Browse Topic: Consumer electronics

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A sweat-powered wearable has the potential to make continuous, personalized health monitoring as effortless as wearing a Band-Aid. Engineers at the University of California San Diego have developed an electronic finger wrap that monitors vital chemical levels — such as glucose, vitamins, and even drugs — present in the same fingertip sweat from which it derives its energy.
Anyone who has ever been frustrated by an overheating mobile phone or wondered how devices will withstand the heat generated by artificial intelligence (AI) applications may be excited to learn about a recent discovery that challenges long-standing assumptions about cooling pathways in electronics.
Designing the next generation of wireless wearables, implantables, and real-time external or in-body monitoring devices is a complex challenge that requires highly reliable electronic components that greatly exceed the performance of standard commercial alternatives.
Ultra-miniature sensors are enabling advanced procedures and treatments across a wide range of medical devices, from catheters and neuro interfaces to wearables. But as electromagnetic sensors get smaller, trade-offs begin to emerge — lower sensitivity, less tolerance for environmental influences, and greater susceptibility to interference — underscoring the need for robust testing to ensure accurate, reliable tracking.
Pilot fatigue represents a critical concern in aviation safety, as it can significantly impair cognitive functions, decision-making abilities, and reaction times. In addition to decreasing performance, in-flight chronic fatigue has negative long-term health effects. Possible causes of fatigue include sleep loss, extended time awake, circadian phase irregularities and workload. Conventionally, the risk due to fatigue in aerospace is reduced by flight time limits and controlled rest requirements. Despite regulations limiting flight time and enabling optimal rostering, fatigue cannot be prevented completely. Hence, there is need to detect pilot fatigue in real time. There is ongoing research to detect pilot fatigue using devices that can capture Electroencephalogram (EEG) and Electrocardiogram (ECG). Though these devices have high fidelity, they are intrusive and can limit pilot activity. This limitation could potentially be overcome by non-intrusive devices such as a smart watch/wrist band/goggles which can measure physiological parameters that provide insights into pilot’s mental health. Heart rate variability (HRV) is one such physiological marker of interest for detecting pilot fatigue in real time. HRV can be effectively derived by processing raw Photoplethysmography (PPG) signals to gain insights into the autonomic nervous system, enabling the assessment of physiological state. Wearable devices such as a wristwatch are used in the current study to measure PPG data. Time and frequency domain analysis were performed to evaluate the potential of HRV indices. The analysis of R-R intervals and the Low Frequency / High Frequency (LF/HF) ratio plots, derived from HRV signals, revealed distinct characteristics that differentiate between an alert and a fatigued pilot. This study demonstrates a reliable non-intrusive method for detecting pilot fatigue and enhancing flight safety.
Nyamagoudar, VinayakP R, NamrathaRamachandran, Venkataramani
In today’s global aviation industry, passenger experience is strongly influenced by effective communication. In-flight announcements, often limited to English and a single local language, can create confusion and stress for international travelers who may not be fluent in either. This communication gap not only impacts passenger comfort but also poses potential risks in conveying time-sensitive or safety-critical information. Recent advances in Generative Artificial Intelligence (GenAI), particularly in speech recognition, neural machine translation, and naturalistic text-to-speech, provide a pathway to overcome these challenges. This paper explores the concept of real-time multilingual in-flight announcements delivered in each passenger’s preferred language through connected headphones or personal devices. The proposed system architecture integrates speech-to-text conversion, language translation, and speech synthesis with aircraft infotainment platforms. Potential applications range from pre-generated multilingual safety messages to long-term visions of fully personalized, real-time translations with minimal latency. Benefits include improved inclusivity, accessibility for hearing-impaired passengers, and enhanced brand differentiation for airlines. Challenges such as regulatory certification, translation accuracy, latency constraints, and hardware integration must be addressed. Beyond aerospace, this capability has cross-domain relevance in automotive, railways, and public services, making it a promising area for future customer experience innovations.
Mishra, AshwiniKature, KartikPatil, Ashish
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 a tendency to shift their attention away from the driving task when L2 is engaged.
Klauer, SheilaDunn, NaomiAnderson, Gabrial T.Barnes, EllenHan, ShuFincannon, ThomasWeaver, Starla
As interest in wearable technology has surged, research into creating energy-storage devices that can be woven into textiles has also increased. Researchers at North Carolina State University have now identified a “sweet spot” at which the length of a threadlike energy storage technology called a “yarn-shaped supercapacitor” (YSC) yields the highest and most efficient flow of energy per unit length.
Wide-scale applications for Micro-Electromechanical Systems (MEMS) became practical in the 1980s when they started being fabricated with the same silicon wafer processes as semiconductor chips. Once they could be mass produced, they found a major market in automobile safety systems as inertial sensors for airbag deployment. Then came the introduction of smart phones in the late 2000s. According to Dr. Alissa M. Fitzgerald, CEO, A.M. Fitzgerald & Associates, a MEMS and microtechnology product development company, every smart phone these days contains dozens of MEMS chips. And now there are many more safety applications in automobiles beyond just air bag sensing.
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 data within a device were less (0.0 to 1.2 s) and more precise. We found small position biases (0.1 to 1.4 m), although the major axis of the 95th percentile confidence ellipses of the position uncertainties ranged from ±3.4 to ±7.2 m across the devices. The speed biases were also small (-0.6 to 0.0 m/s) and had 95th percentile confidence intervals that were between 0.35 and 1.04 m/s wide. These findings help establish the accuracy and uncertainty across a range of consumer-grade GPS-enabled devices and to probabilistically interpret these data for collision reconstruction purposes.
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
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
This paper proposes HaloBus, an innovative, edge-computing solution designed to mitigate this risk by detecting student boarding and exiting in real time using lightweight AI based methods. A persistent challenge in elementary school transportation is the issue of missing students after they exit their buses, which disproportionately impacts low-income households. Current safety systems place the burden of implementation on individual households, often requiring independent methods. Common methods include applications on a personal device or a small tracker. However, not everyone can afford these options, and ensuring child safety is a primary concern for parents and caregivers. That is why HaloBus was invented. The system employs YOLOv5us—an Ultralytics-enhanced, anchor-free, split-head architecture that offers a superior accuracy speed trade-off. By providing real-time, on-device alerts, HaloBus enables immediate intervention to prevent a student from being left behind, thereby shifting the focus from reactive post-incident response to proactive safety. Trained on over 70,000 labeled and unlabeled images, the model can accurately detect multiple students simultaneously, significantly reducing false positives. In real-world deployment, the model sustained 30 frames per second on the Raspberry Pi and achieved detection confidence levels exceeding 75% even when subjects wore sunglasses or hoodies. With opt-in participation for each family, HaloBus effectively balances detection efficiency and privacy protection. Overall, HaloBus offers a low-cost, scalable, and ethically conscious approach to enhancing school-bus safety by delivering reliable, on-device boarding and exit detection for multiple students in varied real-world conditions.
Getz, GraysonZadeh, MehrdadTan, Teik-Khoon
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 via the User Datagram Protocol (UDP). The server performs real-time inference using YOLOv10 and returns the detection results to the edge device, which translates the recognized gestures into corresponding robotic arm movements. Experimental evaluation demonstrates an interfacing speed of approximately 15.7 frames per second and an 11.54 times improvement in performance compared with standalone edge-based processing. The proposed cooperative HGR system successfully integrates advanced computer vision techniques with robotic control to deliver a responsive, touch-free interface, enabling smooth, natural HMI. By overcoming edge-computing limitations, this research contributes to the advancement of Industry 5.0, supporting applications in healthcare, assistive robotics, industrial automation, and collaborative robotics, and promoting effective and safe human–machine collaboration.
DeHaven, Aaron LeePark, Jungme
A team of researchers has developed a next-generation wireless ophthalmic diagnostic technology that replaces the existing stationary, darkroom-based retinal testing method by incorporating an ultrathin OLED into a contact lens. This breakthrough is expected to have applications in diverse fields such as myopia treatment, ocular biosignal analysis, augmented-reality (AR) visual information delivery, and lightbased neurostimulation. The researchers say it is the world’s first wireless contact lens-based wearable retinal diagnostic platform using organic light-emitting diodes (OLEDs).
Continuous glucose monitors (CGMs) have reshaped diabetes management by delivering real-time glucose readings, freeing patients from frequent finger-stick testing. These compact, wearable devices not only enhance quality of life but also allow clinicians to adjust therapy based on accurate, continuous data streams.
This research demonstrates a new way to make carbon-based battery materials much safer, longer lasting, and more powerful by fundamentally redesigning how fullerene molecules are connected. Today’s lithium-ion batteries rely mainly on graphite, which limits fastcharging speed and poses safety risks due to lithium plating. These research findings mean progress toward safer electric vehicles, longer-lasting consumer electronics, and more reliable renewable-energy storage.
MIT engineers have developed a technique to grow and peel ultrathin “skins” of electronic material. The method could pave the way for new classes of electronic devices, such as ultrathin wearable sensors, flexible transistors and computing elements, and highly sensitive and compact imaging devices.
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 handles training the ML models, which can be fed into the Amazon Bedrock cloud agent as a customized model. The Amazon Bedrock code interpretation feature assists in visualizing the machine learning model. The model predicts whether repair, replacement, or maintenance is needed. The results from the ML model are displayed to the driver via a smartphone app. Based on the owner's approval, the service center can access the results from the cloud to perform diagnostics even before the vehicle reaches the service station and allocate servicing slots based on their current workload, spare parts availability, and the vehicle owner's schedule. The modular design approach is applied to accommodate other vehicle types, with provisions for model retraining based on new data.
Sundaram, RameshselvakumarKumar, LokeshSaint Peter Thomas, EdwinSureshkumar, SrihariMuthukumaran, ChockalingamMenon, Abhijith
The global medical tubing market is enjoying strong growth, with analysts forecasting compound annual growth rates (CAGR) ranging from 6 to 9 percent over the next several years. Demand is being propelled by more-frequent or extended treatments for cancers and chronic diseases; increases in minimally or noninvasive surgery, home-based care, and patient wearables; and innovations such as sensor-enabled smart catheters and other advanced tubing applications.
When it comes to haptic feedback, most technologies are limited to simple vibrations. But our skin is loaded with tiny sensors that detect pressure, vibration, stretching and more.
The Singapore-MIT Alliance for Research and Technology (SMART), Massachusetts Institute of Technology’s (MIT) research enterprise in Singapore, has launched a new collaborative research project — Wearable Imaging for Transforming Elderly Care (WITEC).
Flexible pressure sensors can detect subtle mechanical stimuli, making them suitable for use in wearable sensors for human health monitoring and motion analysis. However, current sensors suffer from insufficient sensitivity, poor durability, and subpar stability. In a new study, taking inspiration from cat whiskers, researchers developed novel biomass fiber/sodium alginate aerogel (BFA)-based sensors that demonstrated excellent pressure sensitivity, durability, and rapid response, while being suitable for human physiological monitoring and motion analysis.
Engineers have 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.
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.
Stretchable waveguides maintain efficient, stable signal transmission of surface plasmon polaritons even when bent, twisted, or stretched. These plasmonic waveguides could make it possible to seamlessly embed advanced health monitoring functions into everyday wearable materials.
Wearable electronics could be more wearable, according to a research team at Penn State. The researchers developed a scalable, versatile approach to designing and fabricating wireless, Internet-enabled electronic systems that can better adapt to 3D surfaces, like the human body or common household items, paving the path for more precise health monitoring or household automation, such as a smart recliner that can monitor and correct poor sitting habits to improve circulation and prevent long-term problems.
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 and Li). Faced with these problems, Tupy in an Embrapii project with Tecnogreen LAREX at USP has developed a recycling route for EV batteries that extends its feed to electronic batteries in a flexible hydrometallurgical process. This work presents the results of this process, which includes the semi-pilot scale of 20kg LIBs obtained from electronics. Critical metals recovery efficiency was 71% of cell weight, leading for 83% of Co, 93% of Cu, 86% of Ni and 89% of Li. Such initial results exceed the Cu, Ni and Li efficiencies required by the European Union at the end of 2027.
Gobo, Luciana AssisFerrarese, AndreOliveira, Rafael Piumatti deMartins, Thamiris Auxiliadora GonçalvesGuillen, Daniela RomeroSilva Vasconcelos, David daTenório, Jorge Alberto Soares
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.
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.
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 areas, brackets and side plates were optimized, aiming to minimize weight, improve structural strength and optimize material quantity while maintaining the component’s usefulness and safety. Therefore, the sheet metal bracket was optimized for topology and shape, and the side plate was optimized for size. Thereafter, the condenser assembly resulted in a 2.9% reduction in mass.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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