Multistream Temporal Convolutional Network for Correct/Incorrect Patient Transfer Action Detection Using Body Sensor Network
Multistream Temporal Convolutional Network for Correct/Incorrect Patient Transfer Action Detection Using Body Sensor Network
复制标题
使用身体传感器网络进行正确/不正确的患者转移动作检测的多流时间卷积网络
DOI：
10.1109/jiot.2021.3075477
复制
发表时间：
2021
影响因子：
10.6
通讯作者：
Ota Jun
中科院分区：
文献类型：
--
作者：
Zhong Zhihang;Lin Chingszu;Kanai-Pak Masako;Maeda Jukai;Kitajima Yasuko;Nakamura Mitsuhiro;Kuwahara Noriaki;Ogata Taiki;Ota Jun
具有丰富多模态信号的身体传感器网络（BSNs）的发展使高精度的细粒度动作检测成为可能，这是许多人机交互应用的基石。然而，在连续细粒度动作的情况下，大多数现有的基于可穿戴传感器的检测方法由于其有限的时间感受场而受到滑动窗口的限制，现有的序列对序列检测方法无法有效利用可穿戴传感器的多模态信息潜力。在此，为了让多模态信号在细粒度动作检测中充分发挥作用，我们通过设计一个基于通道注意力的多流结构，提出了一种新的时间卷积网络。我们将其应用于正确和错误的病人转移护理动作检测中。当护士进行病人转移时，从病人的BSN收集数据集。在我们的数据集和公共数据集（C-MHAD）上进行的大量实验表明，所提出的方法优于最先进的方法，因为它可以在每个时间框架内加强对更令人信服的模态流预测特征的利用。源代码可从https://github.com/zzh-tech/Continuous-Action-Detection获得。
The development of body sensor networks (BSNs) with rich multimodal signals has enabled highly accurate fine-grained action detection, which is the cornerstone of many human–computer interaction applications. However, in the case of consecutive fine-grained actions, most existing wearable sensor-based detection methods are constrained by sliding windows because of their limited temporal receptive fields, and existing sequence-to-sequence detection methods cannot effectively leverage the potential of multimodal information of wearable sensors. Herein, to give multimodal signals full play in fine-grained action detection, we propose a novel temporal convolutional network by designing a channel attention-based multistream structure. We apply it to a promising application for correct and incorrect patient transfer nursing action detection. A data set is collected from a BSN on a patient when nurses perform patient transfer. Extensive experiments on our data set and public data set (C-MHAD) demonstrate that the proposed method is superior to the state-of-the-art methods, because it can strengthen the utilization of prediction features from the more convincing modal stream at each time frame. The source code is available at https://github.com/zzh-tech/Continuous-Action-Detection.