An Efficient Machine Learning-Based Fall Detection Algorithm using Local Binary Features. Saleh, M. & Le Bouquin Jeannès, R. In 2018 26th European Signal Processing Conference (EUSIPCO), pages 667-671, Sep., 2018.
An Efficient Machine Learning-Based Fall Detection Algorithm using Local Binary Features [pdf]Paper  doi  abstract   bibtex   
According to the world health organization, millions of elderly suffer from falls every year. These falls are one of the major causes of death worldwide. As a rapid medical intervention would considerably decrease the serious consequences of such falls, automatic fall detection systems for elderly has become a necessity. In this paper, an efficient machine learning-based fall detection algorithm is proposed. Thanks to the proposed local binary features, this algorithm shows a high accuracy exceeding 99% when tested on a large dataset. In addition, it enjoys an attractive property that the computational cost of decision-making is independent from the complexity of the trained machine. Thus, the proposed algorithm overcomes a critical challenge of designing accurate yet low-cost solutions for wearable fall detectors. The aforementioned property enables implementing autonomous, low-power consumption wearable fall detectors.

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