Computer Science Department, Universidad Carlos III de Madrid, 28911 Leganes, Spain.
Sensors (Basel). 2018 Apr 23;18(4):1288. doi: 10.3390/s18041288.
Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.
人类活动识别是面向上下文感知系统和应用的一项具有挑战性的任务。由于不同传感器源、可穿戴智能对象、环境传感器等的普及，人们对其越来越感兴趣。这项任务通常被视为监督机器学习问题，即给定一些输入数据（例如从不同传感器检索到的信号）来预测标签。为了解决传感器网络环境中的人类活动识别问题，本文提出使用深度学习（卷积神经网络）来使用公开可用的 OPPORTUNITY 数据集执行活动识别。我们将让进化算法设计最佳拓扑结构，而不是手动选择合适的拓扑结构，以最大化分类 F1 分数。之后，我们还将探索进化过程中产生的模型的委员会的性能。结果分析表明，所提出的模型能够在异构传感器网络环境中执行活动识别，在使用新传感器数据进行测试时，它能够实现非常高的准确性。基于所有进行的实验，所提出的神经进化系统已被证明能够系统地找到能够超越最先进技术中报告的先前结果的分类模型，表明这种方法是有用的，并改进了以前手动设计的架构。
