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The study aims to systematically map the landscape of video-based computer vision fall detection systems.

Fall Detection Based on Computer Vision: A Systematic Mapping Study

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The study aims to systematically map the landscape of video-based computer vision fall detection systems.
Conducted a systematic mapping of 433 primary studies using PRISMA-inspired workflows.
Characterized approaches into a taxonomy of Feature Engineering, Deep Learning, and hybrid models.
Analyzed algorithmic performance metrics and deployment evidence.
Identified a predominance of deep learning approaches, particularly CNN-based models.
Noted increased use of Transformers and Attention architectures in recent years.
Found inconsistencies in reporting efficiency metrics, affecting reproducibility.

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This systematic study maps video-based fall detection in older adults, highlighting trends and gaps in research.

Gomes et al. (2026) studied this question.

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