Dataset for human fall recognition in an uncontrolled environment
Open accessAbstract
This article presents a dataset (CAUCAFall) with ten subjects, which simulates five types of falls and five types of activities of daily living (ADLs). Specifically, the data include forward falls, backward falls, lateral falls left, lateral falls right, and falls arising from sitting. The participants performed the following ADLs: walking, hopping, picking up an object, sitting, and kneeling. The dataset considers individuals of different ages, weights, heights, and dominant legs. The data were acquired using an RGB camera in a home environment. This environment was intentionally realistic and included uncontrolled features, such as occlusions, lighting changes (natural, artificial, and night), participants different clothing, movement in the background, different textures on the floor and in the room, and a variety in fall angles and different distances from the camera to the fall. The dataset consists of 10 folders, one for each subject, and each folder includes ten subfolders with the performed activities. Each folder contains the video of the action and all the images of that action. CAUCAFall is the only database that contains details of the lighting lux of the scenarios, the distances from the human fall to the camera and the angles of the different falls with reference to the camera. The dataset is also the only one that contains labels for each image. Frames including human falls recorded were labeled as ``fall'', and ADL activities were marked ``nofall". This dataset is useful for developing and evaluating modern fall recognition algorithms, such as those that apply feature extraction, convolutional neural networks with YOLOv3-v4 detectors, and camera location and resolution increase the performance of algorithms such as OPENPOSE. Thus, the dataset enables knowledge of the real progress of research in this area since existing datasets are used in strictly controlled environments. The authors intend to contribute a dataset with real-world housing environments characteristics.
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This approach preserved the natural variability of children's movements, addressing a common limitation in fall detection datasets, which often rely on simulated falls in sterile environments.
“This approach preserved the natural variability of children’s movements, addressing a common limitation in fall detection datasets, which often rely on simulated falls in sterile environments [9].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [9]
The Value of the Data section says fall-detection datasets often use restricted environments and simulated falls, supporting the stated limitation. The Data collection protocol describes an uncontrolled home setting with varied conditions, but it does not discuss children or establish that this approach preserved the natural variability of children’s movements; its participants simulated falls. (LLM, conf=0.88)
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Cites
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A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
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Flagged first, then by citations
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Cited by
Sorted by citations
DelCastillo-Andrés, O., Fernández-García, R., Pastor-Vicedo, J. C., Lira, M. A., Campos-Mesa, M. C., Castañeda-Vázquez, C., Genovesi, E., Krstulović, S., Kuvačić, G., Morvay-Sey, K., et al. (2026) · Sensors · doi:10.3390/s26082491 · Cited by 1 · see citing works
- Authors
- Guerrero, J. C. E., España, E. M., Añasco, M. M., & Lopera, J. E. P.
- Affiliations
- Universidad del Cauca (CO)
- Universidad de la Amazonia (CO)
- Corresponding author
- Guerrero, José Camilo Eraso
- Venue
- Data in Brief
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 45
- Pages
- 108610
- ISSN
- 2352-3409 (ISSN-L)
- PMC
- PMC9508401
- OpenAlex
- W4296143945
- Cited by
- 45
- References
- 16
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 17 · 2023: 4
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Data in Brief (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universidad del Cauca
- 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
- activities of daily living, RGB video, uncontrolled environments, occlusion, lighting conditions, camera distance, fall detection, family environment, image annotation
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