Abstract
Falling has become the leading cause of non-fatal and preventable injuries. The majority of existing fall detection systems (FDSs) rely on motion sensors. However, most of the applied feature extraction methods result in longer processing time and higher computation. This study proposes a novel attitude feature extraction (AFE) method that extract five feature sets from 54 raw measurements obtained from nine inertial measurement units (IMUs) placed on the firefighter’s protective clothing. Our results indicate that the metrics of attitude feature extraction (AFEM) method outperforms the existing metrics of raw feature extraction (RFEM) method in the fall detection. In addition, the proposed method reduces the algorithm processing time and computations significantly. This enables on-device fall detection classification on constrained processing architectures.
Published Version
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