Yifei Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yifei Li has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Fengshi Jing
Faculty of Data Science, City University of Macau, Macao SAR, China
1 shared publication - Hao Ren
Faculty of Data Science, City University of Macau, Avenida Padre Tomás Pereira, Taipa, Macao 999078, P. R. China
1 shared publication - Pei Liu
College of Computer Science, Sichuan University, Chengdu 610065, China
1 shared publication - Xiangyuan Wu
Faculty of Data Science, City University of Macau, Taipa, Macao SAR 999078, China
1 shared publication - Yan Fang
Faculty of Data Science, City University of Macau, Macau, China
1 shared publication - Yewei Xie
Programme in Health Services Research & Population Health, Duke-NUS Medical School , 8 College Road, Singapore 169857
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
A Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization Review
Over the past decade, wearable sensors for fall detection have gained significant attention due to their potential in improving the safety of elderly users and reducing fall-related injuries. This review employs a network-based visualization approach to analyze research trends, key technologies, and collaborative networks. Using studies from SCI- and SSCI-indexed journals from 2015 to 2024, we analyzed 582 articles and 65 reviews with CiteSpace, revealing a significant rise in research on wearable sensors for fall detection. Additionally, we reviewed various datasets and machine learning techniques, from traditional methods to advanced deep learning frameworks, which demonstrate high accuracies, F1 scores, sensitivities, and specificities in controlled settings. This review provides a comprehensive overview of the progress and emerging trends, offering a foundation for future advancements in wearable fall detection systems.
Top Cited PapersOpen AccessA Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization Review
Over the past decade, wearable sensors for fall detection have gained significant attention due to their potential in improving the safety of elderly users and reducing fall-related injuries. This review employs a network-based visualization approach to analyze research trends, key technologies, and collaborative networks. Using studies from SCI- and SSCI-indexed journals from 2015 to 2024, we analyzed 582 articles and 65 reviews with CiteSpace, revealing a significant rise in research on wearable sensors for fall detection. Additionally, we reviewed various datasets and machine learning techniques, from traditional methods to advanced deep learning frameworks, which demonstrate high accuracies, F1 scores, sensitivities, and specificities in controlled settings. This review provides a comprehensive overview of the progress and emerging trends, offering a foundation for future advancements in wearable fall detection systems.