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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 (December 2023)
Smart Healthcare: RL-Based Task Offloading Scheme for Edge-Enable Sensor Networks
Author: Rahul Yadav, Weizhe Zhang, Ibrahim A. Elgendy, Guozhong Dong, Muhammad Shafiq, Asif Ali Laghari, Shiv Prakash
Published in: IEEE Sensors Journal (Volume: 21, Issue: 22, November 2021)
Summary Contributed by: Anupama
Smart healthcare systems produce massive data, which is challenging to manage. The Internet of Medical Things (IoMT) and Artificial intelligence (AI) based smart healthcare systems and applications have shown potential in intelligent and accurate data management and support healthcare. While the edge-enabled network provides necessary computational resources to deal with enormous data, the proposed Computation Offloading using Reinforcement Learning (CORL) algorithm minimizes total latency and energy consumption.
A Radar-Based Human Activity Recognition Using a Novel 3-D Point Cloud Classifier
Author: Zheqi Yu, Ahmad Taha, William Taylor, Adnan Zahid, Khalid Rajab, Hadi Heidari, Muhammad Ali Imran, Qammer H. Abbasi
Published in: IEEE Sensors Journal (Volume: 22, Issue: 19, October 2022)
Summary Contributed by: William Taylor (Author)
Wearable sensors for human activity recognition (HAR) have many uses, especially in health, surveillances and man-machine conversation. Technological advancements have enabled non-invasive, contactless sensing methods to detect human activities. However, insufficient training data severely affects the performance of HAR applications. The paper discusses the dataset collection using 3-D cloud point technology and deep learning algorithms to classify the dataset, which will enable a transition from wearable to contactless sensing.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 8, April 2021)
Summary Contributed by: Laxmeesha Somappa
Origami is the art of paper folding to create a two-dimensional and three-dimensional sculpture. Inspired by this art, the researchers developed a flexible pressure sensor, which finds various applications in wearable devices and healthcare products. The origami structure inherently offers higher sensitivity and measurement range. These pressure sensors can easily be fabricated with 3-D printing technology, making them low-cost and enabling mass production.
Data Fusion Based on Temperature Monitoring of Aquaculture Ponds With Wireless Sensor Networks
Author: Haohui Chen, Xinyuan Nan, Sibo Xia
Published in: IEEE Sensors Journal (Volume: 23, Issue: 1, January 2023)
Summary Contributed by: Haohui Chen (author)
Aquaculture is the farming of aquatic animals and plants in a controlled environment. The water temperature is a vital environmental factor that affects the water quality and life of the aquatic species. The paper proposes an effective real-time aquaculture temperature monitoring method through a layered and clustered wireless sensor networks (WSNs) framework. It is more effective in temperature monitoring with improved accuracy in comparison to the traditional monitoring methods.
Image-Based Force Estimation in Medical Applications: A Review
Author: Ali A. Nazari, Farrokh Janabi-Sharifi, Kourosh Zareinia
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Summary Contributed by: Anupama
Precise, real-time force estimation is still an ongoing challenge in minimally invasive robotic surgical (MIRS) interventions. The applied force depends on the size and deformity of the tissue. Advanced imaging techniques and deep-learning algorithms provide efficient object recognition and force estimation in MIRS. The researchers present a comprehensive review of image-based force estimation techniques for MIRS haptic force feedback.
Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron
Author: Umer Saeed, Syed Yaseen Shah, Adnan Zahid, Jawad Ahmad, Muhammad Ali Imran, Qammer H. Abbasi, Syed Aziz Shah
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
Summary Contributed by: Kamalesh Tripathy
The COVID-19 pandemic highlighted the need for contactless respiration monitoring systems. A software-defined radio frequency sensing technique integrated with a deep learning algorithm was proposed for the non-invasive monitoring of various breathing patterns. The system used variations in channel state information produced by human motions to identify six distinct respiratory patterns. The prototype could classify these respiratory patterns with up to 99% accuracy.
On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review
Author: Muhammad Asif Khan, Hamid Menouar, Aisha Eldeeb, Adnan Abu-Dayya, Flora D. Salim
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Summary Contributed by: Muhammad Asif Khan (Author)
The increasing number of commercial drones poses severe threats to the security of critical infrastructure and people’s privacy. A drone detection system thus becomes inevitable to detect unauthorized drones in the low altitude airspace. This paper delves into the various aspects of an efficient, reliable, robust, and scalable drone detection system by investigating the four fundamental technologies and the associated challenges and limitations.
Inkjet-Printed, Nanofiber-Based Soft Capacitive Pressure Sensors for Tactile Sensing
Author: Riikka Mikkonen, Anastasia Koivikko, Tiina Vuorinen, Veikko Sariola, Matti Mäntysalo
Published in: IEEE Sensors Journal (Volume: 21, Issue: 23, December 2021)
Summary Contributed by: Anupama
Soft electronics enable lighter and more flexible human-machine interfaces. Learn about an inexpensive, additive approach to fabricating flexible, soft electronics using inkjet printing. Inkjet-printed micro-structured dielectric layers were sandwiched between conductive mesh electrodes to form the capacitive tactile pressure sensors. The sensor exhibits high sensitivity, long-term repeatability, and low hysteresis. The proposed approach can fabricate inexpensive, customizable soft electronics human-machine interfaces.
Precise Detection and Quantitative Prediction of Blood Glucose Level With an Electronic Nose System
Author: Zhenyi Ye, Jie Wang, Hao Hua, Xiangdong Zhou, Qiliang Li
Published in: IEEE Sensors Journal (Volume: 22, Issue: 13, July 2022)
Summary Contributed by: Zhenyi Ye (Author)
Monitoring blood glucose levels after exercise, diet, and medication is vital, especially in people with diabetes. A low-cost, no-pain glucose measurement method outside clinical settings for diabetes patients is essential. The work presents a non-invasive glucose measurement method using a novel electronic nose (E-Nose) device enabled by the machine learning algorithm. The proposed method is capable of precise qualitative glucose identification and quantitative analysis.
Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network
Author: Pankaj Kumar Kashyap, Sushil Kumar, Ankita Jaiswal, Mukesh Prasad, Amir H. Gandomi
Published in: IEEE Sensors Journal (Volume: 21, Issue: 16, August 2021)
Summary Contributed by: Vinay S Palaparthy
Smart and precise irrigation planning plays a crucial role in preventing excess water usage and waste. Various machine learning-based irrigation models have been proposed. However, the proposed models should consider unpredictable climate changes. The researchers propose an intelligent neural network model considering the historical temporal dynamics of soil and climate. The prototype efficiently predicts volumetric water demand one day in advance.
Toward a Bio-Inspired Acoustic Sensor: Achroia grisella’s Ear
Author: Lara Díaz-García, Andrew Reid, Joseph C. Jackson-Camargo, James F. C. Windmill
Published in: IEEE Sensors Journal (Volume: 22, Issue: 18, September 2022)
Summary Contributed by: Lara Díaz García (Author)
Taking inspiration from nature can be advantageous when facing challenges in engineering and technology. The researchers overcame one such challenge of manufacturing miniature directional acoustic sensors by studying Achroia grisella, a small moth capable of directional hearing using one ear. Inspired by the shape of the moth eardrum, equations, simulations, and passive directional 3D printed samples were developed and examined with Laser Doppler Vibrometry.
An Implantable Antenna Sensor for Medical Applications
Author: Wei Wang, Xiu-Wei Xuan, Wan-Yi Zhao, Hong-Kuai Nie
Published in: IEEE Sensors Journal (Volume: 21, Issue: 13, July 2021)
Summary Contributed by: Payal Savani
Emerging technologies have led to the development of implantable medical devices, providing new methods for diagnosing and treating diseases. The researchers present a sensor prototype with an S-shaped monopole antenna with a closed-loop design. The prototype outperforms concurrent implantable devices concerning size, radiation gain, and sensitivity. The proposed sensor offers a minimally invasive way to monitor and diagnose cancer tumors and can save countless lives.
Smart bandages can accelerate healing, avoiding infections of severe injuries or surgical wounds by real-time wound assessments. The wound’s healing state can be predicted by tracking parameters like temperature, pressure, pH, and acidity. A smart bandage prototype embedded with wireless temperature and pressure sensors based on a conductive polymer, PEDOT: PSS (poly(3,4-ethylenedioxythiophene) polystyrene sulfonate), and an NFC (Near-field communication) tag is proposed. This battery-less system provides a cost-effective alternative for medical applications.
Metal-Organic Framework Materials Coupled to Optical Fibers for Chemical Sensing: A Review
Author: Chen Zhu, Rex E. Gerald, Jie Huang
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
Summary Contributed by: Dayarnab Baidya
Metal-Organic Frameworks (MOFs) are crystalline nano-porous materials composed of inorganic metal nodes incorporated with organic ligands. Their remarkable structural and physicochemical tunability makes them superior to conventional chemo-sensory materials. The researcher presented a review of MOF-Optical fiber (OF) sensors based on a change in refractive index induced by adsorbed guest molecules. It demonstrated the promising potential of MOFs as dielectric coatings on OF for highly sensitive and selective chemical sensing.
Wireless Characterization and Assessment of an UWB-Based System in Industrial Environments
Author: Imanol Picallo Guembe, Peio Lopez-Iturri, Hicham Klaina, Guillermo Glaria Ezker, Félix Sáez De Jauregui Urdanoz, José Luis Zabalza Cestau, Leyre Azpilicueta, Francisco Falcone
Published in: IEEE Access ( Volume: 9)
Summary Contributed by: Francisco Falcone (Author)
Novel Ultra-Wideband (UWB)-based wireless communication system offers precision location and tracking in industrial settings. The electromechanical interference and heavy machinery can cause severe degradation of signal. The researchers present a hybrid deterministic 3D-RL approximation algorithm for wireless channel characterization of UWB systems in industrial indoor application. The proposed methodology enables optimal system planning and implementation of UWB-based indoor tracking systems in industrial environments.
Maintaining indoor air quality (IAQ) is crucial for health and wellness. Accurate data analysis and contextual anomaly detection are essential for IAQ monitoring. The paper introduces a hybrid deep-learning model, combining long short-term memory (LSTM) with autoencoder (AE). LSTM learns typical carbon dioxide (CO2) time sequence patterns, while AE computes optimal reconstruction errors and detects anomalies. Achieving 99.50% accuracy in real-world testing, the model shows promise for enhancing IAQ monitoring.
Hand gesture recognition has become an integral part of Human-Computer Interactions. The paper introduces a methodology using a video-based dataset and convolutional neural network (CNN) model. It utilizes an RGB-Depth camera to create a dataset of six distinct hand gestures. A lightweight CNN model is then developed to detect and classify hand movements. The experimental results highlight its accuracy and efficiency, facilitating its practical use in scenarios demanding precise gesture recognition.
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