The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Enhancing elderly care: Efficient and reliable real-time fall detection algorithm
Open accessAbstract
Background and Objective: Falls pose a significant risk to public health, especially for the elderly population, and could potentially result in severe injuries or even death. A reliable fall detection system is urgently needed to recognise and promptly alert to falls effectively. A vision-based fall detection system has the advantage of being non-invasive and affordable compared with another popular approach using wearable sensors. Nevertheless, the present challenge lies in the algorithm's limited on-device operating speed due to extremely high computational demands, and the high computational demands are usually essential to improve the performance for the complex scene. Therefore, it is crucial to address the above challenge in computational power and complex scenes. Methods: This article presents the implementation of a real-time fall detection algorithm with low computational costs using a single webcam. The suggested method optimises precision and efficiency by synthesising the strengths of background subtraction and the human pose estimation model BlazePose. The biomechanical features, derived from body key points identified by BlazePose, are utilised in a random forest model for classifying fall events. Results: The proposed algorithm achieves 89.99% accuracy and 29.7 FPS with a laptop CPU on the UR Fall Detection dataset and the Le2i Fall Detection dataset. The algorithm shows great generalisation and robustness in different scenarios. Conclusion: Due to the low computational power of the system, the findings also suggest the potential for implementing the system in small-scale medical monitoring equipment, which maximises its practical value in digital health.
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“An AR>1.0 typically indicated a transition to a lying state, distinguishing falls from daily activities like sitting [14].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Given the inherent class imbalance (pre-test n= 41,223 vs. post-test n= 7969), representative of real-world scenarios where “correct” falls are less frequent than untrained ones, we prioritised metrics robust to skewness, specifically the F1-Score and the Area Under the Receiver Operating Characteristic Curve (AUC) [14].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [14]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Special emphasis was placed on Sensitivity (Recall), as minimizing False Negatives (labeling a dangerous fall as safe) is critical in injury prevention scenarios [14].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [14]
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“Unlike binary fall detection systems that focus solely on identifying the occurrence of a fall event [14], this analysis aims to quantify the learning effect embedded in the movement patterns.”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [14]
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“For instance, Wang et al. (2024) reported an accuracy of 89.99% using Random Forest with BlazePose features [14], and Kong et al. (2025) achieved an F1-score of 91.4% using an SVM on geometric features [20].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [14]
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“Our proposed framework compares favourably with recent vision-based benchmarks. For instance, Wang et al. (2024) reported an accuracy of 89.99% using Random Forest with BlazePose features [14], and Kong et al. (2025) achieved an F1-score of 91.4% using an SVM on geometric features [20].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [14]
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In the citing paper
“This “White-Box” approach is essential for validating the pedagogical effectiveness of the Safe Fall programme [14].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [14]
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2929257 · Cited by 5,536
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Efficient adaptive density estimation per image pixel for the task of background subtraction
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Prevention of falls and consequent injuries in elderly people
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Elderly Fall Detection Systems: A Literature Survey
Wang, X., Ellul, J., & Azzopardi, G. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00071 · Cited by 410
Charfi, I., Mitéran, J., Dubois, J., Atri, M., & Tourki, R. (2013) · Journal of Electronic Imaging · doi:10.1117/1.jei.22.4.041106 · Cited by 217
Distinguishing fall activities from normal activities by velocity characteristics
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Research works printed but not linked: 1
- Bazarevsky V, 2020, BlazePose: on-device real-time body pose tracking
Cited by
Sorted by citations
DelCastillo-Andrés, O., Fernández-García, R., Pastor-Vicedo, J. C., Lira, M. A., Campos-Mesa, M. C., Castañeda-Vázquez, C., Genovesi, E., Krstulović, S., Kuvačić, G., Morvay-Sey, K., et al. (2026) · Sensors · doi:10.3390/s26082491 · Cited by 1
“An AR > 1.0 typically indicated a transition to a lying state, distinguishing falls from daily activities like sitting [14].”
Influential
Metadata
- Authors
- Wang, Y., & Deng, T.
- Affiliations
- University of Sheffield (GB)
- Corresponding author
- Deng, Tiantai
- Venue
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Published
- 2024-01
- Volume
- 10
- Pages
- 20552076241233690-20552076241233690
- ISSN
- 2055-2076 (ISSN-L)
- PMC
- PMC10880526
- OpenAlex
- W4391975041
- Cited by
- 28
- References
- 21
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 5
- FWCI
- 3.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- University of Sheffield
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- fall detection, elderly care, real-time fall detection, background subtraction, human pose estimation, BlazePose, biomechanical features, random forest classification, low computational cost, webcam-based monitoring
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