Fall detection using human skeleton features

Heilym Ramirez, Sergio A. Velastin, Ernesto Fabregas, Ignacio Meza, Dimitrios Makris, Gonzalo Farias

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations


Falls are one of the leading causes of death and serious injury in people, especially for the elderly. In addition, falls accidents have a direct financial cost for health systems and, indirectly, for the productivity of society. Among the most important problems in fall detection systems is privacy, limitations of operating devices, and the comparison of machine learning techniques for detection. This article presents a fall detection system by means of a k-Nearest Neighbor (KNN) classifier based on camera-vision using pose detection of the human skeleton for the features extraction. The proposed method is evaluated with UP-FALL dataset, surpassing the results of other fall detection systems that use the same database. This method achieves a 98.84% accuracy and an F1-Score of 97.41%.

Original languageEnglish
Number of pages6
StatePublished - 2021
Externally publishedYes
Event11th International Conference of Pattern Recognition Systems, ICPRS 2021 - Virtual, Online
Duration: 17 Mar 202119 Mar 2021


Conference11th International Conference of Pattern Recognition Systems, ICPRS 2021
CityVirtual, Online


  • Computer vision
  • Fall detection
  • Human skeleton
  • Pose estimation


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