In this study, we investigate a new self-supervised learning (SSL) approach for complex work activity recognition using wearable sensors. Owing to the cost of labeled sensor data collection, SSL methods for human activity recognition (HAR) that effectively use unlabeled data for pretraining have attracted attention. However, applying prior SSL to complex work activities such as packaging works is challenging because the observed data vary considerably depending on situations such as the number of items to pack and the size of the items in the case of packaging works. In this study, we focus on sensor data corresponding to characteristic and necessary actions (sensor data motifs) in a specific activity such as a stretching packing tape action in an assembling a box activity, and \textcolor{black}{try} to train a neural network in self-supervised learning so that it identifies occurrences of the characteristic actions, i.e., Motif Identification Learning (MoIL). The feature extractor in the network is used in the downstream task, i.e., work activity recognition, enabling precise activity recognition containing characteristic actions with limited labeled training data. The MoIL approach was evaluated on real-world work activity data and it achieved state-of-the-art performance under limited training labels.

翻译：本研究探索了一种基于可穿戴传感器的复杂工作活动识别的新型自监督学习方法。由于标记传感器数据采集成本高昂，有效利用未标记数据进行预训练的人体活动识别自监督学习方法备受关注。然而，将现有自监督学习方法应用于包装作业等复杂工作活动面临挑战，因为观察到的数据会根据情境（如包装物品数量及尺寸）产生显著差异。本研究聚焦于特定活动中具有特征性和必要性的动作对应的传感器数据（传感器数据基序），例如组装纸箱活动中拉伸包装胶带的动作，并尝试通过自监督学习训练神经网络识别这些特征动作的发生，即基序识别学习。该网络中的特征提取器用于下游任务（工作活动识别），从而在有限标注训练数据下实现包含特征动作的精准活动识别。基于真实工作活动数据的评估表明，MoIL方法在训练标签有限的情况下取得了最先进的性能。