Fall detection and pre-impact prediction technologies in older adults

Objective: To map the current landscape of wearable and sensor-based fall detection and pre-impact prediction technologies relevant to older adults and to evaluate their translational maturity within public health contexts. Methods: A scoping review was conducted following PRISMA-ScR guidelines. Four electronic databases (PubMed, Web of Science, Scopus, and IEEE Xplore) were systematically searched for studies published between January 2005 and September 2025. Eligible studies reported the development or validation of fall detection or pre-impact prediction systems incorporating wearable, vision-based, environmental, or multimodal sensing modalities. In total, 243 studies were included in the overall synthesis, with a predefined subgroup of 21 studies involving real-world or mixed real-world validation in older adult populations (≥65 years). Results: = 21), 71.4% focused on post-fall detection, 19.0% investigated pre-impact prediction, and 9.5% addressed fall risk modeling. While technical performance metrics such as sensitivity and specificity were frequently reported under controlled conditions, evidence regarding long-term adherence, workflow integration, and health economic impact was limited. A maturity gradient emerged across modalities, with wearable detection systems demonstrating stronger ecological grounding than predictive, multimodal, and ecosystem-level approaches. Conclusion: Although technological innovation in fall-related sensing systems has expanded rapidly, translational maturity remains uneven. Bridging the gap between algorithmic performance and scalable public health implementation will require robust real-world validation, longitudinal adherence evaluation, implementation science frameworks, and economic assessment. Advancing along a continuum from reactive detection toward predictive and personalized prevention represents a critical pathway for supporting safe and independent aging

Public health · Translational research · Wearable computer · Wearable technology · Workflow · Balance, Gait, and Falls Prevention · Context-Aware Activity Recognition Systems · Healthcare Technology and Patient Monitoring

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