Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human-computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning--based HAR.

翻译：移动和可磨损装置使许多应用得以应用,包括活动跟踪、健康监测和人-计算机互动,以衡量和改善我们的日常生活,其中许多应用是通过利用许多移动和可磨损装置中的大量低功率传感器来进行人类活动的确认(HAR)而得以实现的。最近,深层次的学习大大拉动了HAR在移动和可磨损装置上的界限。本文系统地分类和总结了现有工作,这些工作引进了基于磨损的发光深度学习方法,并对目前的进展、发展趋势和重大挑战进行了全面分析。我们还提出了深层学习发光装置的尖端前沿和未来方向。