School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China.
School of Engineering, Merz Court, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
Sensors (Basel). 2019 Aug 8;19(16):3468. doi: 10.3390/s19163468.
Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap sampling method is utilized to independently train multiple base ELMs which make up the initial base classifier pool. Secondly, the initial pool is pre-pruned by calculating the pairwise diversity measure of each base ELM, which can eliminate similar base ELMs and enhance the performance of HAR system by balancing diversity and accuracy. Then, glowworm swarm optimization (GSO) is utilized to search for the optimal sub-ensemble from the base ELMs after pre-pruning. Finally, majority voting is utilized to combine the results of the selected base ELMs. For the evaluation of our proposed method, we collected a dataset from different locations on the body, including chest, waist, left wrist, left ankle and right arm. The experimental results show that, compared with traditional ensemble algorithms such as Bagging, Adaboost, and other state-of-the-art pruning algorithms, the proposed approach is able to achieve better performance (96.7% accuracy and F1 from wrist) with fewer base classifiers.
基于传感器的人体活动识别（HAR）在学术和应用领域都引起了关注，可用于与健康相关的领域、健身、运动训练等。为了提高基于传感器的 HAR 性能，并优化集成系统的基分类器的泛化能力和多样性，本文提出了一种新的 HAR 方法（基于成对差异度量和萤火虫群优化的选择性集成学习，DMGSOSEN），该方法利用具有差异化极限学习机（ELM）的集成学习。首先，利用引导抽样方法独立训练多个构成初始基分类器池的基 ELM。其次，通过计算每个基 ELM 的成对差异度量来预修剪初始池，这可以消除相似的基 ELM，并通过平衡多样性和准确性来提高 HAR 系统的性能。然后，利用萤火虫群优化（GSO）从预修剪后的基 ELM 中搜索最优子集成。最后，利用多数投票法结合所选基 ELM 的结果。为了评估我们提出的方法，我们从身体的不同部位收集了一个数据集，包括胸部、腰部、左手腕、左脚踝和右臂。实验结果表明，与传统的集成算法（如 Bagging、Adaboost 等）和其他先进的剪枝算法相比，所提出的方法能够使用更少的基分类器实现更好的性能（腕部的准确率和 F1 分别为 96.7%）。
