The recognition of human activities is a topic of great relevance due to its wide range of applications. Different approaches have been proposed to recognize human activities, ranging from the comparison of signals with thresholds to the application of deep and machine learning techniques. In this work, the classification of six human activities (walking, walking downstairs, walking upstairs, standing, sitting, and lying down) is performed using bidirectional LSTM networks that exploit intrinsic mode function (IMF) representation of inertial signals. Records with inertial signals (accelerometer and gyroscope) of 2.56 s, available at the UCI Machine Learning Repository, were collected from 30 subjects using a smartphone. First, inertial signals were standardized to take them to the same scale and were decomposed into IMF using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). IMF were then segmented (split) into nine segments of 1.28 s with 12.5% overlap and introduced to a first network with four outputs to identify the dynamic activities and the statics as a single class called “statics”, giving 98.86% accuracy. Then, the non-segmented IMF of the records assigned to the statics class were introduced to a second network to classify their three activities, giving an accuracy of 88.46%. In total, 92.91% accuracy was obtained to classify the six human activities. This performance is because ICEEMDAN allowed the extraction of information that was embedded in the signal, and the segmentation of the IMF allowed the network to discriminate between static and dynamic activities.

人类活动的认可由于其广泛的应用而成为一个具有重大意义的主题。已经提出了各种不同的方法来识别人类活动，范围从信号与阈值的比较到深度学习和机器学习技术的应用。在这项工作中，使用双向LSTM网络对六种人类活动（步行，楼下行走，楼上行走，站立，坐着和躺下）的分类进行了分类，该双向LSTM网络利用惯性信号的固有模式功能（IMF）表示。从UCI机器学习存储库中获得的惯性信号（加速度计和陀螺仪）为2.56 s的记录是使用智能手机从30名受试者中收集的。第一，惯性信号被标准化以使其具有相同的比例，并使用具有自适应噪声的改进的完整整体经验模式分解（ICEEMDAN）将其分解为IMF。然后，将IMF细分（拆分）为1.28 s的九个部分，重叠率为12.5％，并引入具有四个输出的第一个网络，以将动态活动和静态识别为一个单独的类，称为“静态”，则可提供98.86％的准确性。然后，将分配给静态类的记录的未分段的IMF引入第二个网络，以对它们的三个活动进行分类，准确度为88.46％。总计，对六种人类活动进行分类的准确度为92.91％。之所以如此，是因为ICEEMDAN允许提取嵌入在信号中的信息，而IMF的分段允许网络区分静态和动态活动。