JMIR Publications has announced a new thematic section in its open-access journal JMIR Aging focused on the next generation of ambient intelligence and the emerging role of “invisible” monitoring in aging-in-place. The initiative, announced in Toronto on August 20, 2026, calls for original research, viewpoints, and literature reviews examining how unobtrusive sensors, artificial intelligence, and connected care systems could help older adults live safely and independently at home. The theme arrives as health systems worldwide confront rising demand for long-term care, caregiver support, and clinically meaningful tools that can monitor health continuously without requiring older people to wear devices, operate complicated interfaces, or repeatedly report symptoms.
At the center of the initiative is a shift away from conventional health monitoring, which often depends on cameras, wearable sensors, emergency buttons, or manual data entry. Instead, researchers are being encouraged to investigate ambient intelligence systems capable of observing patterns in a home environment with minimal or no visible interaction. These systems may use radar, radio-frequency signals, LiDAR, acoustic sensors, smart-home devices, or other forms of passive sensing to detect changes in movement and behavior. The goal is not simply to collect more data, but to convert everyday signals into clinically useful information while protecting privacy and preserving a resident’s sense of autonomy. For aging-in-place, this could mean identifying a dangerous fall, declining mobility, or a change in daily routines before a crisis occurs.
Radar-based monitoring is one of the technologies highlighted by the new section. Unlike conventional cameras, radar systems can detect motion, distance, and body position without producing recognizable visual images. Millimeter-wave radar, for example, emits high-frequency radio signals and analyzes how reflected waves change when a person moves. Algorithms can use these variations to estimate gait speed, posture, walking stability, or whether a person has fallen. More advanced systems may distinguish between ordinary movements and clinically concerning events by analyzing trajectories over time. Researchers will be expected to examine not only laboratory accuracy but also performance in real homes, where furniture, pets, multiple residents, changing lighting, and wireless interference can complicate the interpretation of sensor data.
LiDAR and radio-frequency sensing offer complementary approaches. LiDAR systems measure distance by timing the return of emitted light pulses, creating detailed spatial information about rooms, objects, and movement. In an aging-in-place setting, LiDAR could support fall detection, mobility analysis, and assessments of how residents navigate their homes. Radio-frequency systems can detect movement through changes in electromagnetic signals, sometimes allowing monitoring even when a person is not directly visible. Their potential advantage is reduced dependence on lighting and the ability to operate in private areas where cameras would be unacceptable. Yet these technologies also raise technical questions about calibration, data quality, false alarms, cybersecurity, and whether algorithms trained in one home can generalize reliably to thousands of different living environments.
The planned section also targets artificial intelligence systems that interpret ordinary activities of daily living. Computer vision models may analyze posture, household movement, food preparation, or patterns associated with medication routines, while machine-learning systems can combine data from multiple sensors to identify subtle changes. A decline in the frequency of meal preparation, slower movement between rooms, or increased nighttime activity could potentially signal emerging physical frailty, cognitive change, depression, infection, or nutritional risk. Deep-learning models are particularly capable of recognizing complex patterns in large streams of time-series data, but their predictions must be validated against clinically meaningful outcomes. The journal is seeking studies that address whether such systems improve care, rather than merely demonstrating that an algorithm can classify an event under controlled conditions.
For ambient intelligence to become part of mainstream health care, technical performance will not be enough. The new theme therefore places strong emphasis on trust, user experience, digital literacy, and the social conditions that shape AI adoption. Older adults and their families may welcome monitoring that provides reassurance, but they may also worry that continuous sensing turns a home into a surveillance environment. Concerns can involve who owns the data, who can access it, how long it is stored, and whether automated judgments could influence insurance, housing, or medical decisions. Researchers are being invited to study user-centered interfaces, transparent explanations, consent procedures, and ways to prevent digital ageism—the tendency to make assumptions about older people’s abilities, preferences, or willingness to use technology.
Health care providers represent another critical link between ambient intelligence and real-world impact. A sensor can generate thousands of observations each day, but clinicians cannot respond effectively if those observations arrive as an unfiltered stream of alerts. Implementation research will need to determine how ambient data can be summarized, prioritized, and incorporated into existing workflows. Remote care teams might receive a notification when a patient’s gait changes significantly over several weeks, rather than being alerted to every unusual movement. Electronic health record integration could allow validated trends to appear alongside laboratory results, medication information, and clinical notes. However, integration also creates challenges involving interoperability, liability, data overload, reimbursement, workforce training, and the risk that clinicians may either overtrust or ignore algorithmic recommendations.
Privacy-preserving design is expected to be a major area of discussion. Ambient systems can reduce privacy risks by processing information locally, transmitting only abstract features rather than raw recordings, or converting visual data into anonymous silhouettes and movement coordinates. Edge computing, in which analysis occurs on a device inside the home, can limit the amount of sensitive information sent to external servers. Encryption, access controls, audit trails, differential privacy, and federated learning may further protect data while allowing models to improve across multiple locations. Federated learning enables algorithms to learn from distributed data without directly pooling every resident’s records in a central repository, although model updates can still present security risks. Ethical frameworks must accompany these technical safeguards, addressing informed consent, withdrawal of participation, secondary data use, and the rights of people who may have cognitive impairment.
Dr Jing Wang, PhD, MPH, RN, FAAN, the founding editor in chief of JMIR Aging, will serve as special advisor for the theme issue. Wang is dean and professor at Florida State University’s College of Nursing, where she helped establish a Master of Science in Nursing program focused on artificial intelligence applications in health care and launched the Smart Health Home initiative. She also helped develop a partnership with the Coalition for Health AI on responsible-AI education in nursing. As co-director of the Nursing and AI Innovation Consortium, Wang works at the intersection of nursing, technology, policy, and clinical practice. Her involvement underscores the initiative’s aim of connecting engineering advances with the realities of caregiving, professional responsibility, and patient safety.
The call for submissions reflects a broader transformation in how aging and independence may be supported. Ambient intelligence could eventually enable homes to function as quiet health-monitoring environments, detecting meaningful deviations without demanding constant attention from residents or caregivers. But its success will depend on evidence that the technology is accurate, equitable, secure, affordable, and genuinely useful in daily care. JMIR Aging is inviting contributions that examine both promise and limitation, from sensor engineering and machine learning to ethics, implementation, and human behavior. The journal is indexed in PubMed, PubMed Central, MEDLINE, Scopus, DOAJ, EBSCO, CABI, and the Science Citation Index Expanded. Further information about the submission initiative is available through the journal’s announcement page.
Subject of Research: Ambient intelligence, privacy-preserving monitoring, artificial intelligence adoption, and aging-in-place technologies.
Article Title: JMIR Aging Invites Research on “Invisible” Monitoring and AI Adoption for Aging-in-Place
News Publication Date: August 20, 2026
Web References: https://aging.jmir.org/announcements/730 ; https://aging.jmir.org/
Image Credits: JMIR Publications
Keywords
Ambient intelligence, aging-in-place, older adults, artificial intelligence, machine learning, deep learning, radar sensors, LiDAR, radio-frequency sensing, computer vision, fall detection, gait analysis, mobility monitoring, digital health, privacy-preserving technology, electronic health records, caregivers, clinical implementation, responsible AI, health care innovation.
Tags: AI and IoT in aging careAI-enabled aging in placeambient intelligence for seniorsautonomous health monitoring systemsdigital health tools for aging populationInvisible health monitoringlong-term care technology innovationspassive sensing in smart homesremote monitoring of older adultssmart home sensors for independent livingunobtrusive health data collectionunobtrusive sensors for elderly care
