Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection

Anitha Rani Inturi, Vazhora Malayil Manikandan*, Mahamkali Naveen Kumar, Shuihua Wang, Yudong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


According to the World Health Organisation, falling is a major health problem with potentially fatal implications. Each year, thousands of people die as a result of falls, with seniors making up 80% of these fatalities. The automatic detection of falls may reduce the severity of the consequences. Our study focuses on developing a vision-based fall detection system. Our work proposes a new feature descriptor that results in a new fall detection framework. The body geometry of the subject is analyzed and patterns that help to distinguish falls from non-fall activities are identified in our proposed method. An AlphaPose network is employed to identify 17 keypoints on the human skeleton. Thirteen keypoints are used in our study, and we compute two additional keypoints. These 15 keypoints are divided into five segments, each of which consists of a group of three non-collinear points. These five segments represent the left hand, right hand, left leg, right leg and craniocaudal section. A novel feature descriptor is generated by extracting the distances from the segmented parts, angles within the segmented parts and the angle of inclination for every segmented part. As a result, we may extract three features from each segment, giving us 15 features per frame that preserve spatial information. To capture temporal dynamics, the extracted spatial features are arranged in the temporal sequence. As a result, the feature descriptor in the proposed approach preserves the spatio-temporal dynamics. Thus, a feature descriptor of size (Formula presented.) is formed where m is the number of frames. To recognize fall patterns, machine learning approaches such as decision trees, random forests, and gradient boost are applied to the feature descriptor. Our system was evaluated on the UPfall dataset, which is a benchmark dataset. It has shown very good performance compared to the state-of-the-art approaches.

Original languageEnglish
Article number6283
Issue number14
Publication statusPublished - Jul 2023
Externally publishedYes


  • ambient intelligence
  • assistive technology
  • fall detection
  • fall prevention
  • real-time monitoring
  • risk assessment
  • signal processing
  • video analysis
  • vision-based human activity recognition


Dive into the research topics of 'Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection'. Together they form a unique fingerprint.

Cite this