Browse Topic: Anatomy
Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load [1]; however, these reductions can also be related to novice driver inexperience [2] and alcohol intoxication [3]. Through our analysis of the
The effect of seat belt misuse and/or misrouting is important to consider because it can influence occupant kinematics, reduce restraint effectiveness, and increase injury risk. As new seatbelt technologies are introduced, it is important to understand the prevalence of seatbelt misuse. This type of information is scarce due to limitations in available field data coding, such as in NASS-CDS and FARS. One explanation may be partially due to assessment complexity in identifying misuse and/or misrouting. An objective of this study was to first identify types of lap-shoulder belt misuse/misrouting and associated injury patterns from a literature review. Nine belt misuse/misrouting scenarios were identified including shoulder belt only, lap belt only, or shoulder belt under the arm, for example, while belt misrouting included lap belt on the abdomen, shoulder belt above the breasts, or shoulder belt on the neck. Next, the literature review identified various methods used to assess misuse
Komatsu introduced its first battery-electric load-haul-dump (LHD) machine, the WX04B, at the MINExpo tradeshow in September. The WX04B is designed specifically for narrow vein mines in underground hard rock mining operations. Komatsu is pairing the electric LHD with its new OEM-agnostic 150-kW battery charger that was also revealed in Las Vegas. The 4-tonne WX04B LHD features what Komatsu claims is best-in-class energy density, offering up to four hours of runtime on a single charge. The Li-ion NMC (nickel-manganese-cobalt) battery from Proterra has a capacity of 165 kWh and nominal voltage of 660 V. Fewer charge cycles are needed compared to competitors, the company claims, which helps to maximize operational efficiency and minimize downtime. Proterra and Komatsu began their collaboration on the LHD's H Series battery system in 2021, long before Komatsu's acquisition of American Battery Solutions (ABS) in December 2023.
A silicone membrane for wearable devices is more comfortable and breathable thanks to better-sized pores made with the help of citric acid crystals. The new preparation technique fabricates thin, silicone-based patches that rapidly wick water away from the skin. The technique could reduce the redness and itching caused by wearable biosensors that trap sweat beneath them. The technique was developed by bioengineer and professor Young-Ho Cho and his colleagues at KAIST and reported in the journal Scientific Reports.
A thin film that combines an electrode grid and LEDs can both track and produce a visual representation of the brain’s activity in real time. The device is designed to provide neurosurgeons visual information about a patient’s brain to monitor brain states during surgical interventions to remove brain lesions including tumors and epileptic tissue.
The Hospital for Sick Children/University of Toronto Toronto, ON, Canada
University of Utah Salt Lake City, UT
Brain-machine interfaces enable direct communication between a brain’s electrical activity and an external device such as a computer or a robotic limb that allows people to control machines using their thoughts. Researchers have developed a novel biohybrid neuroprosthetic research platform comprised of a dexterous artificial hand electrically interfaced with biological neural networks.
Rear-end vehicle collisions may lead to whiplash-associated disorders (WADs), comprising a variety of neck and head pain responses. Specifically, increased axial head rotation has been associated with the risk of injuries during rear impacts, while specific tissues, including the capsular ligaments, have been implicated in pain response. Given the limited experimental data for out-of-position rear impact scenarios, computational human body models (HBMs) can inform the potential for tissue-level injury. Previous studies have considered external boundary conditions to reposition the head axially but were limited in reproducing a biofidelic movement. The objectives of this study were to implement a novel head repositioning method to achieve targeted axial rotations and evaluate the tissue-level response for a rear impact condition. The repositioning method used reference geometries to rotate the head to three target positions, showing good correspondence to reported interverbal rotations
Thorax injuries are a significant cause of mortality in automotive crashes, with varying susceptibility across sex and age demographics. Finite element (FE) human body models (HBMs) offer the potential for injury outcome analysis by incorporating anthropometric variations. Recent advancements in material constitutive models, cortical bone fracture and continuum damage mechanics model (CFraC) and an orthotropic trabecular bone model (OrthoT), offer the opportunity to further improve rib models. In this study, the CFraC and OrthoT material modes, coupled with age-specific material properties, were progressively implemented to the Global Human Body Model Consortium small female 6th rib. Four distinct 6th rib models were developed and compared against sex and age-specific experimental data. The updated material models notably refined the predictions of force–displacement responses, aligning them more closely with the experimental averages. The CFraC model significantly improved the
Ongoing research in simulated vehicle crash environments utilizes postmortem human subjects (PMHS) as the closest approximation to live human response. Lumbar spine injuries are common in vehicle crashes, necessitating accurate assessment methods of lumbar loads. This study evaluates the effectiveness of lumbar intervertebral disc (IVD) pressure sensors in detecting various loading conditions on component PMHS lumbar spines, aiming to develop a reliable insertion method and assess sensor performance under different loading scenarios. The pressure sensor insertion method development involved selecting a suitable sensor, using a customized needle-insertion technique, and precisely placing sensors into the center of lumbar IVDs. Computed tomography (CT) scans were utilized to determine insertion depth and location, ensuring minimal tissue disruption during sensor insertion. Tests were conducted on PMHS lumbar spines using a robotic test system for controlled loading in flexion
A flexible and stretchable cell has been developed for wearable electronic devices that require a reliable and efficient energy source that can easily be integrated into the human body. Conductive material consisting of carbon nanotubes, crosslinked polymers, and enzymes joined by stretchable connectors, are directly printed onto the material through screenprinting.
In the quest to develop lifelike materials to replace and repair human body parts, scientists face a formidable challenge: Real tissues are often both strong and stretchable and vary in shape and size.
Researchers have succeeded in adding finger straightening or extension to soft rehabilitation gloves through a novel foldable pouch actuator (FPA) without compromising the already existing functionality of finger bending or flexion.
Road safety remains a critical concern globally, with millions of lives lost annually due to road accidents. In India alone, the year 2021 witnessed over 4,12,432 road accidents resulting in 1,53,972 fatalities and 3,84,448 injuries. The age group most affected by these accidents is 18-45 years, constituting approximately 67% of total deaths. Factors such as speeding, distracted driving, and neglect to use safety gear increases the severity of these incidents. This paper presents a novel approach to address these challenges by introducing a driver safety system aimed at promoting good driving etiquette and mitigating distractions and fatigue. Leveraging Raspberry Pi and computer vision techniques, the system monitors driver behavior in real-time, including head position, eye blinks, mouth opening and closing, hand position, and internal audio levels to detect signs of distraction and drowsiness. The system operates in both passive and active modes, providing alerts and alarms to the
Neurostimulators, also known as brain pacemakers, send electrical impulses to specific areas of the brain via special electrodes. It is estimated that some 200,000 people worldwide are now benefiting from this technology, including those who suffer from Parkinson’s disease or from pathological muscle spasms. According to Mehmet Fatih Yanik, professor of neurotechnology at ETH Zurich, further research will greatly expand the potential applications: instead of using them exclusively to stimulate the brain, the electrodes can also be used to precisely record brain activity and analyze it for anomalies associated with neurological or psychiatric disorders. In a second step, it would be conceivable in future to treat these anomalies and disorders using electrical impulses.
A research team at RCSI University of Medicine and Health Sciences has developed a new implant that conveys electrical signals and may have the potential to encourage nerve cell (neuron) repair after spinal cord injury.
The industrial internet of things (IIoT) is the nervous system in manufacturing facilities worldwide, with programmable logic controllers (PLCs) serving as its vital synapses. This digital neural network is transforming isolated machines into interconnected ecosystems of unprecedented intelligence and efficiency. PLCs have evolved from simple control devices into sophisticated nodes in a vast, responsive network.
I’m guessing that when you think about applications for AI, physical fitness is not the first thing that comes to mind. But it is actually one of the more common applications. The most popular fitness sensors use AI to keep track of how many steps you’ve taken, track your progress, and integrate that with health information like heartrate or even blood oxygen level. But AI can do much more than that.
University of Waterloo Chemical Engineering Researcher Dr. Elisabeth Prince teamed up with researchers from the University of Toronto and Duke University to design the synthetic material made using cellulose nanocrystals, which are derived from wood pulp. The material is engineered to replicate the fibrous nanostructures and properties of human tissues, thereby recreating its unique biomechanical properties.
A new coronavirus test can get accurate results from a saliva sample in less than 30 minutes, researchers report in the journal Nature Communications. Many of the components of the handheld device used in this technology can be 3D printed, and the test can detect as little as one viral particle per 1-μL drop of fluid.
A new groundbreaking “smart glove” is capable of tracking the hand and finger movements of stroke victims during rehabilitation exercises. The glove incorporates a sophisticated network of highly sensitive sensor yarns and pressure sensors that are woven into a comfortable stretchy fabric, enabling it to track, capture, and wirelessly transmit even the smallest hand and finger movements.
Items per page:
50
1 – 50 of 5055