Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fall Detection from Human Shape and Motion History Using Video Surveillance
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
An active vision system for fall detection and posture recognition in elderly healthcare
Proceedings of the Conference on Design, Automation and Test in Europe
Fall detection from depth map video sequences
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
Slip and fall event detection using Bayesian Belief Network
Pattern Recognition
Eigenspace-based fall detection and activity recognition from motion templates and machine learning
Expert Systems with Applications: An International Journal
Health-enabling technologies for the elderly - An overview of services based on a literature review
Computer Methods and Programs in Biomedicine
Comparison and validation of capacitive accelerometers for health care applications
Computer Methods and Programs in Biomedicine
Mobile wearable device for long term monitoring of vital signs
Computer Methods and Programs in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach
IEEE Transactions on Circuits and Systems for Video Technology
A survey on fall detection: Principles and approaches
Neurocomputing
Vision-based motion detection, analysis and recognition of epileptic seizures-A systematic review
Computer Methods and Programs in Biomedicine
A smartphone-based fall detection system
Pervasive and Mobile Computing
3D head tracking for fall detection using a single calibrated camera
Image and Vision Computing
Automatic detection of freezing of gait events in patients with Parkinson's disease
Computer Methods and Programs in Biomedicine
ACM Transactions on Management Information Systems (TMIS)
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A fall detection method based on depth image analysis is proposed in this paper. As different from the conventional methods, if the pedestrians are partially overlapped or partially occluded, the proposed method is still able to detect fall events and has the following advantages: (1) single or multiple pedestrian detection; (2) recognition of human and non-human objects; (3) compensation for illumination, which is applicable in scenarios using indoor light sources of different colors; (4) using the central line of a human silhouette to obtain the pedestrian tilt angle; and (5) avoiding misrecognition of a squat or stoop as a fall. According to the experimental results, the precision of the proposed fall detection method is 94.31% and the recall is 85.57%. The proposed method is verified to be robust and specifically suitable for applying in family homes, corridors and other public places.