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. 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.
@InProceedings{8553340,
author = {M. Saleh and R. {Le Bouquin Jeannès}},
booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
title = {An Efficient Machine Learning-Based Fall Detection Algorithm using Local Binary Features},
year = {2018},
pages = {667-671},
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.},
keywords = {decision making;geriatrics;learning (artificial intelligence);medical computing;local binary features;world health organization;rapid medical intervention;automatic fall detection systems;efficient machine learning-based fall detection algorithm;trained machine;low-power consumption wearable fall detectors;Acceleration;Feature extraction;Machine learning algorithms;Detection algorithms;Signal processing algorithms;Detectors;Senior citizens;fall detection;binary features;local features;machine learning;elderly},
doi = {10.23919/EUSIPCO.2018.8553340},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437716.pdf},
}
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