Modeling Human Skeleton Joint Dynamics for Fall Detection

Research output: Chapter in Book/Conference paperConference paperpeer-review

2 Citations (Scopus)


The increasing pace of population aging calls for better care and support systems. Falling is a frequent and critical problem for elderly people causing serious long-term health issues. Fall detection from video streams is not an attractive option for real-life applications due to privacy issues. Existing methods try to resolve this issue by using very low-resolution cameras or video encryption. However, privacy cannot be ensured completely with such approaches. Key points on the body, such as skeleton joints, can convey significant information about motion dynamics and successive posture changes which are crucial for fall detection. Skeleton joints have been explored for feature extraction but with image recognition models that ignore joint dependency across frames which is important for the classification of actions. Moreover, existing models are over-parameterized or evaluated on small datasets with very few activity classes. We propose an efficient graph convolution network model that exploits spatio-temporal joint dependencies and dynamics of human skeleton joints for accurate fall detection. Our method leverages dynamic representation with robust concurrent spatio-temporal characteristics of skeleton joints. We performed extensive experiments on three large-scale datasets. With a significantly smaller model size than most existing methods, our proposed method achieves state-of-the-art results on the large scale NTU datasets.
Original languageEnglish
Title of host publicationInternational Conference on Digital Image Computing: Techniques and Applications (DICTA)
EditorsJun Zhou, Olivier Salvado, Ferdous Sohel, Paulo Vinicius K. Borges, Shilin Wang
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781665417099
Publication statusPublished - 2021
EventDigital Image Computing: Technqiues and Applications (DICTA) 2021 - Gold Coast, Australia
Duration: 29 Nov 20211 Dec 2021

Publication series

NameDICTA 2021 - 2021 International Conference on Digital Image Computing: Techniques and Applications


ConferenceDigital Image Computing: Technqiues and Applications (DICTA) 2021
CityGold Coast


Dive into the research topics of 'Modeling Human Skeleton Joint Dynamics for Fall Detection'. Together they form a unique fingerprint.

Cite this