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A Decade of Progress in Wearable Sensors for Fall Detection (2015-2024): A Network-Based Visualization Review
Sensors (Basel, Switzerland)
|April 12, 2025
Summary
Wearable sensors for fall detection are advancing rapidly, enhancing elderly safety. Research shows significant growth in this field, utilizing machine learning for accurate fall detection and injury reduction.
Area of Science:
- Biomedical Engineering
- Gerontology
- Computer Science
Background:
- Wearable sensors are crucial for elderly safety and reducing fall-related injuries.
- Significant research attention has focused on developing effective fall detection systems.
- Existing systems face challenges in real-world deployment and accuracy.
Purpose of the Study:
- To analyze research trends, key technologies, and collaborations in wearable fall detection.
- To review datasets and machine learning techniques used in fall detection.
- To provide a comprehensive overview of progress and future directions.
Main Methods:
- Network-based visualization analysis using CiteSpace.
- Analysis of 582 articles and 65 reviews from SCI- and SSCI-indexed journals (2015-2024).
- Review of various datasets and machine learning algorithms, including deep learning.
Main Results:
- A significant increase in research publications on wearable fall detection sensors.
- Machine learning and deep learning models demonstrate high accuracy, F1 scores, sensitivity, and specificity in controlled environments.
- Identification of key technologies and collaborative research networks.
Conclusions:
- Wearable fall detection technology is a rapidly evolving field with substantial research growth.
- Advanced machine learning techniques show promise for accurate fall detection.
- This review offers a foundation for future advancements in wearable fall detection systems.
