Pose Estimation based Fall Detection system using MediaPipe
Abstract
Vision based Fall detection systems are much better in terms of convenience and accuracy over other Fall detection methodologies which rely on additional sensory data. The computational cost associated with large number of parameters within non vision based Fall Detection systems make them inefficient performance wise in real time. Feature extraction from video sequences is a very extensive task when done through conventional methodologies and proves to be highly computationally expensive. Due to the rising number of falls in the world and its consequences, a need to develop a fall detection system has emerged, which is not computationally expensive and provides a real time detection with least false positives. A pose estimation based approach which utilises the skeleton key points of human body with help of MediaPipe is explored. Decision conditions are used to classify whether a fall has occurred or not, that include - the velocity of descent over frames, decrease in height trend of the bounding box and the bounding box width-height ratio change over frames. The accuracy obtained is 95.84%.
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Numerous studies explore computer-vision pose estimation techniques, because the extracted data have a wide range of applications including healthcare, sports, daily activity monitoring, and human motion analysis.
“Currently, numerous studies explore these techniques, as the extracted data have a wide range of applications, including healthcare, sports, daily activity monitoring, and any field where human motion analysis is relevant [27, 28].”
From PyBodyTrack: A python library for multi-algorithm motion quantification and tracking in videos (2025) · cites this paper as [27]
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PyBodyTrack: A python library for multi-algorithm motion quantification and tracking in videos
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- Authors
- Saraswat, S., & Malathi, G.
- Affiliations
- Vellore Institute of Technology University (IN)
- ORCID
- Malathi, G
- Venue
- 2024 10th International Conference on Communication and Signal Processing (ICCSP)
- Publisher
- IEEE
- Published
- 2024-04-12
- Pages
- 1733-1738
- OpenAlex
- W4399397175
- Cited by
- 6
- References
- 16
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 1
- FWCI
- 1.24
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- fall detection, pose estimation, MediaPipe, video-based fall detection, real-time detection, false positive reduction
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