College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
Sensors (Basel). 2021 Sep 26;21(19):6434. doi: 10.3390/s21196434.
The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency.
人类活动识别（HAR）的研究在医疗保健、娱乐、体育和智能家居等许多领域都起着重要作用。随着可穿戴电子设备和无线通信技术的发展，利用无处不在的智能移动设备中的惯性传感器进行活动识别引起了广泛关注，并成为研究热点。在识别之前，传感器信号通常经过预处理和分割，然后根据它们提取和选择代表性特征。考虑到可穿戴设备资源有限和维数灾难的问题，生成最佳特征组合对于从特征子集到活动的后续映射最大化性能和效率至关重要。在本文中，我们提出了将蜜蜂群优化（BSO）与深度 Q 网络相结合来执行特征选择，并基于这两种方案提出了混合特征选择方法 BAROQUE。采用包装器方法，BAROQUE 利用 BSO 和多智能体深度 Q 网络（DQN）的吸引人的特性来确定特征子集，并采用分类器来评估这些解决方案。在 BAROQUE 中，BSO 用于在特征空间中平衡搜索的开发和探索，而 DQN 则利用强化学习的优点使局部搜索过程更具适应性和更高效。在一些由智能手机或智能手表收集的基准数据集上进行了广泛的实验，并将指标与 BSO、DQN 和其他一些已发表的方法进行了比较。结果表明，BAROQUE 在 UCI-HAR 数据集上的准确率达到 98.41%，并且比其他方法（如 CFS、SFFS 和 Relief-F）收敛到良好解决方案的时间更短，在准确性和效率方面取得了相当有希望的结果。
