compartment. Rigid components, particularly the brake pedal assembly, can be displaced rearward during a crash, significantly increasing the risk of lower leg injuries. Therefore, minimizing pedal intrusions into the driver foot-well is critical for enhancing lower leg protection. As part of an innovative safety initiative, Tata Motors has developed a collapsible brake pedal mechanism designed to mitigate lower leg injuries during frontal crashes. This patented system incorporates a series of levers and linkages that disengage upon impact, allowing the brake pedal to collapse and thereby reducing the risk of intrusion-related injuries to the driver lower legs. The mechanism is engineered to be robust, ensuring that normal braking performance and pedal operation remain unaffected during everyday vehicle use, while providing effective injury mitigation in crash scenarios.
With the advent of digital displays in driver cabins in commercial vehicles, drivers are being offered many features that convey some useful or critical information to drivers or prompt the driver to act. Due to the availability of a vast number of features, drivers face decision fatigue in choosing the appropriate features. Many are unaware of all available functionalities displayed in the Human Machine Interface (HMI) System, leading to a bare minimum usage or complete neglect of helpful features. This not only affects driving efficiency but also increases cognitive load, especially in complex driving scenarios. To alleviate the fatigue faced by drivers and to reduce the induced lethargy to choose appropriate features, we propose an AI driven recommendation agent/system that helps the driver choose the features. Instead of manually choosing between multiple settings, the driver can simply activate the recommendation mode, allowing the system to optimize selections dynamically. The novelty of this proposal focuses on introducing Intelligence in HMI Systems in such a way that it will maximize the operational usage and reduce decision fatigue in drivers. In this paper, we aim to propose a novel metric - “Decision fatigue index” to conceptualize both – the reduction in driver's cognitive load and AI models to capture, train based on the data from the driver preferences, road conditions, vehicle dynamics and user customizations. The most relevant mitigation/intervention strategies will be augmented in the HMI, which enhances ease of use, improves safety, and ensures that drivers receive the most relevant assistance.
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