Digital Health and Technology-Assisted Aging in Place: Wearables, Smart Homes, and Remote Monitoring

Global demographic shifts are characterized by unprecedented growth in older populations, intensifying the demand for scalable, sustainable models of long-term care and community-based support. The overwhelming majority of older adults express a strong preference to “age in place”, maintaining independence, autonomy, and quality of life within their own homes and familiar communities for as long as possible. However, age-associated functional decline, frailty, multi-morbidity, cognitive impairment, and social isolation pose major barriers to safe, independent living.Rapid advancements in digital health, artificial intelligence (AI), ubiquitous computing, and consumer-grade sensing technologies offer transformative opportunities to redefine aging in place. From wearable biosensors capturing continuous real-world physiological and activity metrics to ambient smart-home environments, ambient assisted living (AAL) architectures, and remote patient monitoring (RPM) platforms, technology enables a paradigm shift from reactive acute care to proactive, continuous, and personalized health preservation. AI and machine learning increasingly underpin this shift, transforming high-frequency, multimodal sensor streams into clinically interpretable digital biomarkers, individualized risk predictions, and timely clinical alerts.Despite promising technological innovations, substantial challenges hinder the widespread, equitable translation of these tools into everyday geriatric care. Critical bottlenecks include clinical validation against established geriatric endpoints, usability and technology acceptance among older users, algorithm accuracy and generalizability in free-living environments, model transparency and explainability, interoperability across healthcare systems, data privacy and ethical oversight, and systemic disparities in digital literacy and broadband access.This Research Topic aims to assemble high-quality, interdisciplinary research at the intersection of gerontology, digital health, artificial intelligence, bioengineering, and health informatics. We invite original research, clinical trials, cohort studies, systematic reviews, methodological frameworks, and perspectives that investigate the design, implementation, clinical utility, and societal impact of digital and AI-enabled health technologies designed to support aging in place.We encourage submissions addressing, but not limited to, the following core themes:Wearable Sensors and Continuous Biomarker Tracking: Validation of wearable devices for continuous monitoring of gait, motor fluctuations, postural stability, fall risk, sleep architecture, circadian rhythmicity, and vital signs in community-dwelling older adults, including AI-derived digital biomarkers extracted from continuous sensor data.Ambient Sensing and Smart-Home Infrastructures: Non-obtrusive environmental sensors, passive infrared (PIR) motion tracking, acoustic sensing, and smart-home ecosystems for detecting behavioral deviations, daily routine changes, and early warning signs of functional or cognitive decline, including unsupervised and anomaly-detection approaches applied to longitudinal behavioral data.Fall Detection, Prevention, and Emergency Response: Machine learning algorithms, computer vision, and wearable- or ambient-based systems for real-time fall detection, near-fall classification, predictive fall-risk modeling, and personalized home hazard mitigation.AI-Enabled Cognitive Assessment and Remote Neurodegenerative Monitoring: Digital biomarkers derived from smartphone interactions, speech and language processing, natural language processing, and passive activity metrics for early screening and longitudinal tracking of mild cognitive impairment (MCI) and dementias.Remote Patient Monitoring (RPM) and Chronic Disease Management: Integration of remote physiological monitoring into primary care, outpatient geriatric services, and virtual wards for older adults managing cardiovascular disease, diabetes, COPD, or multi-morbidity, including AI-supported triage, deterioration prediction, and clinical decision support.Predictive Modeling, Foundation Models, and Data Science Methods in Geriatric Care: Development, validation, and benchmarking of machine learning, deep learning, multimodal data fusion, federated learning, and large language model approaches for aging-in-place applications, with attention to reproducibility, external validation, and real-world performance.User-Centered Design, Acceptability, and Digital Inclusivity: Human-computer interaction (HCI) methodologies, co-design with older adults and informal caregivers, conversational agents and AI-based companions, barriers to digital health adoption, technology literacy, and strategies to prevent digital exclusion.Ethical, Legal, and Data Governance Considerations: Data privacy, surveillance concerns, consent frameworks for cognitively impaired individuals, algorithmic bias and fairness in geriatric populations, explainability and trust in AI-driven decisions, and regulatory standards for digital health and AI-based medical applications.Translational Outcomes and Economic Impact: Clinical efficacy, health-related quality of life (HRQoL), caregiver burden reduction, healthcare utilization metrics, and cost-effectiveness analyses of technology- and AI-assisted aging-in-place interventions. Topic editors: Suguru Shimokihara, Kazuki Yokoyama, Ayuto Kodama Source: https://www.frontiersin.org/research-topics/86203/digital-health-and-technology-assisted-aging-in-place-wearables-smart-homes-and-remote-monitoring