CiTIUS (Centro Singular de Investigación en Tecnoloxías Intelixentes), Universidade de Santiago deCompostela, Santiago de Compostela 15782, Spain.
Situm Technologies S.L., Santiago de Compostela 15782, Spain.
Sensors (Basel). 2020 Feb 21;20(4):1189. doi: 10.3390/s20041189.
Presently, smartphones are used more and more for purposes that have nothing to do withphone calls or simple data transfers. One example is the recognition of human activity, which isrelevant information for many applications in the domains of medical diagnosis, elderly assistance,indoor localization, and navigation. The information captured by the inertial sensors of the phone(accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performedby the person who is carrying the device, in particular in the activity of walking. Nevertheless,the development of a standalone application able to detect the walking activity starting only fromthe data provided by these inertial sensors is a complex task. This complexity lies in the hardwaredisparity, noise on data, and mostly the many movements that the smartphone can experience andwhich have nothing to do with the physical displacement of the owner. In this work, we exploreand compare several approaches for identifying the walking activity. We categorize them into twomain groups: the first one uses features extracted from the inertial data, whereas the second oneanalyzes the characteristic shape of the time series made up of the sensors readings. Due to the lackof public datasets of inertial data from smartphones for the recognition of human activity underno constraints, we collected data from 77 different people who were not connected to this research.Using this dataset, which we published online, we performed an extensive experimental validationand comparison of our proposals.
目前，智能手机的用途越来越广泛，不仅限于通话或简单的数据传输。例如，识别人类活动就是一个例子，这对于医疗诊断、老年人辅助、室内定位和导航等领域的许多应用来说都是相关信息。手机惯性传感器（加速度计、陀螺仪和磁力计）捕捉到的信息可以进行分析，以确定携带设备的人所进行的活动，特别是行走活动。然而，仅从这些惯性传感器提供的数据出发，开发一个能够检测行走活动的独立应用程序是一项复杂的任务。这种复杂性在于硬件差异、数据噪声，以及智能手机可能经历的许多与所有者的物理位移无关的运动。在这项工作中，我们探索并比较了几种识别行走活动的方法。我们将它们分为两类：第一类使用从惯性数据中提取的特征，而第二类则分析由传感器读数组成的时间序列的特征形状。由于缺乏用于在无约束条件下识别人类活动的智能手机惯性数据的公共数据集，我们从 77 位与该研究无关的不同人员那里收集了数据。我们使用这个数据集在网上发布，并对我们的提案进行了广泛的实验验证和比较。
