Fall detection and recognition from egocentric visual data: A case study

Xueyi Wang*, Estefanía Talavera Martínez, Dimka Karastoyanova, George Azzopardi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
119 Downloads (Pure)


Falling is among the most damaging events for elderly people, which sometimes may end with significant injuries. Due to fear of falling, many elderly people choose to stay more at home in order to feel safer. In this work, we propose a new fall detection and recognition approach, which analyses egocentric videos collected by wearable cameras through a computer vision/machine learning pipeline. More specifically, we conduct a case study with one volunteer who collected video data from two cameras; one attached to the chest and the other one attached to the waist. A total of 776 videos were collected describing four types of falls and nine kinds of non-falls. Our method works as follows: extracts several uniformly distributed frames from the videos, uses a pre-trained ConvNet model to describe each frame by a feature vector, followed by feature fusion and a classification model. Our proposed model demonstrates its suitability for the detection and recognition of falls from the data captured by the two cameras together. For this case study, we detect all falls with only one false positive, and reach a balanced accuracy of 93% in the recognition of the 13 types of activities. Similar results are obtained for videos of the two cameras when considered separately. Moreover, we observe better performance of videos collected in indoor scenes.

Original languageEnglish
Title of host publicationPattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings
EditorsAlberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030687625
Publication statusPublished - 2021
Event25th International Conference on Pattern Recognition Workshops, ICPR 2020 - Milan, Italy
Duration: 10-Jan-202111-Jan-2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12661 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Pattern Recognition Workshops, ICPR 2020


  • Egocentric vision
  • Fall detection
  • Fall recognition
  • Health monitoring
  • Wearable camera
  • Well-being

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