Enhancing Geriatric Care with AI: Strategies for Fall Prevention and Aging-in-Place

The integration of artificial intelligence (AI) in geriatric care marks a pivotal shift towards addressing the essential needs of the rapidly growing elderly population globally. Challenges such as maintaining independence and reducing fall-related injuries have become increasingly prominent, emphasizing the necessity for advanced, proactive healthcare strategies. AI technologies, encompassing machine learning, sensor technologies, and robotics, offer revolutionary methods to enhance early detection of fall risks, constant health monitoring, and efficient care coordination. This technological synergy not only minimizes the risk of injuries but also elevates the social and emotional well-being of older adults through personalized care and sustained communication.

This Research Topic aims to dissect the influence and capabilities of AI-driven applications in reshaping geriatric healthcare, particularly focusing on fall prevention and support for aging-in-place. Through detailed research, the goal is to evaluate how AI can be effectively integrated into everyday health management for the elderly, addressing both their immediate safety and overall quality of life. The emphasis will be on the deployment of AI to tailor interventions that meet individual needs and continuously adapt based on real-world data, ensuring that older adults can enjoy a safer, more autonomous lifestyle.

To gather further insights into this transformative approach to elderly care, we welcome articles addressing, but not limited to, the following themes:

•Sensor-Based Monitoring and Wearable Technologies
•Advanced Machine Learning and Predictive Analytics
•Robotics and Assistive Devices
•Telehealth and Remote Monitoring Integration
•User-Centric Design and Human Factors
•Ethical, Legal, and Socioeconomic Considerations

We invite contributions in the form of original research articles, systematic reviews, meta-analyses, short communications, and case studies that explore innovative solutions and critical assessments pertinent to this field. Through this Research Topic, we aim to showcase groundbreaking strategies that improve fall prevention mechanisms and support a more sustainable aging-in-place paradigm for older adults.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence, Aging-in-place, Fall Prevention, Healthcare Technology, Geriatric Care

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.