A Probabilistic Model of Human Activity Recognition with Loose Clothing.
A Probabilistic Model of Human Activity Recognition with Loose Clothing.
复制标题
人类活动识别的概率模型，穿着宽松的衣服。
DOI：
10.3390/s23104669
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发表时间：
2023-05-11
期刊：
影响因子：
--
通讯作者：
Howard M
中科院分区：
文献类型：
--
作者：
Shen T;Di Giulio I;Howard M
随着人体可穿戴传感技术的发展，人体活动识别已成为一个引人注目的研究领域。基于纺织品的传感器最近已被用于活动识别。借助最新的电子纺织技术，传感器可以集成到服装中，让用户可以舒适地享受长期人体运动记录。然而，最近的经验发现令人惊讶地表明，与刚性连接的传感器相比，挂在衣服上的传感器实际上可以实现更高的活动识别精度，特别是在从短时间窗口进行预测时。这项工作提供了一个概率模型，解释了通过增加记录的运动之间的统计距离来提高织物感知的响应性和准确性。当窗户尺寸为30时，舒适的贴布传感器的精度可以比刚性贴附的传感器提高更多。几个参与者的模拟和真实人体运动捕捉实验证实了模型的预测，表明准确地捕捉到了这种违反直觉的效应。
Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve higher activity recognition accuracy than rigid-attached sensors, particularly when predicting from short time windows. This work presents a probabilistic model that explains improved responsiveness and accuracy with fabric sensing from the increased statistical distance between movements recorded. The accuracy of the comfortable fabric-attached sensor can be increased by more than rigid-attached sensors when the window size is . Simulated and real human motion capture experiments with several participants confirm the model’s predictions, demonstrating that this counterintuitive effect is accurately captured.
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