论文标题
通过多传感器训练数据实现单传感器复杂活动识别
Achieving Single-Sensor Complex Activity Recognition from Multi-Sensor Training Data
论文作者
论文摘要
在这项研究中，我们提出了一种基于单个传感器的活动识别的方法，该方法训练了来自多个传感器的数据。毫无疑问，当我们使用足够质量的足够传感器时，复杂的活动识别系统的性能会提高，但是在现实生活中，出于各种原因，例如用户舒适性，隐私，电池保存和/或成本，使用这种丰富的传感器可能是不可行的。在许多情况下，只有一种智能手机之类的设备，并且通过单个传感器实现高精度是一项挑战，更适合复杂的活动。我们的方法将表示学习与功能映射结合在一起，以利用训练期间提供的多个传感器信息，同时在测试或实际使用过程中使用单个传感器。我们的结果表明，与培训相比，在新用户方案中使用相同的传感器数据时，提出的方法可以将复杂活动识别的F1得分提高到17％。
In this study, we propose a method for single sensor-based activity recognition, trained with data from multiple sensors. There is no doubt that the performance of complex activity recognition systems increases when we use enough sensors with sufficient quality, however using such rich sensors may not be feasible in real-life situations for various reasons such as user comfort, privacy, battery-preservation, and/or costs. In many cases, only one device such as a smartphone is available, and it is challenging to achieve high accuracy with a single sensor, more so for complex activities. Our method combines representation learning with feature mapping to leverage multiple sensor information made available during training while using a single sensor during testing or in real usage. Our results show that the proposed approach can improve the F1-score of the complex activity recognition by up to 17\% compared to that in training while utilizing the same sensor data in a new user scenario.
