Search Results (105)

As people age, more careful health monitoring becomes increasingly important. The article presents the development and implementation of an integrated system for monitoring the health of elderly individuals using Internet of Things (IoT) technology and a wearable bracelet to continuously collect vital data. The device integrates MAX30100 sensors for heart rate monitoring and MPU-6050 for step counting and sleep quality analysis (deep and superficial sleep). The collected data for average heart rate (AR), minimum (mR), maximum (MR), number of steps (S), deep sleep time (DST), and superficial sleep time (SST) is processed in real-time through a health anomaly detection algorithm (HADA), based on the dimensionality reduction method using PCA. The system is connected to the Azure cloud infrastructure, ensuring secure data transmission, preprocessing, and the automatic generation of alerts for prompt medical interventions. Studies conducted over two years demonstrated a sensitivity of 100% and an accuracy of 98.5%, with a tendency to generate additional alerts to avoid overlooking critical events. The results outline the importance of personalizing the analysis, adapting algorithms to individual characteristics, and the system’s potential to prevent medical complications and improve the quality of life for elderly individuals. Full article

Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This paper presents a novel gamified, AI-driven system embedded within Internet of Things (IoT)-enabled environments to address this gap. The proposed platform combines micro-games, adaptive surveys, sensor data, and AI analytics to support personalized and context-aware depression monitoring and self-regulation. Unlike traditional static models, this system continuously tracks behavioral, cognitive, and environmental patterns. This data is then used to deliver timely, tailored interventions. One of its key strengths is a research-ready design that enables real-time simulation, algorithm testing, and hypothesis exploration without relying on large-scale human trials. This makes it easier to study cognitive and emotional trends and improve AI models efficiently. The system is grounded in metacognitive principles. It promotes user engagement and self-awareness through interactive feedback and reflection. Gamification improves the user experience without compromising clinical relevance. We present a unified framework, robust evaluation methods, and insights into scalable mental health solutions. Combining AI, IoT, and gamification, this platform offers a promising new approach for smart, responsive, and data-driven mental health support in modern living environments. Full article

The rising global burden of chronic diseases and cancer in the workplace has intensified the need for accessible, rapid diagnostic strategies within workplace settings. Point-of-care testing (POCT) offers a decentralized solution, providing timely diagnostic insights without the need for centralized laboratory facilities. In the workplace, POCT offers significant advantages for early detection and management of cancer and chronic diseases, improving employee health outcomes and reducing absenteeism. Concurrently, the development of digital health passports has created secure, dynamic platforms for managing and sharing personal health data. This review explores the technological innovations underpinning POCT, examines its application in workplace health screening, and analyzes how integration with the Internet of Things (IoT) and digital health passports can enhance early detection and chronic disease management. The discussion extends to the ethical, regulatory and practical challenges associated with implementation. Furthermore, emerging trends such as artificial intelligence-driven diagnostics, blockchain-enabled data security and wearable biosensors are considered as potential future directions. Together, POCT and digital health passports represent a significant evolution towards proactive, personalized workplace healthcare systems. Full article

Personal health records (PHRs) are digital repositories that allow individuals to access, manage, and share their health information. By enabling patients to track their health over time and communicate effectively with healthcare providers, personal health records support more personalized care and improve the quality of healthcare. Their integration with emerging technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain, enhances the utility and security of health data management, facilitating continuous health monitoring, automated decision support, and secure, decentralized data exchange. Despite their potential, PHR systems face significant challenges, including privacy concerns, security issues, and digital accessibility problems. This paper discusses the fundamental concepts, requirements, system architectures, and data sources that underpin modern PHR implementations, highlighting how they enable continuous health monitoring through the integration of wearable sensors; mobile health applications; and IoT-enabled medical devices that collect, process, and transmit data to support proactive care and personalized treatments. The benefits and limitations of PHR systems are also discussed in detail, with a focus on interoperability, adoption drivers, and the role of advanced technologies in supporting the development of secure and scalable health information systems for a smarter world. Full article

In the evolving landscape of Internet of Things (IoT) applications, human activity recognition plays an important role in domains such as health monitoring, elderly care, sports training, and smart environments. However, current approaches face significant challenges: sensor data are often noisy and variable, leading to difficulties in reliable feature extraction and accurate activity identification; furthermore, ensuring data integrity and user privacy remains an ongoing concern in real-world deployments. To address these challenges, we propose a novel framework that synergizes advanced statistical signal processing with state-of-the-art machine learning and deep learning models. Our approach begins with a rigorous preprocessing pipeline—encompassing filtering and normalization—to enhance data quality, followed by the application of probability density functions and key statistical measures to capture intrinsic sensor characteristics. We then employ a hybrid modeling strategy combining traditional methods (SVM, Decision Tree, and Random Forest) and deep learning architectures (CNN, LSTM, Transformer, Swin Transformer, and TransUNet) to achieve high recognition accuracy and robustness. Additionally, our framework incorporates IoT security measures designed to safeguard data integrity and privacy, marking a significant advancement over existing methods in both efficiency and effectiveness. Full article

The aging global population requires innovative remote monitoring systems to assist doctors and caregivers in assessing the health of elderly patients. Doctors often lack access to continuous behavioral data, making it difficult to detect deviations from normal patterns when elderly patients arrive for a consultation. Without historical insights into common behaviors and potential anomalies detected with unobtrusive techniques (e.g., non-wearable devices), timely and informed medical interventions become challenging. To address this, we propose an edge-based Internet of Things (IoT) framework that enables real-time monitoring and anomaly detection using non-wearable sensors to assist doctors and caregivers in assessing the health of elderly patients. By processing data locally, the system minimizes privacy concerns and ensures immediate data availability, allowing healthcare professionals to detect unusual behavioral patterns early. The system employs advanced machine learning (ML) models to identify deviations that may indicate potential health risks. A prototype of our system has been developed to test its feasibility and demonstrate, through the application of two of the most frequently used ML models, i.e., isolation forest and Long Short-Term Memory (LSTM) networks, that it can provide scalability, efficiency, and reliability in the context of elderly care. Further, the provided dashboard enables caregivers and healthcare professionals to access real-time alerts and longitudinal trends, facilitating proactive interventions. The proposed approach improves healthcare responsiveness by providing instant insights into patient behavior, facilitating more accurate diagnoses and interventions. This study lays the groundwork for future advancements in the field and offers valuable insights for the research community to harness the full potential of combining edge computing, artificial intelligence (AI), and the IoT in elderly care. Full article

Biosensors are innovative and cost-effective analytical devices that integrate biological recognition elements (bioreceptors) with transducers to detect specific substances (biomolecules), providing a high sensitivity and specificity for the rapid and accurate point-of-care (POC) quantitative detection of selected biomolecules. In the meat production chain, their application has gained attention due to the increasing demand for enhanced food safety, quality assurance, food fraud detection, and regulatory compliance. Biosensors can detect foodborne pathogens (Salmonella, Campylobacter, Shiga-toxin-producing E. coli/STEC, L. monocytogenes, etc.), spoilage bacteria and indicators, contaminants (pesticides, dioxins, and mycotoxins), antibiotics, antimicrobial resistance genes, hormones (growth promoters and stress hormones), and metabolites (acute-phase proteins as inflammation markers) at different modules along the meat chain, from livestock farming to packaging in the farm-to-fork (F2F) continuum. By providing real-time data from the meat chain, biosensors enable early interventions, reducing the health risks (foodborne outbreaks) associated with contaminated meat/meat products or sub-standard meat products. Recent advancements in micro- and nanotechnology, microfluidics, and wireless communication have further enhanced the sensitivity, specificity, portability, and automation of biosensors, making them suitable for on-site field applications. The integration of biosensors with blockchain and Internet of Things (IoT) systems allows for acquired data integration and management, while their integration with artificial intelligence (AI) and machine learning (ML) enables rapid data processing, analytics, and input for risk assessment by competent authorities. This promotes transparency and traceability within the meat chain, fostering consumer trust and industry accountability. Despite biosensors’ promising potential, challenges such as scalability, reliability associated with the complexity of meat matrices, and regulatory approval are still the main challenges. This review provides a broad overview of the most relevant aspects of current state-of-the-art biosensors’ development, challenges, and opportunities for prospective applications and their regular use in meat safety and quality monitoring, clarifying further perspectives. Full article

Manual intravenous (IV) monitoring delays, put patients at risk, as the reaction time of nursing staff can be critical to the patient’s health. The widespread use of LoRa networks today is a reality. The deployment of devices and applications based on LoRa networks in healthcare environments, such as hospital facilities, is of great interest and can offer both time savings for medical and nursing staff and improvements in medical care. In this work an integrated low-cost, real-time monitoring system for fluid infusion based on a LoRa network is presented. The measured (monitoring) data are the weight of the fluid infusion flask and the number of fluid drops. The design of the system and the affordability of the materials (low-cost devices) give the possibility for immediate application in healthcare environments. As the system consists of low-cost sensors, and given that it is intended for health purposes, extensive research has been carried out on the evaluation and reliability of the measurements. The proposed system is intended for medical care; in this sense it should have the lowest possible measurement error. The evaluation of the system has revealed a polynomial equation as a corrective factor for weight and shows an improvement of the error from 2% of the raw measurements to 0.6% of the corrected measurements, while regarding the calculation of the weight from the measurement of the droplets, it shows an error of 1.6%. The proposed system contributes directly to both the valuable time of the medical staff and the improvement of patient care. The evolution of technology should be applied to the health sector and low-cost and internet of things (IoT) devices can be applied to healthcare after thorough evaluation and calibration procedures. Full article

This paper presents a systematic review to explore how the Internet of Things (IoT) is empowering the Internet of Pets (IoP) to enhance the quality of life for companion animals. Thirty-six relevant papers published between 2010 and 2024 were retrieved and analyzed following both the PRISMA and the Kitchenham and Charters guidelines for conducting literature reviews. The findings demonstrate that the IoP is transforming pet care by offering innovative solutions for monitoring, feeding, and animal welfare. Asian countries are leading the development of these technologies, with a surge in research activity in recent years (2020–2024). While remote feeding prototypes currently dominate the field (79%), the IoP is anticipated to expand into other areas. Monitoring health (25%), surveillance and monitoring activities (49%), and providing comfort (17%) for pets are the primary research interests. The IoT holds immense potential to improve pet care. Research in this area is expected to continue growing, driving innovation and the creation of new IoP solutions utilizing artificial intelligence to achieve smart and predictive devices. In the future, the development of multifunctional devices that combine various capabilities in a single unit will become commonplace in a society where it is trending for young people to adopt pets instead of having children. Full article

Cardiovascular diseases pose a significant threat to global health, and cardiac rehabilitation (CR) has become a critical component of patient care. Heart Rate Variability Biofeedback (HRVB) is a non-invasive approach that helps modulate the Autonomic Nervous System (ANS) through Resonance Frequency (RF) breathing, supporting CR for cardiovascular patients. However, traditional HRVB techniques rely heavily on manual RF selection and face-to-face guidance, limiting their widespread application, particularly in home-based CR. To address these limitations, we propose a remote human-computer collaborative HRVB system, “FreeResp”, which features autonomous RF adjustment through a simplified cognitive computational model, eliminating the reliance on therapists. Furthermore, the system integrates wearable technology and the Internet of Things (IoT) to support remote monitoring and personalized interventions. By incorporating tactile guidance technology with an airbag, the system assists patients in performing diaphragmatic breathing more effectively. FreeResp demonstrated high consistency with conventional HRVB methods in determining RF values (22/24) from 24 valid training samples. Moreover, a one-month home-based RF breathing training using FreeResp showed significant improvements in Heart Rate Variability (HRV) (p < 0.05). These findings suggest that FreeResp is a promising solution for home-based CR, offering timely and precise interventions and providing a new approach to long-term cardiovascular health management. Full article

Low-power gas sensors that can be used in IoT (Internet of Things) systems, consumer devices, and point-of-care devices will enable new applications in environmental monitoring and health protection. We fabricated a monolithic chemiresistive gas sensor by integrating a micro-lightplate with a 2D sensing material composed of single-layer graphene and monolayer-thick TiO₂. Applying ultraviolet (380 nm) light with quantum energy above the TiO₂ bandgap effectively enhanced the sensor responses. Low (<1 μW optical) power operation of the device was demonstrated by measuring NO₂ gas at low concentrations, which is typical in air quality monitoring, with an estimated limit of detection < 0.1 ppb. The gas response amplitudes remained nearly constant over the studied light intensity range (1–150 mW/cm²) owing to the balance between the photoinduced adsorption and desorption processes of the gas molecules. The rates of both processes followed an approximately square-root dependence on light intensity, plausibly because the electron–hole recombination of photoinduced charge carriers is the primary rate-limiting factor. These results pave the way for integrating 2D materials with micro-LED arrays as a feasible path to advanced electronic noses. Full article

One of the health challenges in the 21st century is to rethink approaches to non-communicable disease prevention. A solution is a smart city that implements technology to make health smarter, enables healthcare access, and contributes to all residents’ overall well-being. Thus, this paper proposes an architecture to deliver smart health. The architecture is anchored in the Internet of Things and edge computing, and it is driven by artificial intelligence to establish three foundational layers in smart care. Experimental results in a case study on glucose prediction noninvasively show that the architecture senses and acquires data that capture relevant characteristics. The study also establishes a baseline of twelve regression algorithms to assess the non-invasive glucose prediction performance regarding the mean squared error, root mean squared error, and r-squared score, and the catboost regressor outperforms the other models with 218.91 and 782.30 in MSE, 14.80 and 27.97 in RMSE, and 0.81 and 0.31 in $R^2$, respectively, on training and test sets. Future research works involve extending the performance of the algorithms with new datasets, creating and optimizing embedded AI models, deploying edge-IoT with embedded AI for wearable devices, implementing an autonomous AI cloud engine, and implementing federated learning to deliver scalable smart health in a smart city context. Full article

Digital society strategies in healthcare include the rapid development of digital twins (DTs) for patients and human organs in medical research and the use of artificial intelligence (AI) in clinical practice to develop effective treatments in a cheaper, quicker, and more effective manner. This is facilitated by the availability of large historical datasets from previous clinical trials and other real-world data sources (e.g., patient biometrics collected from wearable devices). DTs can use AI models to create predictions of future health outcomes for an individual patient in the form of an AI-generated digital twin to support the rapid assessment of in silico intervention strategies. DTs are gaining the ability to update in real time in relation to their corresponding physical patients and connect to multiple diagnostic and therapeutic devices. Support for this form of personalized medicine is necessary due to the complex technological challenges, regulatory perspectives, and complex issues of security and trust in this approach. The challenge is also to combine different datasets and omics to quickly interpret large datasets in order to generate health and disease indicators and to improve sampling and longitudinal analysis. It is possible to improve patient care through various means (simulated clinical trials, disease prediction, the remote monitoring of apatient’s condition, treatment progress, and adjustments to the treatment plan), especially in the environments of smart cities and smart territories and through the wider use of 6G, blockchain (and soon maybe quantum cryptography), and the Internet of Things (IoT), as well as through medical technologies, such as multiomics. From a practical point of view, this requires not only efficient validation but also seamless integration with the existing healthcare infrastructure. Full article

Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder impacting women’s health and quality of life. This scoping review explores the use of the Internet of Things (IoT) in PCOS management. Results were grouped into six domains of the IoT: mobile apps, social media, wearables, machine learning, websites, and phone-based. A further domain was created to capture participants’ perspectives on using the IoT in PCOS management. Mobile apps appear to be useful for menstrual cycle tracking, symptom recording, and education. Despite concerns regarding the quality and reliability of social media content, these platforms may play an important role in disseminating PCOS-related information. Wearables facilitate detailed symptom monitoring and improve communication with healthcare providers. Machine learning algorithms show promising results in PCOS diagnosis accuracy, risk prediction, and app development. Although abundant, PCOS-related content on websites may lack quality and cultural considerations. While patients express concerns about online misinformation, they consider online forums valuable for peer connection. Using text messages and phone calls to provide feedback and support to PCOS patients may help them improve lifestyle behaviors and self-management skills. Advancing evidence-based, culturally sensitive, and accessible IoT solutions can enhance their potential to transform PCOS care, address misinformation, and empower women to better manage their symptoms. Full article

The spectrum of Internet of Things (IoT) applications is vast. It serves in various domains such as smart homes, intelligent buildings, health care, emergency response, and many more, reflecting the exponential market penetration of the IoT. Various security threats have been made to modern-day systems. Cyberattacks have seen a marked surge in frequency, particularly in recent times. The growing concern centers around the notable rise in cloning attacks, persisting as a significant and looming threat. In our work, an in-depth survey on the IoT that employs physically unclonable functions (PUFs) was conducted. The first contribution analyzes PUF-based authentication, communication protocols, and applications. It also tackles the eleven challenges faced by the research community, proposes solutions to these challenges, and highlights cloning attacks. The second contribution suggests the implementation of a framework model known as PUF3S-ML, specifically crafted for PUF authentication in the Internet of Things (IoT), incorporating innovative lightweight encryption techniques. It focuses on safeguarding smart IoT networks from cloning attacks. The key innovation framework comprises three stages of PUF authentication with IoT devices and an intelligent cybersecurity monitoring unit for IoT networks. In the methodology of this study, a survey relevant to the concerns was conducted. More data were provided previously regarding architecture, enabling technologies, and IoT challenges. After conducting an extensive survey of 125 papers, our analysis revealed 23 papers directly relevant to our domain. Furthermore, within this subset, we identified 11 studies specifically addressing the intersection of communication protocols with PUFs. These findings highlight the targeted relevance and potential contributions of the existing literature to our research focus. Full article