Browse Topic: Medical, health, and wellness
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
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
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.
Severe rear-impact collisions can cause significant intrusion into the occupant compartment when the structural integrity of the rear survival space is insufficient. Intrusion patterns are influenced by impact configuration—underride, in-line, or override—with underride collisions channeling forces below the beltline through the rear wheels as a primary load path. This force concentration rapidly propels the rear seat-pan forward, contacting the rearward-rotating front seatback. The resulting bottoming-out phenomenon produces a forward impulse that amplifies loading on the front occupant’s upper torso, increasing the risk of thoracic injury even when the head is properly supported by the head restraint. This study analyzes a real-world rear-impact collision that resulted in fatal thoracic injuries to the driver, attributed to the interaction between the driver’s seatback and the forward-moving rear seat pan. A vehicle-to-vehicle crash test was conducted to replicate similar intrusion
This study introduces a novel in-cabin health monitoring system leveraging Ultra-Wideband (UWB) radar technology for real-time, contactless detection of occupants' vital signs within automotive environments. By capturing micro-movements associated with cardiac and respiratory activities, the system enables continuous monitoring without physical contact, addressing the need for unobtrusive vehicle health assessment. The system architecture integrates edge computing capabilities within the vehicle's head unit, facilitating immediate data processing and reducing latency. Processed data is securely transmitted via HTTPS to a cloud-based backend through an API Gateway, which orchestrates data validation and routing to a machine learning pipeline. This pipeline employs supervised classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) to analyze features such as temporal heartbeat variability, respiration rate stability, and heart rate. Empirical
Occupant Safety systems are usually developed using anthropomorphic test devices (ATDs), such as the Hybrid III, THOR-50M, ES-2, and WorldSID. However, in compliance with NCAP and regulatory guidelines, these ATDs are designed for specific crash scenarios, typically frontal and side impacts involving upright occupants. As vehicles evolve (e.g., autonomous layouts, diverse occupant populations), ATDs are proving increasingly inadequate for capturing real-world injury mechanisms. This has led to the adoption of computational Human Body Models (HBMs), such as the Global Human Body Models Consortium (GHBMC) and Total Human Model for Safety (THUMS), which offer superior anatomical fidelity, variable anthropometry, active muscle behaviour modelling, and improved postural flexibility. HBMs can predict internal injuries that ATDs cannot, making them valuable tools for future vehicle safety development. This study uses a sled CAE simulation environment to analyze the kinematics of the HBMs
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
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
One of the biggest goals for companies in the field of artificial intelligence (AI) is developing “agentic” systems. These metaphorical agents can perform tasks without a guiding human hand. This parallels the goals of the emerging urban air mobility industry, which hopes to bring autonomous flying vehicles to cities around the world. One company wants to do both and got a head start with some help from NASA.
Cornell researchers and collaborators have developed a neural implant so small that it can rest on a grain of salt, yet it can wirelessly transmit brain activity data in a living animal for more than a year.
Trying to document how single brain cells participate in networks that govern behavior is a daunting task. Brain probes called Neuropixels, which feature high-density silicon arrays, have enabled scientists to collect electrophysiological data of this nature from a variety of animals. These include fish, reptiles, rodents, and primates, as well as humans.
Researchers have pioneered a 3D printing method that grows metals and ceramics inside a water-based gel, resulting in exceptionally dense, yet intricate constructions for next-generation biomedical technologies.
Researchers are exploring new ways to utilize microwave technology in monitoring and assessing health conditions. The results of experiments conducted with realistic models are promising. Bras that detect breast cancer, leg sleeves that identify blood clots, and a helmet that monitors the effects of radiation therapy offer a glimpse into what future healthcare might look like.
EPFL researchers have invented a remarkably small and ultraflexible neurovascular microcatheter. Powered by blood flow, it can safely navigate the most intricately branched arteries in a matter of seconds.
Bioelectronics, such as implantable health monitors or devices that stimulate brain cells, are not as soft as the surrounding tissues due to their metal electronic circuits. A team of scientists has developed a soft polymer hydrogel that can conduct electricity as well as metal can. As the material is both flexible and soft, it is more compatible with sensitive tissues. This finding has the potential for a large number of applications, for example, in biocompatible sensors and in wound healing.
EPFL researchers have engineered a fiber-based electronic sensor that remains functional even when stretched to over 10 times its original length. The device holds promise for smart textiles, physical rehabilitation devices, and soft robotics.
University of Texas at Dallas researchers have developed biosensor technology that when combined with artificial intelligence (AI) shows promise for detecting lung cancer through breath analysis.
Spinoff is NASA’s annual publication featuring successfully commercialized NASA technology. This commercialization has contributed to the development of products and services in the fields of health and medicine, consumer goods, transportation, public safety, computer technology, and environmental resources.
Researchers combined mussel adhesive protein with decellularized extracellular matrix (dECM) to develop a composite hemostatic sponge that offers both strong tissue adhesion and biocompatible biodegradability.
A low-cost, portable biosensor can quickly identify a protein whose altered levels are associated with psychiatric disorders, such as depression, schizophrenia, and bipolar disorder. When it becomes commercially available in the future, it may contribute to early detection, which is essential for treating and monitoring patients’ clinical conditions.
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