ArticleInternational journal of environmental research and public health2020
Feasibility of Using Floor Vibration to Detect Human Falls.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 17 citations in OpenAlex.
- Human Fall Detection with Infrared Imaging: A Comparison of Graph Convolutional Networks and YOLO.Sensors (Basel, Switzerland) · 2026Article
- Research of Fall Detection and Fall Prevention Technologies: A Review.Sensors (Basel, Switzerland) · 2026Review
- Monomethyl Phthalate Causes Early Embryo Development Delay, Apoptosis, and Energy Metabolism Disruptions Through Inducing Redox Imbalance.Reproductive sciences (Thousand Oaks, Calif.) · 2024Article
- Probabilistic detection of impacts using the PFEEL algorithm with a Gaussian Process Regression Model.Engineering structures · 2023Article
- Prenatal exposure to famine and the development of diabetes later in life: an age-period-cohort analysis of the China health and nutrition survey (CHNS) from 1997 to 2015.European journal of nutrition · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.
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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 Socratic graph.