What does this research mean for the field?

Despite advancements in deep learning, very few vision-based fall detection systems validate real-time performance on physical hardware, and existing models are severely limited by a lack of real-world testing with older adults and reliance on simulated fall datasets. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This review aims to evaluate vision-based fall detection systems specifically focused on real-time performance metrics.

August 13, 2026SensorsOpen Access

Real-Time Vision-Based Fall Detection Systems for the Elderly: A Systematic Review

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This review aims to evaluate vision-based fall detection systems specifically focused on real-time performance metrics.
Conducted a systematic review following PRISMA and Kitchenham guidelines.
Identified records from four databases between 2019 and 2024, narrowing down to 11 studies meeting all inclusion criteria.
Analyzed architectures, deployment platforms, and optimization strategies for real-time inference.
CNN-based architectures were the most common, with a preference for edge devices for deployment.
Only 11 studies validated real-time performance on physical hardware, indicating a significant gap.
Highlighted issues included lack of real-world testing with older adults and dependency on small controlled datasets.

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Implication

Systematic review evaluates real-time performance of vision-based fall detection systems for the elderly, indicating gaps in validation.

Cite This Study

A 2026 study studied this question.

synapsesocial.com/papers/6a7d75c72b0e0cff3f63e92a https://doi.org/10.3390/s26165069

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Real-Time Vision-Based Fall Detection Systems for the Elderly: A Systematic Review

Key Points

Discussion

Implication

Cite This Study

Real-Time Vision-Based Fall Detection Systems for the Elderly: A Systematic Review

Key Points

Discussion

Implication

Cite This Study