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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2205 |
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| author | Yifei Li Pei Liu Yan Fang Xiangyuan Wu Yewei Xie Zhongzhi Xu Hao Ren Fengshi Jing |
| author_facet | Yifei Li Pei Liu Yan Fang Xiangyuan Wu Yewei Xie Zhongzhi Xu Hao Ren Fengshi Jing |
| author_sort | Yifei Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-741e69e2e805408185f2a9a0ec8b82b6 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-741e69e2e805408185f2a9a0ec8b82b62025-08-20T03:08:59ZengMDPI AGSensors1424-82202025-03-01257220510.3390/s25072205A Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization ReviewYifei Li0Pei Liu1Yan Fang2Xiangyuan Wu3Yewei Xie4Zhongzhi Xu5Hao Ren6Fengshi Jing7Hikvision Research Institute, Hangzhou 310051, ChinaHikvision Research Institute, Hangzhou 310051, ChinaFaculty of Data Science, City University of Macau, Taipa, Macao SAR 999078, ChinaFaculty of Data Science, City University of Macau, Taipa, Macao SAR 999078, ChinaProgramme in Health Services and Systems Research, Duke-NUS Medical School, Singapore 169857, SingaporeSchool of Public Health, Sun Yat-sen University, Guangzhou 510080, ChinaGuangzhou Key Laboratory of Smart Home Ward and Health Sensing, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou 510317, ChinaFaculty of Data Science, City University of Macau, Taipa, Macao SAR 999078, ChinaOver 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.https://www.mdpi.com/1424-8220/25/7/2205wearable sensorInternet of Thingsfall detectionfall preventioninertial sensorpre-impact fall |
| spellingShingle | Yifei Li Pei Liu Yan Fang Xiangyuan Wu Yewei Xie Zhongzhi Xu Hao Ren Fengshi Jing A Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization Review Sensors wearable sensor Internet of Things fall detection fall prevention inertial sensor pre-impact fall |
| title | A Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization Review |
| title_full | A Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization Review |
| title_fullStr | A Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization Review |
| title_full_unstemmed | A Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization Review |
| title_short | A Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization Review |
| title_sort | decade of progress in wearable sensors for fall detection 2015 2024 a network based visualization review |
| topic | wearable sensor Internet of Things fall detection fall prevention inertial sensor pre-impact fall |
| url | https://www.mdpi.com/1424-8220/25/7/2205 |
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