Smartphones are a promising platform for continuous monitoring of human behavior. However, the ability to capture people’s behavioral patterns in-the-wild is a challenge, as the user’s behavior and physical activities can vary, given the variability of settings and environments. Modeling and understanding of human activity in-the-wild must not overlook a user’s behavioral context, which is just as crucial as recognizing the range of physical activities. The work in this paper presents a novel framework for context-aware human activity recognition by incorporating human behavioral contexts with physical activities. The proposed framework utilizes a series of machine learning classifiers to validate the efficiency of the proposed method.

中文翻译：

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使用智能手机加速度计的野外环境感知人类活动识别 (CAHAR)

智能手机是一个很有前途的平台，可以持续监控人类行为。然而，捕捉人们野外行为模式的能力是一项挑战，因为考虑到环境和环境的可变性，用户的行为和身体活动可能会有所不同。对野外人类活动的建模和理解不能忽视用户的行为背景，这与识别身体活动范围一样重要。本文的工作通过将人类行为环境与身体活动相结合，提出了一种用于环境感知人类活动识别的新框架。所提出的框架利用一系列机器学习分类器来验证所提出方法的效率。