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"IEEE Sensors Alert" is a new service of the IEEE Sensors Council. Started as one of its new initiatives, this weekly digest publishes teasers and condensed versions of our journal papers in layperson's language.
Articles Posted in the Month (May 2022)
Optimization of Sports Training Systems Based on Wireless Sensor Networks Algorithms
Author: Jun Yang and Wu Lv
Published in: IEEE Sensors Journal (Volume: 21, Issue: 22, November 2021)
Summary Contributed by: Anupama
Sports trainers traditionally use vision-based motion recognition technology and qualitative analysis to develop personalized training strategies for each athlete. Wireless Sensor Network-based smart monitoring system can track an athlete’s performance and biometrics in real-time and analyze it on cloud-based platforms. The results can help the trainers develop an informed training strategy for the athletes.
A Linear Slope Analyzing Strategy of GMR Sensor Transfer Curve for Static Detection of Magnetic Nanoparticles
Author: Shuai Yuan , Yimeng Du, and Philip W. T. Pong
Published in: IEEE Sensors Journal (Volume: 21, Issue: 21, November 2021)
Summary Contributed by: Pranjali Maru
Magnetic Nanoparticles (MNPs) have found promising applications in various upcoming technologies. The quantity of MNPs helps detect the biomolecules of the magnetic bio-detection platform marked by magnetic labels. Compared to the traditional subtraction method, a new linear slope analyzing strategy based on giant magnetoresistance (GMR) platform shows impressive performance.
Electronic Waste Reduction through Devices and Printed Circuit Boards designed for Circularity
Author: Moupali Chakraborty; Jeff Kettle; Ravinder Dahiya
Published in: IEEE Journal on Flexible Electronics (Volume: 1, Issue: 1, Jan. 2022)
Summary Contributed by: Ravinder Dahiya (author)
The extensive use of electronic goods has accelerated the threat of the rise in electronics waste (e-Waste). Its unregulated disposal causes environmental and health issues. Despite international policies and associated legalization, the exponential growth in production of waste Printed Circuit boards (WPCBs), use of poor raw material and energy-hungry manufacturing processes have spiked this problem as unsustainable. The use of emerging eco-friendly materials, resource-efficient manufacturing processes, and new technologies are needed to improve the industry's sustainability.
Summary Contributed by: Nauman Ali Choudhry (author)
Electronic textiles (E-textiles) have recently emerged as a promising technology and will soon transform the wearable industry. Due to its flexibility and ease of embedding in garments, wearable sensing has become a favourite choice for continuous health monitoring of athletes and medical patients. Advancements in textile-based sensors significantly impact the quality of life and will play an important role in the field of Internet of Things (IoT).
Though still not widespread in clinical applications, the detection of circulating tumor cells (CTC) attracts researchers' interest as a technique to diagnose and monitor cancer patients. They have been working on developing a miniaturized, portable, low cost, mass-producible device with the potential for automated and non-invasive diagnostics of CTCs in blood samples at point-of-care locations.
Portable Sensing Devices for Detection of COVID-19: A Review
Author: Deniz Sadighbayan, Ebrahim Ghafar-Zadeh
Published in: IEEE Sensors Journal (Volume: 21, Issue: 9, May 2021)
Summary Contributed by: Deniz Sadighbayan, Ebrahim Ghafar-Zadeh (authors)
Portable biosensing systems are crucial in deterrence, timely detection, and intensive care of pandemic-causing diseases like the disastrous COVID-19. The Global emergency led researchers to accelerate the development of portable diagnostic devices for recognizing SARS-CoV-2 and advancing the existing biosensor technology such as electrochemical, optical, and electrical for detecting other viruses and low viral loads.
A PCA-based method to select the number and the body location of piezoresistive sensors in a wearable system for respiratory monitoring
Author: Luigi Raiano, Joshua Di Tocco, Carlo Massaroni, Giovanni Di Pino, Emiliano Schena, Domenico Formica
Published in: IEEE Sensors Journal (Volume: 21, Issue: 5, March 2021)
Summary Contributed by: Leena Jha
The optimal number and body location of piezoresistive sensors to design wearables for monitoring respiratory rate are still debated. A Principal Component Analysis (PCA) based method developed to address this challenge considered different references (i.e., at rest and during walking/running). Trials demonstrated that real-time situations strongly influence the number of sensors and their location to optimize wearable performances.
A Wearable, Multimodal Sensing System to Monitor Knee Joint Health
Author: Caitlin N. Teague, J. Alex Heller, Brandi N. Nevius, Andrew M. Carek , Samer Mabrouk , Florencia Garcia-Vicente, Omer T. Inan , Mozziyar Etemadi
Published in: IEEE Sensors Journal ( Volume: 20, Issue: 18, September 2020)
Summary Contributed by: Anupama
Knee injuries and other minor or chronic knee conditions are prevalent. Monitoring rehabilitation or medication progress in knee treatment is time-consuming, expensive, and requires regular imaging, follow-ups, and several tests. However, knee health can be monitored and “joint health score” calculated remotely with wearable sensors that pick up sound, swelling, temperature and motion. Packaging these sensors into a wearable brace is vital for monitoring the knee.
Janus: A Combined Radar and Vibration Sensor for Beehive Monitoring
Author: Herbert M. Aumann; Margery K. Aumann; Nuri W. Emanetoglu
Published in: IEEE Sensors Letters (Volume: 5, Issue: 3, March 2021)
Summary Contributed by: Anupama
The two-faced sensor system, JANUS, is designed to help beekeepers track bee activities like ‘Swarming’ and ‘Robbing’. The outward-looking Doppler radar monitors the bee flights while the inward-looking piezoelectric transducer senses the vibrations made by bees inside the hive. Researchers were able to use the level, duration and correlation between the two sensor signals to provide sufficient indication about different types of bee activity.
Author: Ernie W. Hill, Aravind Vijayaragahvan, Kostya Novoselov
Published in: IEEE Sensors Journal (Volume: 11, Issue: 12, December 2011)
Summary Contributed by: Anupama
Graphene is often called a ‘miracle material’ due to its exceptional mechanical, electrical and chemical properties. It is a highly conductive, thinnest yet strongest, transparent and non-porous layer of pure carbon atoms in a honeycomb structure. Graphene has immense potential for fabricating various types of flexible sensors like mechanical, magnetic, electrochemical, biosensors, optical sensors etc.
Author: Anuj Kumar, Hiesik Kim, and Gerhard P. Hancke
Published in: IEEE Sensors Journal (Volume: 13, Issue: 4, April 2013)
Summary Contributed by: Jayraj Mulani
Humans are paying a heavy price for economic growth and overall development, whether infrastructure or industrial growth. The pollution and greenhouse gas emissions have led to environmental concerns and climate change, affecting health and life’s quality. However, a rise in environmental awareness created a demand for Environment monitoring systems (EMS) to detect the source and quantify the pollution level by providing a real-time data monitoring and alarm system.
Author: Sergi Foix, Guillem Alenyà, and Carme Torras
Published in: IEEE Sensors Journal (Volume: 11, No: 9, September 2011)
Summary Contributed by: Pranjali Maru
Time of Flight (ToF) camera sensor has emerged as a promising technology. Depth intensity pixel associated higher frame rate images, lightweight, compact design, and reduced power consumption and errors in the output have built great potential for ToF imaging in various domains. Despite its limitations like low resolutions and high noise, the ToF cameras are extensively used in computer graphics, machine vision, and robotics.
Published in: IEEE Sensors Journal (Volume: 1, No: 4, December 2001)
From mechanical to automatic to self-driven cars, the emerging sensors are revolutionizing the automobile industry. Sensors have emerged as essential components of the automotive electronic control system. The three major areas of automotive systems application–powertrain, chassis, and body are all controlled by arrays of sensors. Advancing automotive sensor technologies have a significant impact on the present with immense scope for the future development of automotive systems.
Published in: IEEE Sensors Journal (Volume: 1, Issue: 4, December 2001)
The human desire for accuracy in exploration and guided navigation has brought inventive changes in inertial sensors technology. Integrating inertial sensors with external aids like Doppler, star tracker, or Global Positioning Systems (GPS) improves their accuracy, enhances reliability, and helps overcome inertial drift. Its vast applications in autonomous vehicles, military and space technology, etc., demand the need for extremely low-cost, small size, efficient and batch-producible sensors.
A Wireless, Passive Carbon Nanotube-Based Gas Sensor
Author: Keat Ghee Ong, Kefeng Zeng, and Craig A. Grimes
Published in: IEEE Sensors Journal (Volume: 2, Issue: 2, April 2002)
Multiwall carbon nanotube-silicon dioxide (MWNT-SiO2) coated composite surface has been developed as a linear, responsive, sensitive gas sensors for O2, CO2 and NH3 gases. The presence of gas concentration is measured by measuring corresponding changes in permittivity and conductivity of MWNT which consequently changes its resonant frequency. The advent of MWNT-SiO2 offers an enormous potential to build low cost, highly sensitive, linear, passive, portable, low power wireless gas sensors.
GPS-based navigation, widely used worldwide, is cost-effective for providing position, velocity, and time data. However, it is susceptible to spoofing, especially in UAVs (unmanned aerial vehicles). The paper proposes a GPS spoofing detection and mitigation method using distributed radar tracking and data fusion techniques. The approach combines primary and secondary data through extended Kalman filters and track-to-track association. It ensures accuracy even during spoofing attacks, making it ideal for drone swarms.
Work-related stress should be detected and managed to avoid adverse impacts on individuals and society. This study proposes a deep learning approach to detect work-related stress automatically by analyzing multimodal signals. Deep neural networks, facial expressions, and physiological signals were fused at different levels to achieve promising accuracy. The novel approach of studying the level of work-related stress with just a 10-second-long electrocardiogram, respiration, and facial images shows potential for effective stress detection.
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