College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China.
College of Engineering and Information Technology, Buraydah Private Colleges, Buraydah 51418, Saudi Arabia.
Biosensors (Basel). 2022 Oct 3;12(10):821. doi: 10.3390/bios12100821.
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.
在本文中，我们研究了元启发式（MH）优化算法在基于传感器数据的人体活动识别（HAR）和跌倒检测中的应用。众所周知，MH 算法已被应用于复杂的工程和优化问题，包括特征选择（FS）。因此，在这方面，本文使用了九种 MH 算法作为 FS 方法，以提高 HAR 和跌倒检测应用的分类准确性。应用的 MH 算法包括：雕鸮优化器（AO）、算术优化算法（AOA）、海洋捕食者算法（MPA）、人工蜂群算法（ABC）、遗传算法（GA）、黏菌算法（SMA）、灰狼优化器（GWO）、鲸鱼优化算法（WOA）和粒子群优化算法（PSO）。首先，我们应用高效的预处理和分割方法来揭示运动模式并降低时间复杂度。其次，我们使用先进的深度学习方法开发了一种轻量级特征提取技术。所开发的模型是 ResRNN，它由包括卷积神经网络（CNN）、残差网络和双向循环神经网络（BiRNN）在内的几个深度学习网络的构建块组成。第三，我们应用上述 MH 算法选择最佳特征并提高分类准确性。最后，支持向量机和随机森林分类器用于对多分类情况下的每个活动进行分类，并对二进制分类情况下的跌倒和非跌倒动作进行检测。我们使用了七个不同的复杂数据集进行多分类：PAMMP2、Sis-Fall、UniMiB SHAR、OPPORTUNITY、WISDM、UCI-HAR 和 KU-HAR 数据集。此外，我们还使用 Sis-Fall 数据集进行二进制分类（跌倒检测）。我们使用不同的性能指标比较了这九种 MH 优化方法的结果。我们得出结论，MH 优化算法在 HAR 和跌倒检测应用中具有良好的性能。
