Real-time Data Collection Research Articles

Postharvest Management Techniques for Improved Shelf Life of Horticultural Crops: A Review

Postharvest management in horticulture is important for maintaining the quality, safety, and shelf life of produce, significantly impacting global food security and economic stability. This review explores critical advancements in postharvest practices, focusing on quality control, safety standards, and emerging technologies. Postharvest quality assessment involves the evaluation of physical, chemical, and sensory attributes, supported by microbiological testing to ensure food safety. Global standards like HACCP, GlobalG.A.P., and ISO 22000 provide frameworks for ensuring compliance and market access. Technological innovations, including near-infrared spectroscopy, hyperspectral imaging, and smart sensors, enable precise, real-time monitoring of produce quality, enhancing operational efficiency. Emerging technologies are revolutionizing the field; nanotechnology offers advanced packaging solutions that improve shelf life and food safety, while drones and AI enhance postharvest monitoring and logistics through real-time data collection and predictive analytics. Blockchain technology introduces unprecedented levels of traceability and transparency, ensuring accountability and rapid response to food safety issues. The integration of these technologies not only improves the efficiency and sustainability of postharvest systems but also aligns with consumer demands for high-quality, safe, and sustainably sourced produce. This holistic approach reduces post- harvest losses, enhances market competitiveness, and contributes to global efforts in ensuring food security. The synergy of traditional quality control methods with cutting-edge technologies paves the way for a resilient and adaptive post- harvest management system that meets the evolving challenges of the agricultural sector.

Classification of Ship Type from Combination of HMM–DNN–CNN Models Based on Ship Trajectory Features

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system.

Legal Study on the Oversight of Overloaded Motor Vehicles: Efforts to Protect Road Safety and Infrastructure

The oversight of motor vehicles exceeding weight limits is critical in safeguarding road safety and protecting infrastructure from premature damage. Overloaded vehicles are a common issue, contributing significantly to accidents and accelerated road deterioration, which incurs high repair costs and disrupts public transport and logistics. This study examines the legal frameworks and regulatory practices aimed at controlling vehicle overloading in Indonesia. Utilizing a normative juridical approach, the research reviews pertinent laws, regulations, and real-world cases involving load limit violations to gain insights into the effectiveness of existing policies. Findings reveal that while stringent load regulations are in place, enforcement remains inconsistent due to various limitations, including outdated monitoring technology, insufficient human resources, and challenges in coordinating between regulatory agencies. The research identifies critical obstacles in the current oversight mechanisms, such as a lack of real-time data collection and limited roadside weighing facilities, which hinder comprehensive monitoring. Furthermore, penalties for overloading are often inadequate to deter repeat violations. Based on these findings, this study recommends a multi-faceted approach to strengthen vehicle load regulation enforcement. Suggested improvements include upgrading monitoring technology, such as installing automatic weigh-in-motion systems, increasing the frequency and coverage of roadside inspections, and implementing more severe penalties to enhance deterrence. Additionally, the study emphasizes the need for inter-agency coordination and public awareness campaigns to foster compliance among vehicle operators. Through these initiatives, Indonesia can better protect its roadways, ensuring both road safety for all users and the long-term sustainability of its infrastructure.

Long-term load forecasting for smart grid

Abstract The load forecasting problem is a complicated non-linear problem connected with the weather, economy, and other complex factors. For electrical power systems, long-term load forecasting provides valuable information for scheduling maintenance, evaluating adequacy, and managing limited energy supplies. A future generating, transmission, and distribution facility's development and planning process begins with long-term demand forecasting. The development of advanced metering infrastructure (AMI) has greatly expanded the amount of real-time data collection on large-scale electricity consumption. The load forecasting techniques have changed significantly as a result of the real-time utilization of this vast amount of smart meter data. This study suggests numerous approaches for long-term load forecasting using smart-metered data from an actual distribution system on the NIT Patna campus. Data pre-processing is the process of converting unprocessed data into a suitable format by eliminating possible errors caused by lost or interrupted communications, the presence of noise or outliers, duplicate or incorrect data, etc. The load forecasting model is trained using historical load data and significant climatic variables discovered through correlation analysis. With a minimum MAPE and RMSE for every testing scenario, the proposed artificial neural network model yields the greatest forecasting performance for the used system data. The efficacy of the proposed technique has been through a comparison of the acquired results with various alternative load forecasting methods.

Stabilizing food supply chains with Blockchain technology during periods of economic inflation

This study explores the potential of blockchain technology to stabilize food supply chains during periods of economic inflation. Economic inflation poses significant challenges to food security, as rising prices can disrupt supply chain transparency, efficiency, and trust among stakeholders. This research develops a conceptual model demonstrating how blockchain can enhance transparency and stability in food supply chains, ultimately improving resilience against inflationary pressures. By providing a decentralized, immutable ledger, blockchain technology allows for real-time tracking of food products from farm to table, enabling better inventory management and demand forecasting. The proposed model integrates key components, including smart contracts, which automate transactions and ensure compliance with quality and safety standards. This feature not only reduces delays but also fosters trust among supply chain participants, such as farmers, distributors, retailers, and consumers. Additionally, the model emphasizes the role of data analytics in harnessing blockchain data to provide insights into pricing trends and inventory levels, facilitating proactive decision-making in response to inflation. Future research directions are identified, focusing on innovations within blockchain technology that could further mitigate the effects of inflation on food supply chains. Potential areas of exploration include integrating artificial intelligence (AI) for predictive analytics, utilizing Internet of Things (IoT) devices for real-time data collection, and developing decentralized finance (DeFi) solutions to enhance liquidity and investment in the agricultural sector. This study contributes to the growing body of literature on blockchain applications in supply chain management by highlighting its potential as a tool for enhancing food security in inflationary environments. By ensuring transparency, improving stakeholder collaboration, and providing actionable insights, blockchain technology can play a pivotal role in stabilizing food supply chains amidst economic fluctuations. Ultimately, this research advocates for the adoption of blockchain solutions as part of comprehensive strategies to safeguard food systems from the adverse impacts of inflation. Keywords: Blockchain Technology, Food Supply Chains, Economic Inflation, Transparency, Stability, Smart Contracts, Predictive Analytics, Data Analytics, Food Security, Decentralized Finance.

GTBTL-IoT: An Approach of Curtailing Task Offloading Time for Improved Responsiveness in IoT-MEC Model

INTRODUCTION: The Internet of Things (IoT) has transformed daily life by interconnecting digital devices via integrated sensors, software, and connectivity. Although IoT devices excel at real-time data collection and decision-making, their performance on complex tasks is hindered by limited power, resources, and time. To address this, IoT is often combined with cloud computing (CC) to meet time-sensitive demands. However, the distance between IoT devices and cloud servers can result in latency issues. OBJECTIVES: To mitigate latency challenges, Mobile Edge Computing (MEC) is integrated with IoT. MEC offers cloud-like services through servers located near network edges and IoT devices, enhancing device responsiveness by reducing transmission and processing latency. This study aims to develop a solution to optimize task offloading in IoT-MEC environments, addressing challenges like latency, uneven workloads, and network congestion. METHODS: This research introduces the Game Theory-Based Task Latency (GTBTL-IoT) algorithm, a two-way task offloading approach employing Game Matching Theory and Data Partitioning Theory. Initially, the algorithm matches IoT devices with the nearest MEC server using game-matching theory. Subsequently, it splits the entire task into two halves and allocates them to both local and MEC servers for parallel computation, optimizing resource usage and workload balance. RESULTS: GTBTL-IoT outperforms existing algorithms, such as the Delay-Aware Online Workload Allocation (DAOWA) Algorithm, Fuzzy Algorithm (FA), and Dynamic Task Scheduling (DTS), by an average of 143.75 ms with a 5.5 s system deadline. Additionally, it significantly reduces task transmission, computation latency, and overall job offloading time by 59%. Evaluated in an ENIGMA-based simulation environment, GTBTL-IoT demonstrates its ability to compute requests in real-time with optimal resource usage, ensuring efficient and balanced task execution in the IoT-MEC paradigm. CONCLUSION: The Game Theory-Based Task Latency (GTBTL-IoT) algorithm presents a novel approach to optimize task offloading in IoT-MEC environments. By leveraging Game Matching Theory and Data Partitioning Theory, GTBTL-IoT effectively reduces latency, balances workloads, and optimizes resource usage. The algorithm's superior performance compared to existing methods underscores its potential to enhance the responsiveness and efficiency of IoT devices in real-world applications, ensuring seamless task execution in IoT-MEC systems.

Early Viral Dynamics Predict HIV Post-Treatment Control After Analytic Treatment Interruption.

A key research priority for developing an HIV cure strategy is to define the viral dynamics and biomarkers associated with sustained post-treatment control. The ability to predict the likelihood of sustained post-treatment control or non-control could minimize the time off antiretroviral therapy (ART) for those destined to not control and anticipate longer periods off ART for those destined to control. Mathematical modeling and machine learning were used to characterize virologic predictors of long-term virologic control using viral kinetics data from several studies in which participants interrupted ART. Predictors of post-ART outcomes were characterized using data accumulated from the time of treatment interruption, replicating real-time data collection in a clinical study, and classifying outcomes as either post-treatment control (plasma viremia ≤400 copies/mL at 2 of 3 time points for ≥24 weeks) or non-control. Potential predictors of virologic control were the time to rebound, the rate of initial rebound, and the peak plasma viremia. We found that people destined to be non-controllers could be identified within 3 weeks of rebound (prediction scores: accuracy, 80%; sensitivity, 82%; specificity, 71%). Given the widespread use of analytic treatment interruption in cure-related trials, these predictors may be useful to increase the safety of analytic treatment interruption through the early identification of people who are unlikely to become post-treatment controllers.

Application of the integrated well-surface facility production system for selecting the optimal operating mode of equipment

This work is dedicated to the analysis and development of an integrated production system that combines wells and surface facilities to select the optimal operating mode for equipment. The main focus is on studying data analysis methods, as well as collecting and processing information, which ensures high accuracy in process control and optimal use of technological capabilities. The integration of data from various levels of the production chain, from the well to the surface facilities, opens up new opportunities for optimizing equipment performance and improving resource management quality. Integration of wells and surface facilities – The effective integration of wells and surface facilities is crucial for optimizing production processes, minimizing operational costs, and reducing environmental impact. Integrated management systems allow the automation of many processes, providing continuous monitoring of operating parameters, automatic adjustment of settings, and supplying data for quick decision-making. This includes real-time data collection, analysis, and the use of the obtained information to select optimal operating modes, which helps improve both overall efficiency and safety in production processes. In the modern oil and gas industry, the efficient use of equipment at all stages of hydrocarbon production is of particular importance. Optimizing the operation of wells and surface facilities is a key aspect for increasing economic efficiency and reducing environmental impact. In this regard, the development and application of integrated systems that enable real-time control and adaptation of equipment operating modes has become a relevant and significant task.

Language change across the lifespan: The changing status of a local variant

Abstract Three major studies on pronunciation were conducted in Iceland over a period of 70 years, including collection of real-time data. One subgroup of participants was interviewed as children in the 1940s, again as adults in the 1980s, and once again in the 2010s. Another subgroup was interviewed twice. By reanalysing raw data on participants in the first study the author was able to compare them with subsequent studies and trace individual developments, and thus examine lifespan changes over a long period. This article focuses on 62 participants from the two subgroups (47 people born in 1927–1932, and 15 born in 1963–1970), all from north-eastern Iceland, and a regional variant called ‘hard speech’ (harðmæli), i.e., the use of aspirated rather than unaspirated intervocalic plosives, comparing it with another north-eastern variant, so-called ‘voiced pronunciation’ (raddaður framburður), i.e., the use of voiced rather than voiceless sonorants before /p,t,k/. This reanalysis of the data of these seminal studies not only corroborates earlier findings that pronunciation can and does change among adult individuals, it also suggests (a) that such lifespan changes are more likely to happen in the first 2–3 decades of adulthood; and (b) that while ‘hard speech’ is fading as a local variant it shows signs of strengthening among individual speakers over their lifetime. This development takes place concurrently with attitudinal changes towards ‘hard speech’, which has come to be perceived as ‘clear pronunciation’.

Biomedical Engineering and Measurement Technology

Abstract The intersection of biomedical engineering and measurement technology has transformed the medical landscape, offering innovative solutions for diagnostics, treatment, and patient monitoring. Biomedical engineering encompasses a broad spectrum of applications, from molecular biology and tissue engineering to medical robotics and wearable technologies. Measurement technology, meanwhile, enables precise and real-time data collection through advanced sensing techniques, ensuring that these biomedical systems operate at optimal efficiency. In recent years, significant advancements in medical sensors, imaging technologies, and artificial intelligence have paved the way for improved accuracy and performance in medical diagnostics and treatment. This special issue focuses on the latest developments in these fields, showcasing groundbreaking research that enhances our understanding of physiological phenomena and revolutionizes medical measurement applications. Submissions for this topic in Measurement Science and Technology are open for submission from 03 November 2022 to 31 July 2023 and contains 21 outstanding papers in above research fields.

Assessing Mental Health During an Extreme Weather Event in Southern Brazil.

This study aimed to evaluate mental health factors in individuals affected by the floods in Rio Grande do Sul from May 10 to June 6, 2024. A real-time survey was conducted with 1,552 participants. The Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Acute Stress Disorder (ASD) symptom checklist were used to assess mental health outcomes. Sociodemographic factors, trauma exposure, rescue participation, and psychological support were evaluated. Statistical analyses included t-tests, ANOVA, linear regression, and MANCOVA. High rates of depressive and anxious symptoms were found, with over half of the individuals with moderate to severe symptoms reporting no prior history of these conditions. Participants directly affected by the floods, and those with close ones affected, had higher PHQ-9, GAD-7, and ASD scores compared to the unaffected group. Moreover, younger age, female sex, lower family income, participation in rescues, and unmet psychological support needs were correlated with worse mental health outcomes. Individuals who participated in rescues had significantly higher rates of suicide ideation. Notably, psychological support had the largest effect size in mitigating mental health symptoms, regardless of trauma exposure status, when controlled for age, sex, and income according to MANCOVA. The findings highlight the critical role of psychological support in disaster response and the importance of community resilience. Real-time data collection during crises can inform targeted interventions, emphasizing the need for robust mental health services and community support networks. These efforts are essential to reduce long-term psychological morbidity and foster recovery in vulnerable populations.

PicPatrolling: Smart Surveillance with ESP32CAM

Robotics is an exciting and developing branch of science. Robots are now common in society because of their high efficiency and durability, and they have many users. Security and surveillance robots, especially those with wheeled movements and agility, are important for multiplexes in security and surveillance domains, including public security, military, facilities, and home security. These robots carry built-in high-end sensors, cameras, and communication equipment that simplify real-time data collection in indoor and outdoor environments. "PicPatrolling: Smart Surveillance with ESP32CAM” is a technique that attempts to improve the current concept of security and surveillance. The proposed system incorporates ESP32CAM for image capture and transmission without delay and GPS for location tracking. The system includes sending photo e-mail alerts, making immediate reports, and guaranteeing adequate area coverage. The robot can move during operation in a complex environment using computer vision, machine learning, and sensor fusion to recognize objects, faces, and changes and monitor important regions.. Application areas for surveillance robots include commercial and populated regions, transport points and architectural structures, other soft regions that people cannot access, and other dangerous areas that humans cannot monitor for various reasons.

Ensuring safety and monitoring adverse events in clinical trials: Challenges and innovations

Safety and adverse events (AEs) monitoring in clinical trials are critical to ensuring participant safety and the successful development of new therapies. Effective monitoring not only protects patients but also helps identify any risks associated with the investigational product. This paper elaborates on the methodologies used to detect, categorize, and report adverse events during clinical trials, and how these processes are governed by stringent regulatory frameworks. Additionally, the discussion highlights the role of pharmacovigilance systems, the ethical obligations researchers face in AE reporting, and the technological innovations that are transforming safety monitoring. Challenges, including under-reporting, data variability, and interpretation difficulties, are examined in depth. Ultimately, advancements in real-time data collection, adaptive trial designs, and artificial intelligence (AI) are promising to improve the accuracy and timeliness of adverse event monitoring, shaping the future of clinical research and patient safety.

Usage of Information technology for the success of managing various stages of the COVID-19 pandemic

This study explores the crucial role of Information and Communication Technology (ICT) in managing the COVID-19 pandemic, focusing on its impact on surveillance, contact tracing, patient monitoring, telemedicine, artificial intelligence (AI), vaccination logistics, communication, and public education. ICT has facilitated the collection and analysis of real-time data, enabling authorities to make informed decisions about containment strategies. Digital contact tracing systems have been instrumental in limiting the virus's spread, while remote patient monitoring and telemedicine have allowed healthcare services to continue with minimal transmission risk. AI and machine learning have enhanced the speed and accuracy of COVID-19 diagnosis, and ICT has streamlined vaccine distribution and monitoring to ensure equitable access. Additionally, online platforms have played a key role in public education, fostering collaboration among researchers and promoting interdisciplinary research efforts. The study emphasizes that the continued integration of ICT into healthcare systems is critical for addressing future global health crises. However, policymakers must invest in ICT infrastructure, bridge the digital divide, and ensure that marginalized populations have equitable access to these technologies. The findings highlight the importance of ongoing ICT development to improve healthcare outcomes in future pandemics and suggest that collaboration across sectors is essential for maximizing the potential of these digital tools. Ultimately, ICT has been pivotal in the effective management of the COVID-19 pandemic and will be vital in future crisis preparedness.

The future of wearable health technology: From monitoring to preventive healthcare

Wearable health technology has undergone rapid development, evolving from simple fitness trackers to sophisticated medical devices that provide real-time monitoring and data collection. As these technologies become increasingly integrated into healthcare systems, their potential to shift from reactive treatment to proactive preventive care is gaining attention. This review paper explores the current state of wearable health devices, including the key technologies that enable monitoring and data analysis, and discusses the latest advancements such as AI integration, smart fabrics, and implantable wearables. The paper also delves into the significant role these technologies play in preventive healthcare by enabling continuous monitoring, early disease detection, and personalized health interventions. Furthermore, it addresses the challenges facing wearables, including issues of data accuracy, privacy, and user compliance. Finally, the paper explores future directions and ethical considerations, emphasizing the potential for wearables to reshape healthcare by promoting more accessible, equitable, and preventive healthcare solutions.

OPTIMISING EFFORT CAPACITY IN TRIATHLON

Background. My personal triathlon practice inspired the creation of a specialized training regimen to improve triathlon training in Romania. This program aims to enhance performance, well-being, and injury prevention for athletes. At the INTERDISCIPLINARY RESEARCH CENTRE (CCI) “Dr. Alexandru Partheniu” of UNEFS, we used AEROSCAN technology to assess exercise capacity, measuring heart and breathing rates with the Bruce Protocol for precise physiological monitoring. Objectives. This research demonstrates that tailored training cycles for swimming, cycling, and running can significantly enhance triathlon performance. The goal was to create a customizable, comprehensive training program to maximize the overall training process and provide a scientific foundation for triathlon training in Romania. This research aims to demonstrate that tailored training cycles can significantly enhance triathlon performance. Methods. We used observation, experimental, statistical-mathematical, and bibliographic study methods to interpret the experiment's outcomes. The bibliographic study provided the theoretical basis, while observation and experimental methods enabled real-time data collection. Statistical analysis was crucial for interpreting the data. Results. Using AEROSCAN and the Polar system, we evaluated exercise capacity and tracked heart rate. The Bruce protocol was applied for both initial and final tests. The data showed significant improvements in endurance, speed, and overall performance, supported by statistical analysis, after implementing the customized training program. Conclusion: Our research indicates that tailored athletic training regimens for each triathlon discipline can significantly enhance performance and promote athletes' health and longevity. The results highlight the importance of individualized training plans and suggest potential improvements in Romanian triathlon training practices. Individualized training programs lead to notable improvements in performance and athlete well-being.

A Three-Dimensional Surface-Adaptive Stretchable Sensor for Online Monitoring of Composite Materials Curing.

Recently, rigid sensors have been commonly applied to online monitoring of the core curing processes of composite materials to prevent both overcuring and under-curing. However, conventional rigid sensors are prone to causing cracks and bubbles in composite materials during the curing process, thereby affecting both the mechanical performance and the overall reliability of the materials. Herein, stretchable interdigital dielectric sensors with flexible substrates and electrodes are designed to conform to complex 3D surfaces, thus enabling embedded nondestructive monitoring of composite curing processes. The sensors obtained can endure 1000 cycles of bending from 0° to 180° and 1000 cycles of stretching at 30% strain while still conforming perfectly to complex 3D surfaces, thus overcoming the inability of traditional curing monitoring sensors to bend. Additionally, sensor integration with an electronic circuit enables real-time data collection and transmission, which makes the device more portable, compact, and lightweight. Moreover, after atmospheric exposure for 5 months, the unit sensitivity of the sensor decreased by only 0.1%, thus demonstrating its excellent reliability and stability. Furthermore, during curing monitoring of the complex three-dimensional surfaces of the Fendouzhe deep-sea submersible, the unit's sensitivity is close to that of conventional planar monitoring equipment, decreasing by only 0.4%. The proposed online nondestructive monitoring technology demonstrates high sensitivity, high monitoring accuracy, and high reliability during surface monitoring, thus enabling long-term curing monitoring under complex nonplanar conditions.

Secure low-cost photovoltaic monitoring system based on LoRaWAN network and artificial intelligence

The advanced study will focus on developing a secure and cost-effective photovoltaic monitoring system using Long Range Wide Area Network (LoRaWAN) and Artificial Intelligence technologies for highly risky missions. With the world shifting to using renewable energy, the development of secure and efficient cost-effective monitoring systems for photovoltaic installations becomes very important. However, most of the solutions in use for PV installation monitoring are characterized by high costs, hence their limited use, especially in developing regions. In this regard, the present work presents country-specific protocols designed for the country of Turkey. The proposed system in this study is expected to offer real-time monitoring and data collection at a very meager cost by utilizing inexpensive microcontrollers and sensors. The accuracy rate demonstrated by the research is highly above board: 97.65%. It costs $210, with a very low power consumption of 0.5W and a range of 10 km. The major features underpin real-time monitoring with wide area coverage for effectiveness and efficiency in PVs monitoring. Test results in many places around Turkey have turned out reliable and accurate to justify a possible reduction in operational costs without compromising the quality of data received. This design is reinforced by a detailed mathematical model that assures one of its thorough analysis in performance. There is custom-built firmware that enhances communication efficiency and ensures data integrity in implementation. Comparative studies carried out with existing technologies indicate that the proposed system is cost-effective and operationally sound. This work gives a scalable, low-cost solution that probably might raise the uptake of PV systems in economically constrained areas.

Leveraging digital platforms to improve Public Health in hospital smart buildings

Abstract The integration of digital platforms into hospital infrastructure represents a significant leap forward in public health management. This session explores how smart hospital buildings, equipped with advanced digital technologies, can enhance public health outcomes through more effective disease surveillance, improved environmental controls and enhanced patient care management. Smart hospital buildings utilize a range of new technologies such as IoT sensors, AI-driven analytics and big data solutions to monitor and optimize various aspects of hospital operations and care. These technologies enable real-time data collection on environmental conditions, patient health metrics and resource utilization, creating an ecosystem that not only supports the immediate health needs of patients but also contributes to broader public health goals. The session will address four key areas where digital platforms can make an impact: -Disease Surveillance and Management: Digital tools can track health data trends and patterns within hospital settings, enabling early detection of infectious outbreaks and more precise tracking of patient outcomes. -Environmental Monitoring: IoT sensors can continuously assess conditions such as air quality, temperature and humidity, which are critical for preventing hospital-acquired infections and ensuring a safe environment for patients and staff. -Resource Optimization: AI algorithms can analyse data to optimize resource allocation, ensuring that medical supplies and human resources are used efficiently, which is crucial in health emergencies. -Patient-Centred Care: Digital platforms can facilitate personalized patient care through real-time health monitoring and data analysis, ensuring timely medical interventions and better health management. This session will highlight case studies demonstrating successful implementation of digital technologies in smart hospital buildings and discuss the challenges and lessons learned from these initiatives.

Data ecosystem using smart home technology to support healthy aging

Abstract Background Smart home technology has been widely used to mitigate the challenges that population aging brings. However, the lack of a centralized data ecosystem has prevented these technologies from realizing their full potential. This project aimed to construct a data ecosystem compatible with smart home sensors to monitor healthcare for seniors, improving their quality of life. Methods The infrastructure was constructed using a user-centered design approach, allowing smart devices such as motion, temperature sensors to operate independently and communicate through a Wi-Fi mesh network. API technology provides a reliable channel for real-time data collection. To ensure data privacy and security, extracted data is hosted in a cloud environment that provides comprehensive data protection, and cloud computing is utilized for high-performance computation tasks. Analytical models such as Machine Learning can be implemented to recognize indoor activities, achieving the goal of remote monitoring. Results This data ecosystem advances in privacy and security, interoperability, accessibility, and data-sharing. Unified authentication mechanism ensures data is transmitted among heterogeneous devices with minimal vulnerability. Additionally, the common communication protocol provides the foundation of interoperability between devices. With unobtrusive devices, inhabitants are not burdened with operation, ensuring their accessibility and improving their user experience. Lastly, the data is exchanged in a standardized JSON data format, enabling efficient data-sharing board data integration. Conclusions This project developed a data ecosystem based on existing smart home environments. The ecosystem is compatible with smart devices that families already have, effectively reducing additional healthcare costs. Implications of this study include helping health policy and decision makers to adopt more smart home ecosystems to keep older people healthy at home. Key messages • Study constructed a data ecosystem compatible with smart home sensors, achieving remote monitoring of indoor activities and improving the quality of life for seniors. • The developed ecosystem offers promotion in privacy and security, interoperability, accessibility, and efficient data-sharing, reducing healthcare costs.