Summarising contextual activity and detecting unusual inactivity in a supportive home environment
Pattern Analysis & Applications
Identification of gait patterns related to health problems of elderly
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Automatic recognition of gait-related health problems in the elderly using machine learning
Multimedia Tools and Applications
Real-Time fall detection method based on hidden markov modelling
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
A survey on fall detection: Principles and approaches
Neurocomputing
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In this paper, we present an approach for human fall detection, which has important applications in the field of safety and security. The proposed approach consists of two parts: object detection and the use of a fall model. We use an adaptive background subtraction method to detect a moving object and mark it with its minimum-bounding box. The fall model uses a set of extracted features to analyze, detect and confirm a fall. We implement a two-state finite state machine (FSM) to continuously monitor people and their activities. Experimental results show that our method can detect most of the possible types of single human falls quite accurately.