Browse Topic: Consumer electronics

Items (845)
This project was designed to better understand how the activation of SAE International Level 2 (L2) system features affect the duration of secondary task engagement. Four naturalistic driving datasets were used: one that included drivers without L2 experience, two that included drivers with L2 experienced, and one that included drivers of L0 vehicles. Dependent variables that were assessed include frequency of secondary tasks, duration of secondary task, and proportion of time that drivers engaged in cell phone tasks when L2 systems were active compared to when L2 systems were available but inactive. Results suggest that both the frequency and proportion of time drivers engaged in secondary tasks were significantly higher when L2 systems were active compared to when systems were available but inactive. Drivers without L2 experience took longer to perform tasks involving the center stack/instrument panel compared to experienced L2 drivers. These results suggest that drivers demonstrate
Klauer, SheilaDunn, NaomiAnderson, Gabrial T.Barnes, EllenHan, ShuFincannon, ThomasWeaver, Starla
The automotive industry is evolving from a reactive, independently self-determined approach to cybersecurity, complicated by a complex supply chain. Over time, this has resulted in a fragmented industry comprised of any number of proprietary solutions verses a standardized, regulated paradigm to facilitate a platform-oriented approach. This document, an update on collaborative work from the SAE Vehicle Electrical Hardware Security Task Force (TEVEES18B) and GlobalPlatform Automotive Task Force, outlines this transition strategy. An extensible number of additional examples of use cases of Global Platform Technologies are explored in this document.
Mazzara, BillRawlings, Craig
The goal of this study is to quantify the accuracy (bias) and precision (uncertainty) of the time, position, and speed data acquired by a range of consumer-grade devices (4 bike computers, 5 watches, 1 application on 3 smart phones, and a camera) that access Global Positioning System (GPS) satellite signals. We acquired data at each device’s maximum sampling rate (typically 1 Hz) during 207 minutes (twelve sessions of ~17 min) over 61.6 km of road cycling. The time and position data from these devices were compared to real-time kinematic (RTK) data acquired using a differential GPS system, and speed data from these devices were compared to a high-resolution wheel speed sensor synchronized to the RTK data in order to statistically estimate the bias and 95th percentile confidence intervals of the uncertainty of the devices’ data. Overall, we found the position and speed data from the devices generally lagged the reference by 4 s or less, although the lags between the speed and position
Booth, Gabrielle R.Mitchell, Alan L.Siegmund, Gunter P.
Battery fires pose a significant risk across a wide range of applications, including electric vehicles, consumer electronics, and grid-scale energy storage systems. Early detection of fire and smoke is critical to preventing catastrophic failures and ensuring human safety. In this study, we developed a synthetic dataset of battery fire and smoke images in the context of a simple battery pack. The primary application of this dataset is to support the development of a machine learning–based visual classification system capable of accurately detecting battery fires and smoke in real time at an early stage. The intended outcome is a deployable classification system that enhances battery safety through rapid visual identification of hazardous conditions.
Govilesh, VidarshanaGunasekaran, AswinChalla, KarthikeyaMaxim, BruceShen, Jie
In the context of Industry 5.0, effective human–machine collaboration requires seamless and natural interaction. Hand-Gesture Recognition (HGR) has emerged as a promising technology for developing human–machine interfaces (HMI) that enable users to control robotic systems without physical controllers or wearable devices. This research presents a real-time HGR system designed to control a 6-Degree-of-Freedom (DoF) robotic arm using YOLOv10, a state-of-the-art deep learning model for hand gesture detection and classification. While YOLOv10 delivers high recognition accuracy, its computational demands surpass the capabilities of edge devices typically mounted on robotic platforms, creating a hardware bottleneck. To address this challenge, a cooperative client–server architecture is proposed, distributing computational workload between the edge device and a more powerful remote server. An RGB camera attached to the robotic arm captures hand gesture images and transmits them to the server
DeHaven, Aaron LeePark, Jungme
The evolution of wireless communications and the miniaturization of electrical circuits have fundamentally reshaped our lives and the digital landscape. However, as we push toward higher-frequency communications in an increasingly connected world, engineers face growing challenges from multipath propagation — a phenomenon where the same radio signal reaches receiving antennas through multiple routes, usually with time delays and altered amplitudes. Multipath interference leads to many reliability issues, ranging from “ghosting” in television broadcasts to signal fading in wireless communications.
The Automobile Life Extender (ALE) comprises an on-board function, a machine learning model operating via cloud computing and a smartphone app. The on-board function receives signals such as engine RPM, throttle position, brake pedal position, and hydraulic pressure from the vehicle's ECUs. Based on this data, the on-board ALE module calculates the engine load, brake circuit load, etc., and sends it to the predictive maintenance model via the on-board IoT system. The predictive maintenance model contains recorded data about the type of engine, brake system, and their performance curves acquired from tests conducted by its OEM. Machine learning models holds a crucial role in dynamically analyzing vehicle data, identifying drive patterns, and predicting the need for maintenance of a part or system. A hybrid approach of training models based on supervised and unsupervised learning is incorporated, creating an active learning strategy to maximize the use of available data. Amazon SageMaker
Sundaram, RameshselvakumarKumar, LokeshSaint Peter Thomas, EdwinSureshkumar, SrihariMuthukumaran, ChockalingamMenon, Abhijith
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.
Engineers have developed a next-generation wearable system that enables people to control machines using everyday gestures — even while running, riding in a car, or floating on turbulent ocean waves.
Researchers from Harbin Institute of Technology and their collaborators have developed a multifunctional polyelectrolyte hydrogel reinforced with aramid nanofibers (ANFs) and MXene nanosheets, achieving outstanding performance in absorption-dominated electromagnetic interference (EMI) shielding and wearable sensing. This innovative hydrogel addresses the long-standing challenge of balancing electrical conductivity and effective EMI absorption in flexible electronic materials. The research was published in the journal Nano-Micro Letters. 1
Lithium-ion batteries (LIBs) have consolidated their place in the technology market for the energetic transition, with global manufacturing capacity exceeding 1 TWh in recent years and costs falling in this competitive environment. At the same time, the number of end-of-life LIBs is increasing, stimulating the recycling industry to process battery streams, thus promoting the circular economy to meet the increased demand for strategic raw materials and decarbonization. Vehicle electrification is the main driver of battery production, but their end-of-life will take some time to be significant in volume in the next years. Consumer electronics such as smartphones, laptops and power tools are now available at an appropriate volume enabling the preparation of recycling industry for the moment. In this scenario, recyclers are looking for sustainable routes to absorb all these streams and the different LIBs chemistries (LFP, NCA, NMC, LCO, LMO) to recover the critical metals (Ni, Co, Cu, Mn
Gobo, Luciana AssisFerrarese, AndreOliveira, Rafael Piumatti deMartins, Thamiris Auxiliadora GonçalvesGuillen, Daniela RomeroSilva Vasconcelos, David daTenório, Jorge Alberto Soares
Battery technology is at the center of global innovation. From electric vehicles and off-highway machinery to consumer electronics and grid storage, demand for high-performing, reliable batteries has never been higher. This acceleration creates pressure on manufacturers to scale production while safeguarding quality and throughput.
Researchers from RMIT University have developed a wearable wound monitoring device with integrated sensors that could reduce infection risks by minimizing the need for frequent physical contact.
Waiting for a wound to heal is incredibly frustrating. First, it must clot; then an immune system response is needed; followed by scabbing and scarring — and that’s not even getting into the pain part.
During the first two years of life, the motor development of children is monitored closely, as motion is the natural base for their other development and interaction with the environment. Current methods do not allow accurate developmental monitoring throughout early childhood.
CAE (Computer Aided Engineering) optimization plays a pivotal role in various industries to gain a competitive edge. CAE optimization is essential in several industries, such as automotive, aerospace and consumer electronics, etc., concentrating on enhancing component structural design. The process helps in addressing complex design challenges, including weight reduction, material usage efficiency and operational effectiveness. This paper presents applications for an integrated form shape, size and topology optimization approach of structural systems by using CAE tools. For the present study, CAD (Computer Aided Design) was prepared using CATIA V5 followed by meshing in Hyper-mesh 2022.3 version software. Optistruct was used as a solver tool. Modal analysis was performed to extract the natural frequencies of vibration and respective mode shapes. According to the results of the frequency response function study performed on the automobile air conditioning condenser, based on low-stress
Mehra, AkankshaParayil, Paulson
A team of Caltech engineers has developed a technique for inkjet printing arrays of special nanoparticles that enables the mass production of long-lasting wearable sweat sensors. These sensors could be used to monitor a variety of biomarkers, such as vitamins, hormones, metabolites, and medications, in real time, providing patients and their physicians with the ability to continually follow changes in the levels of those molecules.
Although lithium is highly effective to treat bipolar disorder, the chemical has a narrow therapeutic window — too high a dose can be toxic to patients, causing kidney damage, thyroid damage, or even death, while too low a dose renders the treatment ineffective.
In today’s medtech landscape, innovation isn’t just about what a device does — it’s about how reliably and cost-effectively it gets to market. As devices grow smaller, smarter, and more user-centered, materials like liquid silicone rubber (LSR) play a bigger role in enabling performance, comfort, and compliance. From implantables to connected wearables, LSR is helping engineers meet growing design and usability demands. As demand for the material grows, so do the pressures on supply chains, including launch timelines, increased regulatory scrutiny, and rising technical complexity.
Researchers have developed a wearable wound monitoring device with integrated sensors that could reduce infection risks by minimizing the need for frequent physical contact. The proof-of-concept device is designed for reuse, making it more cost-effective and practical than disposable smart bandages and other emerging wound monitoring technologies.
Scientists have produced a new, powerful electricity-conducting material that could improve wearable technologies, including medical devices. The new technique uses hyaluronic acid applied directly to a gold-plated surface to create a thinner, more durable film, or polymer, used to conduct electricity in devices like biosensors. It could lead to major improvements in the function, cost, and usability of devices like touchscreens and wearable biosensors.
In today’s digital age, the use of “Internet-of-Things” devices (embedded with software and sensors) has become widespread. These devices include wireless equipment, autonomous machinery, wearable sensors, and security systems. Because of their intricate structures and properties there is a need to scrutinize them closely to assess their safety and utility and rule out any potential defects. But, at the same time, damage to the device during inspection must be avoided.
Using artificial intelligence (AI) as a key tool to both interpret and accomplish custom design needs remains in its infancy. However, if our partnership with NASA is any indication, we are on the edge of a massive change in the way engineers use AI's immense computing power to develop CAD models for critical applications. This paradigm shift will resonate for years to come as industries such as aerospace, medtech, and consumer electronics, find ways to harness this new technology to speed iteration and go-to-market times. A quick spoiler alert: Using AI still requires well-considered human input to make it all happen and it demands digital manufacturing to do it quickly.
A wearable wristband could significantly improve diabetes management by continuously tracking not only glucose but also other chemical and cardiovascular signals that influence disease progression and overall health.
A fiber sensor inspired by the shape of DNA, developed by researchers at Shinshu University, introduces a new design for more durable, flexible fiber sensors in wearables. Traditional fiber sensors have electrodes at both ends, which often fail under repeated movement when placed on body joints. The proposed double-helical design, however, places both electrodes on one end, allowing the sensor to endure repeated stretching and movement, effectively addressing a key limitation of conventional wearable sensors.
Sensors are used everywhere — from smartphones and wearable devices to industrial systems and logistics. But traditional sensors often rely on rigid components and batteries, limiting their applications in soft systems. To address this, researchers from Shibaura Institute of Technology, Japan, have developed a smarter alternative. Using a paper-folding technique in combination with a triboelectric nanogenerator, they created a novel energy-harvesting sensor with promising potential for next-generation soft devices.
Not a traditional university lab, Harvard University’s Move Lab employs professional engineers, product developers, and academics who work across disciplines to bring research innovations to market. The lab is focused on human performance enhancement to protect people’s physical ability to guard against injury, extend their abilities beyond the limits of advancing age, and restore them to people who have lost them. They have developed wearable solutions that support functional movements and allow impaired individuals to more easily interact with their environment.
When it comes to technology adoption, the healthcare industry is historically risk averse. Despite strict regulations protecting patient data and concerns over medical outcomes, a new report from Mordor Intelligence reports that the global market for wireless portable medical devices is expected to exceed $31.4 billion this year. 1 The same report projects 12.14 percent compound annual growth through 2030 to meet the demands of a burgeoning geriatric population for wearable and implantable devices and in-home vital signs monitoring.
A long-lasting, 3D-printed, adhesive-free wearable provides a more comprehensive picture of a user’s physiological state. The device, which measures water vapor and skin emissions of gases, continuously tracks and logs physiological data associated with dehydration, metabolic shifts, and stress levels.
Engineers have developed a smart lactation pad that can quantify a wide range of chemicals in breast milk in real time. This work is pioneering the first wearable, rapid sensor for at-home measurement of chemicals in breast milk, addressing an important technology gap for improving the health of the mother and the baby.
Today, our mobile phones, computers, and GPS systems can give us very accurate time indications and positioning thanks to the over 400 atomic clocks worldwide. All sorts of clocks - be it mechanical, atomic or a smartwatch - are made of two parts: an oscillator and a counter. The oscillator provides a periodic variation of some known frequency over time while the counter counts the number of cycles of the oscillator. Atomic clocks count the oscillations of vibrating atoms that switch between two energy states with very precise frequency.
Researchers developed wearable skin sensors that can detect what’s in a person’s sweat. Using the sensors, monitoring perspiration could bypass the need for more invasive procedures like blood draws and provide real-time updates on health problems such as dehydration or fatigue. The sensor design can be rapidly manufactured using a roll-to-roll processing technique that essentially prints the sensors onto a sheet of plastic.
Not only the use, but also the wearing time of medical wearables continues to increase in modern healthcare. However, to ensure that wearable products do not cause skin irritation, product designers must consider the moisture vapor transmission rate (MVTR) during development. It plays an important role in skin compatibility and wearing comfort — and can be decisively influenced by the right joining technology.
A team of researchers has developed self-powered, wearable, triboelectric nanogenerators (TENGs) with polyvinyl alcohol (PVA)-based contact layers for monitoring cardiovascular health. TENGs help conserve mechanical energy and turn it into power.
A major challenge in self-powered wearable sensors for health care monitoring is distinguishing different signals when they occur at the same time. Researchers from Penn State and China’s Hebei University of Technology addressed this issue by uncovering a new property of a sensor material, enabling the team to develop a new type of flexible sensor that can accurately measure both temperature and physical strain simultaneously but separately to more precisely pinpoint various signals.
An invention that uses microchip technology in implantable devices and other wearable products such as smart watches can be used to improve biomedical devices including those used to monitor people with glaucoma and heart disease.
This document establishes test plans/procedures for the AS5643 Standard that by itself defines guidelines for the use of IEEE-1394b as a data bus network in military and aerospace vehicles. This test specification defines procedures and criteria for testing device compliance with the AS5643 Standard.
AS-1A Avionic Networks Committee
Apple’s mobile phone LiDAR capabilities can be used with multiple software applications to capture the geometry of vehicles and smaller objects. The results from different software have been previously researched and compared to traditional ground-based LiDAR. However, results were inconsistent across software applications, with some software being more accurate and others being less accurate. (Technical Paper 2023-01-0614. Miller, Hashemian, Gillihan, Benes.) This paper builds upon existing research by utilizing the updated LiDAR hardware that Apple has added to its iPhone 15 smartphone lineup. This new hardware, in combination with the software application PolyCam, was used to scan a variety of crashed vehicles. These crashed vehicles were also scanned using a FARO 3D scanners and Leica RTC 360 scanners, which have been researched extensively for their accuracy. The PolyCam scans were compared to FARO and Leica scans to determine accuracy for point location and scaling. Previous
Miller, Seth HigginsStogsdill, MichaelMcWhirter, Seth
Driver distraction remains a leading cause of traffic accidents, making its recognition critical for enhancing road safety. In this paper, we propose a novel method that combines the Information Bottleneck (IB) theory with Graph Convolutional Networks (GCNs) to address the challenge of driver distraction recognition. Our approach introduces a 2D pose estimation-based action recognition network that effectively enhances the retention of relevant information within neural networks, compensating for the limited data typically available in real-world driving scenarios. The network is further refined by integrating the CTR-GCN (Channel-wise Topology Refinement Graph Convolutional Network), which models the dynamic spatial-temporal relationships of human skeletal data. This enables precise detection of distraction behaviors, such as using a mobile phone, drinking water, or adjusting in-vehicle controls, even under constrained input conditions. The IB theory is applied to optimize the trade
Zhang, JiBai, Yakun
As the main power source for modern portable electronic devices and electric vehicles, lithium-ion batteries (LIBs) are favored for their high energy density and good cycling performance. However, as the usage time increases, battery performance gradually deteriorates, leading to a heightened risk of thermal runaway (TR) increases, which poses a significant threat to safety. Performance degradation is mainly manifested as capacity decline, internal resistance increase and cycle life reduction, which is usually caused by internal factors of LIBs, such as the fatigue of electrode materials, electrolyte decomposition and interfacial chemical reaction. Meanwhile, external factors of LIBs also contribute to performance degradation, such as external mechanical stresses leading to internal structural damage of LIBs, triggering internal short-circuit (ISC) and violent electrochemical reactions. In this paper, the performance degradation of LIBs and TR mechanism is described in detail, as well
Zhou, JingtaoZhong, XiongwuWang, KunjunZhou, YouhangYou, GuojianTang, Xuan
Researchers from Skoltech and the University of Texas at Austin have presented a proof-of-concept for a wearable sensor that can track healing in sores, ulcers, and other kinds of chronic skin wounds, even without the need to remove the bandages. The paper was published in the journal ACS Sensors.
A team of engineers is on a mission to redefine mobility by providing innovative wearable solutions to physical therapists, orthotic and prosthetic professionals, and individuals experiencing walking impairment and disability. Co-founded by Ray Browning and Zach Lerner, Portland-based startup Biomotum, aims “to empower mobility by energizing every step” through their wearable robotics technology.
Writing in Nature Electronics, the Brown University research team describes a novel approach for a wireless communication network that can efficiently transmit, receive, and decode data from thousands of microelectronic chips that are each no larger than a grain of salt.
Robotics researchers have already made great strides in developing sensors that can perceive changes in position, pressure, and temperature — all of which are important for technologies like wearable devices and human-robot interfaces. But a hallmark of human perception is the ability to sense multiple stimuli at once, and this is something that robotics has struggled to achieve.
Engineers have developed a wearable ultrasound device that can provide long-term, wireless monitoring of muscle activity with potential applications in healthcare and human-machine interfaces. Designed to stick to the skin with a layer of adhesive and powered by a battery, the device enables high-resolution tracking of muscle function without invasive procedures. In tests, the device was worn over the rib cage to monitor diaphragm motion and thickness, which are useful for assessing respiratory health. By tracking diaphragm activity, the technology could potentially support patients with respiratory conditions and those reliant on mechanical ventilation.
Researchers have developed a new method to pull moisture from the air and turn that water into electricity. The paper-based wearable device provides sustained high-efficiency power output through moisture capture.
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