An efficient machine learning-based fall detection algorithm using local binary features

Abstract : 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. © EURASIP 2018.
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Conference papers
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-02036727
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Submitted on : Wednesday, February 20, 2019 - 11:32:13 AM
Last modification on : Thursday, February 21, 2019 - 1:27:58 AM

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M. Saleh, R. Le Bouquin Jeannès. An efficient machine learning-based fall detection algorithm using local binary features. 26th European Signal Processing Conference, EUSIPCO 2018, Sep 2018, Rome, Italy. pp.667-671, ⟨10.23919/EUSIPCO.2018.8553340⟩. ⟨hal-02036727⟩

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