Development of SERA (Smart Ergonomic Rescue Apparel) with Biometric Sensors and LoRa for the Early Detection of Hypothermia in the Waters of the Riau Islands

Conventional biometric spoof detection sensors suffer from several problems, including complex expensive sensor assembly and design. They are susceptible to environmental perturbation and are not robust against all types of spoof attacks. Further, for a secured biometric sensor, the hierarchy of spoofing attempts (i.e., curious attempts, low-level attack, and high-level attack) needs to be precisely distinguished to perform different actions (e.g., a high-level attack should be handled carefully). In this article, we propose a novel three-level antispoof palm print biometric sensor using low-cost components and simple optical techniques (photography, fringe projection, and biospeckle analysis). At the first level, to eliminate the curious futile users, a 2-D image of the palm print is captured, and feature matching is performed. Next, the most prevalent low-level attacks (photo and layered attacks) are detected using fringe projection profilometry. At the final level, a novel biospeckle index based on a subtraction average technique is introduced to quantify the liveliness map for eliminating the high-level attacks, including fake prints and cadavers. The robustness of the biospeckle analysis against different possibilities to generate false activity (tremor, air flow, and heat flow) has also been verified.

In this paper, we present a low cost ultra wideband (UWB) biometric radar sensor for respiration detection and monitoring applications. By using pulse Doppler radar technology, the developed sensor goes beyond detecting the breathing of a single person as conventional radars do; to simultaneously localizing and monitoring multiple human objects as well. The biometric sensor achieves a high range resolution of 3 mm, which makes it capable of detecting very tiny motions, such as breathing and heartbeat. A high unambiguous Doppler velocity of 9 m/s is obtained by the sensor and makes it an attractive device to monitor and investigate any other human physical activities too. Experiments of detecting the location and respiration characteristic of single and multiple human objects will be discussed.

Since 2020, due to the pandemic caused by the COVID-19 virus, the world has come to a standstill, causing the isolation of the population. The vulnerability perceived by the population, as well as the lock-down, have wreaked havoc on the physical and emotional health of most people. One of the main conditions has been stress, which can trigger other conditions such as heart issues, anger, depression, among others. Therefore, at the Autonomous University of Hidalgo State, in the Software Engineering degree program and in collaboration with institutes as the Technologic Institute of Zacatepec, we have proposed to implement a development based on Arduino and biometric sensors which allow monitoring bio-signals to determine the user's perceived level of stress, through connection with a mobile application. For this reason, this document is based on researching, comparing, and analyzing the sensors to be applied to the final project.

Integrating wearable technologies and affective computing into aviation training presents a promising avenue for enhancing Peer Support Programs (PSPs), a critical component in promoting pilots' and aircrew's mental health and well-being. PSPs have traditionally offered emotional and psychological support to aviation professionals, fostering a culture of mutual care and intervention. However, traditional PSPs face challenges in real-time monitoring and early detection of stress, fatigue, and emotional distress. Wearable technologies, such as biometric sensors, smartwatches, and EEG headsets, combined with social and affective computing, provide a novel approach to addressing these limitations by enabling continuous, real-time assessment of physiological indicators linked to emotional and cognitive states.This paper explores how wearable technologies can augment the effectiveness of PSPs by offering several key advantages. First, real-time monitoring through wearables allows for early detection of mental health challenges, enabling timely peer or professional intervention. Second, the data collected from wearable devices can be used to create personalized mental health profiles, allowing for more targeted support tailored to trainees' individual needs. Third, affective computing enhances peer interactions by analyzing emotional states in real time, fostering empathy, and improving group dynamics during training sessions. Fourth, wearable technologies provide real-time feedback to trainees on their emotional and cognitive states, helping them develop self-awareness and emotional regulation skills, which are crucial for high-stress aviation environments.Furthermore, integrating wearable technologies into PSPs supports a broader safety and mental well-being culture in aviation. By normalizing the use of such technologies in training, the industry can reduce the stigma surrounding mental health monitoring and encourage open discussions about psychological resilience. The paper also addresses ethical concerns about data privacy and consent, proposing best practices for handling sensitive biometric and emotional data in aviation training contexts.Based on the literature review, wearable technologies and affective computing offer significant potential to transform PSPs in aviation training. This approach provides a proactive, data-driven approach to supporting the mental and emotional well-being of aviation professionals. This integration not only enhances individual well-being but also contributes to overall aviation safety by ensuring that pilots and crew members are mentally and emotionally prepared for the demands of their roles.The presented research examines how Europe has proactively explored wearable technologies to enhance PSPs. This is partly due to EASA’s comprehensive safety management system requirements, which encourage innovation in mental health support. In the U.S., while there is growing interest in using technology to monitor pilot well-being, regulatory and cultural challenges have slowed the widespread adoption of wearables in PSPs. In both the U.S. and Europe, these technologies can:1. Improve Early Detection: Wearables can help detect signs of stress, fatigue, and emotional distress earlier than traditional PSP methods, allowing for more effective peer support interventions.2. Facilitate Personalized Support: The data collected can be used to create personalized mental health profiles, enabling more tailored support for pilots and crew members.3. Enhance Training and Performance: Real-time feedback on emotional states during training sessions can help pilots develop better emotional regulation and resilience, ultimately improving performance in high-stress environments.Finally, implementing wearable technologies in PSPs requires careful consideration of ethical and privacy concerns. In Europe, robust data protection laws like GDPR provide a model for how sensitive health data can be handled responsibly, balancing privacy with the benefits of real-time monitoring. In the U.S., the lack of a unified regulatory framework presents challenges, but the growing emphasis on safety and well-being in aviation could drive further adoption of these technologies.In conclusion, this paper examines how Wearable Technologies Enhance the Implementation of Peer Support Programs in Aviation Training, following the ICAO ADDIE approach in the USA and Europe.

Alzheimer's disease and related dementias (ADRD) are debilitating neurodegenerative disorders affecting one in every ten older Americans. In the United States, roughly 35.6 million Americans have dementia and approximately 5.7 million have Alzheimer's. By 2050, the count is expected to increase to 13.2 million older Americans. Alzheimer's is the top sixth cause of death in the U.S. People with ADRD face substantial challenges adapting to their physical, psychological and social environments. Like cognitively-intact older adults, ADRD persons prefer to age in place in a familiar environment. However, aging in one's own habitat comes with the potential for loneliness, social isolation, and the consequences of impaired mobility that threaten the displacement of ADRD persons to non-preferred living arrangements. The decrease in multi-generational living and increase in nuclear family living calls for innovative solutions to promote safe mobility, independence, decision making and communication for persons with ADRD. Assistive technologies (ATs) if user friendly and properly engineered for persons with ADRD, hold the potential to enhance daily functioning and improve the quality of life for ADRD persons in living habitats of their choices. This review paper aims to discuss existing and emerging AT megatrends in the Internet of things (IoT) era. Five technological megatrends are examined: assistive robots (e.g. assistance in daily activities, social robots for communication, telepresence robots for social connectedness), biometric sensors and movement sensor technologies (IMURs) for gait and walkability (e.g. non-wearable sensors), multimodal interaction systems for early disease detection, augmented reality systems that

A systematic review of the literature was carried out, key technologies such as machine learning, computer vision, wearable devices and intelligent monitoring systems are identified. These tools are applied in accident prevention, continuous monitoring of workers' health, automation of surveillance and improvement of safety training. The implementation of predictive AI makes it possible to identify risks and prevent accidents, reducing the incident rate and improving safety. Wearable devices and biometric sensors are effective in the early detection of occupational diseases and musculoskeletal disorders. Additionally, automating surveillance with computer vision optimizes compliance with safety standards, such as the use of personal protective equipment (PPE), easing the operational burden on security managers. Despite its benefits, the implementation faces ethical and technical challenges, such as data privacy, algorithm transparency, and worker training. The need to develop clear regulations and an ethical approach in the adoption of AI is highlighted. In conclusion, AI tools have great potential to transform occupational health and safety systems, but it is essential to address ethical challenges and technicians to guarantee its responsible and effective implementation.

There is a huge challenge to reach efficiency of national health systems and Information and Communication Technologies (ICTs) play a significant role towards such objective. The increasing connectivity and the fast development and availability of imaging and biometric sensors as well as of the Internet of Things devices have opened a world of possibilities. One of these examples is given by the automatic distant monitoring of Parkinson's and Alzheimer's patients by the collection of data that could be analyzed to reveal valuable insights for early detection and/or prevention of events related to their condition. In this paper, a complete overview of a system intended to improve the Quality-of-Life (QoL) of such patients is presented. The system collects signals from diverse sensors, identifies the user behavior and context, and triggers proper actions for improving the patient's QoL. The system offers comparable/improved results for the detection of abnormal behavior in daily motion with respect to the state-of-the-art.

The Fast-Moving Consumer Goods (FMCG) sector operates in a dynamic environment, facing numerous challenges in maintaining Health, Safety, and Environment (HSE) standards while meeting the demands of a rapidly evolving market. To address these challenges, integrating advanced technologies has emerged as a strategic approach for enhancing HSE management practices within the FMCG sector. This review explores the integration of various advanced technologies and their impact on improving HSE management in the FMCG industry. The utilization of technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics has revolutionized HSE management practices in the FMCG sector. IoT sensors embedded in production machinery and equipment enable real-time monitoring of environmental conditions, equipment performance, and worker safety. AI-driven predictive analytics algorithms analyze vast amounts of data to identify potential safety hazards, predict equipment failures, and optimize HSE protocols. Furthermore, the adoption of wearable devices equipped with biometric sensors provides continuous health monitoring for employees, ensuring early detection of fatigue, stress, or other health-related issues. Virtual Reality (VR) and Augmented Reality (AR) technologies are utilized for immersive HSE training simulations, enabling employees to practice safety procedures in realistic virtual environments, thus enhancing their preparedness for real-life scenarios. Moreover, the integration of drone technology facilitates remote monitoring of vast operational areas, enabling quick identification of potential hazards and swift response to emergencies. Additionally, blockchain technology ensures the transparency and traceability of HSE data across the supply chain, enhancing accountability and compliance with regulatory standards. The integration of advanced technologies holds significant promise for enhancing HSE management practices in the FMCG sector, fostering a safer and more sustainable operational environment while addressing the evolving challenges of the industry.
 Keywords: HSE, Management, FMCG, Technologies, Technology, Review, Innovation.

Nanomaterial-based sensors are emerging as highly promising tools for the early detection of diseases, offering superior sensitivity, specificity, and miniaturization compared to traditional diagnostic methods. By leveraging the unique properties of nanomaterials such as gold nanoparticles, carbon nanotubes, graphene, and quantum dots, these sensors can detect disease biomarkers at much lower concentrations, enabling earlier diagnosis and intervention. This paper reviews the latest advancements in nanomaterial-based sensors, focusing on their applications in cancer, infectious diseases, and neurological disorders. Key developments include gold nanoparticle-based colorimetric sensors for cancer biomarker detection, graphene-based electrochemical sensors for prostate cancer and other malignancies, and quantum dot-based fluorescence sensors for both cancer and pathogen detection. Despite their promise, challenges remain, including issues with sensitivity, selectivity, reproducibility, biocompatibility, and scalability. Future directions for nanomaterial-based sensors involve improving their performance, developing multiplexed platforms for simultaneous detection of multiple biomarkers, and integrating these sensors into wearable or point-of-care devices. Overcoming these challenges and achieving regulatory approval will be crucial for the widespread clinical adoption of these advanced sensors, ultimately transforming healthcare by enabling more effective, rapid, and accessible early disease detection.

Atrial fibrillation is a leading cause of stroke and early detection and treatment of the condition are critical. Paroxysmal atrial fibrillation is often asymptomatic and may go undetected and untreated in the routine management of patients with ischaemic strokes or transient ischaemic attacks. Prolonged monitoring does increase the diagnosis rate of atrial fibrillation after an ischaemic cerebrovascular event. Biometric and ECG sensors have been integrated with smartphones, apps and wearable devices which may increase rates of diagnosis of arrhythmias. This case study describes an asymptomatic patient who two months after her initial transient ischaemic attack was alerted by her smartwatch about her nocturnal tachycardia and was subsequently diagnosed with atrial fibrillation ensuring appropriate secondary prophylaxis.

Design of ring and cross shaped graphene metasurface sensor for efficient detection of malaria and 2 bit encoding applications

We herein report an organic field-effect transistor (OFET) based chemical sensor for multi-oxyanion detection with pattern recognition techniques. The oxyanions ubiquitously play versatile roles in biological systems, and accessing the chemical information they provide would potentially facilitate fundamental research in diagnosis and pharmacology. In this regard, phosphates in human blood serum would be a promising indicator for early case detection of significant diseases. Thus, the development of an easy-to-use chemical sensor for qualitative and quantitative detection of oxyanions is required in real-world scenarios. To this end, an extended-gate-type OFET has been functionalized with a metal complex consisting of 2,2'-dipicolylamine and a copper(II) ion (CuII-dpa), allowing a compact chemical sensor for oxyanion detection. The OFET combined with a uniform CuII-dpa-based self-assembled monolayer (SAM) on the extended-gate gold electrode shows a cross-reactive response, which suggests a discriminatory power for pattern recognition. Indeed, the qualitative detection of 13 oxyanions (i.e., hydrogen monophosphate, pyrophosphate, adenosine monophosphate, adenosine diphosphate, adenosine triphosphate, terephthalate, phthalate, isophthalate, malonate, oxalate, lactate, benzoate, and acetate) has been demonstrated by only using a single OFET-based sensor with linear discriminant analysis, which has shown 100% correct classification. The OFET has been further applied to the quantification of hydrogen monophosphate in human blood serum using a support vector machine (SVM). The multiple predictions of hydrogen monophosphate at 49 and 89 μM have been successfully realized with low errors, which indicates that the OFET-based sensor with pattern recognition techniques would be a practical sensing platform for medical assays. We believe that a combination of the OFET functionalized with the SAM-based recognition scaffold and powerful pattern recognition methods can achieve multi-analyte detection from just a single sensor.

A Volatile Organic Compounds (VOC) detection system for lung cancer diagnosis through deep learning (DL) technology is implemented in a special Hollow-Core Photonic Crystal Fibre (HC-PCF) sensor platform. COMSOL Multiphysics is used to simulate the HC-PCF. A hexagonal lattice structure of silica material with 1 μm pitch dimensions and 0.5 μm air hole diameters allow for exceptional light guidance and VOC interaction when detecting exhaled breath components. The sensor achieves a remarkable refractive index sensitivity of 920 nm/RIU for detecting cancerous and non-cancerous VOC profiles. The refractive index measurements of lung cancer-related VOC samples fell within 1.380 to 1.392, while VOC samples from healthy patients ranged from 1.350 to 1.360. Sensor spectral response data processing relied on a Convolutional Neural Network (CNN) model that was trained to distinguish different VOC signature patterns. When applied to a dataset of 1,200 breath samples consisting of 600 cancer-positive and 600 healthy specimens, the CNN architecture reached a 96.3% overall classification accuracy combined with 94.7% sensitivity and 97.8% specificity. Full Text: PDF References S. Sharma, L. Tharani, "Photonic Crystal Fiber Sensor Design for Enhanced Tumor Detection: Structural Optimization and Sensitivity Analysis", Photonics Lett. Poland 16(2), 25 (2024). CrossRef A. Yasli, "Cancer Detection with Surface Plasmon Resonance-Based Photonic Crystal Fiber Biosensor", Plasmonics 16, 1605 (2021). CrossRef S. Sharma, S. Das, C.S. Shieh et al. "Design and Numerical Analysis of a Gold-Coated Photonic Crystal Fiber Sensor for Metabolic Disorder Detection with Deep Learning Assistance", Plasmonics (2025). CrossRef N. Ayyanar, G.T. Raja, M. Sharma, D.S. Kumar, "Photonic Crystal Fiber-Based Refractive Index Sensor for Early Detection of Cancer", IEEE Sensors Journal 18(17), 7093 (2018). CrossRef S. Sharma, L. Tharani, "Photonics for AI and AI for photonics integration : Materials and characteristics", J. Information and Optimization Sciences 45(3), 805 (2024). CrossRef M. Babińska, A. Władziński, "Enhanced Sensitivity of Absorption Spectroscopy Glucose Detection by Machine Learning", Photonics Lett. Poland 17(1), 16 (2025). CrossRef M. Babińska, A. Władziński, T. Talaśka, M. Szczerska, "Machine Learning Enhanced Optical Fiber Sensor For Detection Of Glucose Low Concentration In Samples Mimicking Tissue", Photonics Lett. Poland 17(1), 20 (2025). CrossRef

Accelerometer-based IoT wearable sensors for PD symptom detection and assessment are discussed in this chapter. Accelerometers measure PD-related movement patterns and tremors in the IoT system. These discrete body sensors collect non-invasive, real-time data for early symptom detection and continuous monitoring. Accelerometers can track symptoms such tremors, bradykinesia, and postural instability. It also emphasizes early PD detection and how it might improve patient outcomes and lower healthcare expenditures. The integration of machine learning algorithms for data analysis further enriches the capabilities of these wearable sensors, enabling the identification of subtle changes in motor function over time. This chapter concludes that IoT-based accelerometer sensors can transform Parkinson's disease monitoring. By detecting, analyzing, and personalizing care, these sensors may enhance PD patients' lives. IoT accelerometers provide early intervention and better management of this complex neurological disorder.

Myeloperoxidase-based in-vitro test strip sensor for early detection of wound infections at the patient´s bedside