Technology-Enhanced Healthcare in Smart Homes: Scoping Review of Sensor Technologies, Clinical Applications, Integration Challenges and Future Directions

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.