Acceleration-based Activity Recognition of Repetitive Works with Lightweight Ordered-work Segmentation Network
Acceleration-based Activity Recognition of Repetitive Works with Lightweight Ordered-work Segmentation Network
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DOI：
10.1145/3534572
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发表时间：
2022-07
影响因子：
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通讯作者：
Naoya Yoshimura;T. Maekawa;Takahiro Hara;Atsushi Wada
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文献类型：
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作者：
Naoya Yoshimura;T. Maekawa;Takahiro Hara;Atsushi Wada
本研究提出一种新的神经网络模型，用于识别在工业环境中使用身体佩戴的加速度计的手工作品，命名为轻量级有序工作分割网络（LOS-Net）。在工业领域中，人类工作者通常重复地执行一组预定义的过程，每个过程由预定义顺序的一系列活动组成。最先进的活动识别模型，如编码器-解码器模型，具有许多可训练的参数，使得它们在工业领域中的训练很困难，因为随之而来的准备大量标记数据的巨大成本。相比之下，LOS-Net被设计为在有限数量的训练数据上进行训练。具体而言，LOS-Net中的解码器具有很少的可训练参数，并且被设计为仅捕获精确识别有序作品所需的信息。这些是（i）连续活动之间的边界信息，因为所执行的活动中的转变通常与在手动工作期间收集的传感器数据的趋势变化相关联，以及（ii）关于有序工作的长期上下文，例如，关于上一个和下一个活动的信息，这对于识别当前活动很有用。该信息是通过引入一个模块来获得的，该模块可以使用很少的可训练参数在遥远的时间步长收集该信息。此外，LOS-Net可以通过结合关于活动顺序的先验知识来细化解码器的活动估计。我们使用从实际工厂和物流中心的工人收集的传感器数据证明了LOS-Net的有效性，并表明它可以实现最先进的性能。
This study presents a new neural network model for recognizing manual works using body-worn accelerometers in industrial settings, named Lightweight Ordered-work Segmentation Network (LOS-Net). In industrial domains, a human worker typically repetitively performs a set of predefined processes, with each process consisting of a sequence of activities in a predefined order. State-of-the-art activity recognition models, such as encoder-decoder models, have numerous trainable parameters, making their training difficult in industrial domains because of the consequent substantial cost for preparing a large amount of labeled data. In contrast, the LOS-Net is designed to be trained on a limited amount of training data. Specifically, the decoder in the LOS-Net has few trainable parameters and is designed to capture only the necessary information for precise recognition of ordered works. These are (i) the boundary information between consecutive activities, because a transition in the performed activities is generally associated with the trend change of the sensor data collected during the manual works and (ii) long-term context regarding the ordered works, e.g., information about the previous and next activity, which is useful for recognizing the current activity. This information is obtained by introducing a module that can collect it at distant time steps using few trainable parameters. Moreover, the LOS-Net can refine the activity estimation by the decoder by incorporating prior knowledge regarding the order of activities. We demonstrate the effectiveness of the LOS-Net using sensor data collected from workers in actual factories and a logistics center, and show that it can achieve state-of-the-art performance.