ArticleSensors (Basel, Switzerland)2019
Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Fall Detection in Elderly People: A Systematic Review of Ambient Assisted Living and Smart Home-Related Technology Performance.Sensors (Basel, Switzerland) · 2025Pooled it
- Simulation-to-Real Trip-Fall Detection with Continuous-Wave Doppler Radar via Physics-Informed Kinematic Modeling and Domain Randomization.Sensors (Basel, Switzerland) · 2026Article
- Current Trends in Artificial Intelligence for Recognizing Work Postures to Prevent Work-Related Musculoskeletal Disorders: Systematic Review and Meta-Analysis by Occupational Activity.Bioengineering (Basel, Switzerland) · 2026Review
- Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures.International journal of environmental research and public health · 2023Article
- Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems.Sensors (Basel, Switzerland) · 2021Article
- Correction: Liu et al. Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM.Sensors (Basel, Switzerland) · 2021Article
- Feasibility of Using Floor Vibration to Detect Human Falls.International journal of environmental research and public health · 2020Article
- Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Human falls are the premier cause of fatal and nonfatal injuries among older adults. The health outcome of a fall event is largely dependent on rapid response and rescue of the fallen elder. Being able to provide an accurate and fast fall detection will dramatically improve the health outcomes of the older population and reduce the associated healthcare cost after a fall. To achieve the goal, a multi-features semi-supervised support vector machines (MFSS-SVM) algorithm utilizing measurements from structural floor vibration obtained through accelerometers is proposed in this study to detect falling events with limited labeled samples. In this MFSS-SVM algorithm, the peak value, energy, and correlation coefficient of the accelerometer signal are used as classification features. The performance of the proposed algorithm was validated with laboratory experiments among activities including falling, walking, free jumping, rhythmic jumping, bag dropping, and ball dropping. To further illustrate the performance of the algorithm, a benchmark database was adopted and expanded to test its ability to accurately identify falling, compared with the algorithm used in the benchmark study. Results show that by using the proposed algorithm, the falling events can be identified with high accuracy and confidence, even with small training datasets and test nodes.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.