论文标题
宽松衣服的活动识别概率模型
A Probabilistic Model of Activity Recognition with Loose Clothing
论文作者
论文摘要
随着车上可穿戴感应技术的发展，人类活动的认可已成为一个有吸引力的研究领域。借助舒适的电子质地，传感器可以嵌入衣服中，以便可以长期记录人类运动。但是，一个长期存在的问题是如何处理通过相对于身体运动引入的运动伪像。出乎意料的是，最近的经验发现表明，与刚性连接的传感器相比，与固定的传感器相比，布置的传感器实际上可以实现更高的活动识别精度，尤其是在从短时间窗口中进行预测时。在这项工作中，引入了概率模型，其中通过织物传感记录的运动之间的统计距离增加了这种提高的准确性和呼吸。模型的预测在模拟和真实的人类运动捕获实验中得到了验证，在这种实验中，很明显这种违反效应是紧密捕获的。
Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. With comfortable electronic-textiles, sensors can be embedded into clothing so that it is possible to record human movement outside the laboratory for long periods. However, a long-standing issue is how to deal with motion artefacts introduced by movement of clothing with respect to the body. Surprisingly, recent empirical findings suggest that cloth-attached sensor can actually achieve higher accuracy of activity recognition than rigid-attached sensor, particularly when predicting from short time-windows. In this work, a probabilistic model is introduced in which this improved accuracy and resposiveness is explained by the increased statistical distance between movements recorded via fabric sensing. The predictions of the model are verified in simulated and real human motion capture experiments, where it is evident that this counterintuitive effect is closely captured.
