Universidade Federal do Amazonas, Manaus 69080-900, Brazil.
University of Ontario Institute of Technology, Oshawa ON L1H 7K4, Canada.
Sensors (Basel). 2019 Jul 21;19(14):3213. doi: 10.3390/s19143213.
The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people's lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users' physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.
智能手机的普及和计算资源的发展，如连接、处理、便携性和感知能力，极大地改变了人们的生活。如今，许多智能手机都内置了各种强大的传感器，包括运动、位置、网络和方向传感器。运动或惯性传感器（例如加速度计）已被广泛用于识别用户的身体活动。这为智能手机中惯性传感器的许多不同的有趣应用开辟了道路，例如在健康和交通领域。在这篇观点文章中，我们全面地回顾了智能手机中惯性传感器的人类活动识别（HAR）解决方案的现状。本文首先讨论了人类活动的概念以及完整的历史事件，重点关注智能手机，展示了过去二十年该领域的发展演变。接下来，我们详细描述了 HAR 方法学，重点介绍了在惯性传感器背景下 HAR 解决方案的步骤。对于每个步骤，我们都引用了主要参考文献，其中采用了科学界建议的最佳实现实践。最后，我们从智能手机中嵌入的惯性传感器的角度介绍了 HAR 解决方案的主要结果。
