Bashar Syed K, Al Fahim Abdullah, Chon Ki H
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5888-5891. doi: 10.1109/EMBC44109.2020.9176239.
For the past few years, smartphone based human activity recognition (HAR) has gained much popularity due to its embedded sensors which have found various applications in healthcare, surveillance, human-device interaction, pattern recognition etc. In this paper, we propose a neural network model to classify human activities, which uses activity-driven hand-crafted features. First, the neighborhood component analysis derived feature selection is used to choose a subset of important features from the available time and frequency domain parameters. Next, a dense neural network consisting of four hidden layers is modeled to classify the input features into different categories. The model is evaluated on publicly available UCI HAR data set consisting of six daily activities; our approach achieved 95.79% classification accuracy. When compared with existing state-of-the-art methods, our proposed model outperformed most other methods while using fewer features, thus showing the importance of proper feature selection.
在过去几年中，基于智能手机的人类活动识别（HAR）因其嵌入式传感器而广受欢迎，这些传感器在医疗保健、监控、人机交互、模式识别等领域有各种应用。在本文中，我们提出了一种神经网络模型来对人类活动进行分类，该模型使用活动驱动的手工特征。首先，使用邻域成分分析衍生的特征选择从可用的时域和频域参数中选择重要特征的子集。接下来，构建一个由四个隐藏层组成的密集神经网络，将输入特征分类到不同类别。该模型在由六种日常活动组成的公开可用的UCI HAR数据集上进行评估；我们的方法实现了95.79%的分类准确率。与现有的最先进方法相比，我们提出的模型在使用更少特征的情况下优于大多数其他方法，从而显示了正确特征选择的重要性。
