Leigh Fleming

Background: Smart home technologies integrated with technology-enhanced health care (TEH) systems are transforming residential care by supporting independent living, continuous health monitoring, and remote clinical interventions. The Internet of Medical Things, wearable biosensors, and AI-driven analytics enable proactive health care delivery and personalized interventions, particularly for older adults and individuals with chronic conditions. Objective: This review synthesizes literature on TEH integration within smart homes, examining global deployment patterns, technological maturity, biomedical sensor integration, machine learning applications, and health outcomes. It also identifies implementation challenges and disparities to improve digital health care strategies. Methods: A scoping review was conducted across PubMed, Scopus, Web of Science, ScienceDirect, and IEEE Xplore for peer-reviewed studies published between January 2005 and February 2025. Following screening of 6276 records, 169 studies were included, covering experimental, qualitative, and system design methodologies. Data were extracted on geographic deployment, sensor types, TEH architectures, machine learning algorithms, clinical outcomes, and adoption barriers. Results: TEH adoption is concentrated in Europe, East and Southeast Asia, and higher-income countries, with potential emerging initiatives in West Asia in lower-income regions. Smart home maturity ranges from foundational systems with basic automation to connected ecosystems with centralized Internet of Things coordination, and intelligent systems with data-driven adaptive monitoring. The literature was synthesized across thematic domains, including sensor technologies, smart home infrastructure, predictive analytics, telehealth integration, and ethical and regulatory considerations, and interpreted through a 3-level maturity taxonomy of foundational, connected, and intelligent smart home systems. Integration of biomedical sensors can enable continuous monitoring of cardiovascular, respiratory, neurological, metabolic, and mobility parameters, while machine learning algorithms can support early disease detection, predictive health analytics, activity recognition, and personalized interventions. Evidence from current literature indicates remote monitoring improves early detection of health issues, chronic disease management, medication adherence, and psychological well-being. Several studies reported that remote monitoring systems improved early detection of health deterioration, chronic disease management, medication adherence, and patient well-being. Adoption barriers include interoperability challenges, data privacy, digital literacy gaps, social and economic disparities, and long-term sustainability concerns. Conclusions: Unlike previous work, this review emphasizes practical adoption barriers, interoperability challenges, and equity considerations alongside sensor performance and system integration. The key significance in this scoping review is how it highlights technological trends and implementation patterns while mapping global deployments of TEH smart homes through encompassing wearable and environmental systems. It demonstrates realistic integration scenarios and how it enhances independent living, preventive care, and personalized health management while reducing hospitalizations and health care costs. It demonstrates how widespread implementation requires standardized evaluation frameworks, robust interoperability, adaptable design, equitable access, and clinically friendly integration. By addressing technical, social, and regulatory challenges, smart home systems can achieve scalable, sustainable, and effective digital health care delivery.

This paper investigates existing process models for the development of Data-Driven Industrial Services (DDIS). These process models are increasingly important as the role of data in industrial asset management grows. This paper conducts a Systematic Literature Review (SLR) across six databases to address three research questions: Firstly, which existing process models are available for DDIS development? Secondly, do existing process models for DDIS development provide methods and roles for the support of their process models? Lastly, what further research can be derived from the current literature on process models for DDIS development? The findings reveal 17 process models, with eight of them based on the well-established CRoss Industry Standard Process for Data Mining (CRISP-DM). There is variability in how the process models are supported by methods and roles. In addition, the paper establishes a research agenda that addresses research gaps. This paper contributes to the broader understanding of service digitalization in the form of an overview of existing process models for DDIS development and a research agenda that leads towards enabling industrial companies to choose and apply the appropriate process models for DDIS.

BACKGROUND Smart home technologies integrated with Technology-Enhanced Healthcare (TEH) systems are transforming residential care by supporting independent living, continuous health monitoring, and remote clinical interventions. The Internet of Medical Things (IoMT), wearable biosensors, and AI-driven analytics enable proactive healthcare delivery and personalized interventions, particularly for older adults and individuals with chronic conditions. OBJECTIVE This review synthesizes current literature on TEH integration within smart homes, examining global deployment patterns, technological maturity, biomedical sensor integration, machine learning applications and health outcomes. It also identifies implementation challenges and disparities to improve digital healthcare strategies. METHODS A systematic search was conducted across PubMed, Scopus, Web of Science, ScienceDirect, Google Scholar, and IEEE Xplore for peer-reviewed studies published between January 2005 and February 2025. Following screening of 6,047 records, 119 studies were included, covering experimental, qualitative, and system design methodologies. Data were extracted on geographic deployment, sensor types, TEH architectures, machine learning algorithms, clinical outcomes, and adoption barriers. RESULTS TEH adoption is concentrated in Europe, East and Southeast Asia, and higher income countries, with potential emerging initiatives in West Asia in lower income regions. Smart home maturity ranges from foundational systems with basic automation to connected ecosystems with centralized IoT coordination and intelligent systems with data driven adaptive monitoring). Integration of biomedical sensors—wearable ECG, SpO₂, EEG, glucose monitors, smart rings, environmental sensors, and radar-based devices—enables continuous monitoring of cardiovascular, respiratory, neurological, metabolic, and mobility parameters. Machine learning and AI algorithms can support early disease detection, predictive health analytics, activity recognition, and personalized interventions. Evidence indicates remote monitoring improves early detection of health issues, chronic disease management, medication adherence, and psychological wellbeing. A representative case study in Australia demonstrated that remote TEH monitoring of 100 patients over 276 days led to a 46.3% reduction in predicted healthcare expenditure, 53.2% reduction in predicted hospital admissions, and 67.9% reduction in length of stay compared with 137 matched controls. Adoption barriers include interoperability challenges, data privacy, digital literacy gaps, social and economic disparities, and long-term sustainability concerns. CONCLUSIONS TEH integration in smart homes enhances independent living, preventive care, and personalized health management while reducing hospitalizations and healthcare costs. Widespread implementation requires standardized evaluation frameworks, robust interoperability, adaptable design, equitable access, and clinical friendly. By addressing technical, social, and regulatory challenges, TEH-smart home systems can achieve scalable, sustainable, and effective digital healthcare delivery.

Thin functional barrier layers of aluminum oxide (Al₂O₃) that are used particularly in photovoltaic (PV) modules to prevent the possibility of water vapor ingress should be applied over the entire PV surface without any defects. However, for barrier layer thicknesses within the sub-micrometer range (up to 50 nm) produced through the atomic layer deposition (ALD) method, it is common for defects to occur during the production process. To avoid defective barriers from being incorporated in the final PV unit, defects need to be detected during the barrier production process.In this paper, the implementation of in process inspection system capable of detecting surface defects such as pinholes, scratches, or particles down to a lateral size of 3 µm and a vertical resolution of 10 nm over a 500 mm barrier width is presented. The system has a built-in environmental vibration compensation capability, and can monitor ALD-coated films manufactured using roll-to-roll (R2R) techniques. Ultimately, with the aid of this in process measurement system, it should be possible to monitor the coating surface process of large-area substrates, and if necessary, carry out remedial work on the process parameters.