Abstract: |
In this paper, we study the applications of metaheuristics (MH) optimization algorithms
in human activity recognition (HAR) and fall detection based on sensor data. It is known that MH
algorithms have been utilized in complex engineering and optimization problems, including feature
selection (FS). Thus, in this regard, this paper used nine MH algorithms as FS methods to boost
the classification accuracy of the HAR and fall detection applications. The applied MH were the
Aquila optimizer (AO), arithmetic optimization algorithm (AOA), marine predators algorithm (MPA),
artificial bee colony (ABC) algorithm, genetic algorithm (GA), slime mold algorithm (SMA), grey
wolf optimizer (GWO), whale optimization algorithm (WOA), and particle swarm optimization
algorithm (PSO). First, we applied efficient prepossessing and segmentation methods to reveal the
motion patterns and reduce the time complexities. Second, we developed a light feature extraction
technique using advanced deep learning approaches. The developed model was ResRNN and was
composed of several building blocks from deep learning networks including convolution neural
networks (CNN), residual networks, and bidirectional recurrent neural networks (BiRNN). Third, we
applied the mentioned MH algorithms to select the optimal features and boost classification accuracy.
Finally, the support vector machine and random forest classifiers were employed to classify each
activity in the case of multi-classification and to detect fall and non-fall actions in the case of binary
classification. We used seven different and complex datasets for the multi-classification case: the
PAMMP2, Sis-Fall, UniMiB SHAR, OPPORTUNITY, WISDM, UCI-HAR, and KU-HAR datasets. In
addition, we used the Sis-Fall dataset for the binary classification (fall detection). We compared the
results of the nine MH optimization methods using different performance indicators. We concluded
that MH optimization algorithms had promising performance in HAR and fall detection applications.
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