Evolving consumer demands and market trends have led to businesses increasingly embracing a production approach that prioritizes flexibility and customization. Consequently, factory workers must engage in tasks that are more complex than before. Thus, productivity depends on each worker's skills in assembling products. Therefore, analyzing the behavior of a worker is crucial for work improvement. However, manual analysis is time consuming and does not provide quick and accurate feedback. Machine learning have been attempted to automate the analyses; however, most of these methods need several labels for training. To this end, we extend the Gaussian process hidden semi-Markov model (GP-HSMM), to enable the rapid and automated analysis of worker behavior without pre-training. The model does not require labeled data and can automatically and accurately segment continuous motions into motion classes. The proposed model is a probabilistic model that hierarchically connects GP-HSMM and HSMM, enabling the extraction of behavioral patterns with different granularities. Furthermore, it mutually infers the parameters between the GP-HSMM and HSMM, resulting in accurate motion pattern extraction. We applied the proposed method to motion data in which workers assembled products at an actual production site. The accuracy of behavior pattern extraction was evaluated using normalized Levenshtein distance (NLD). The smaller the value of NLD, the more accurate is the pattern extraction. The NLD of motion patterns captured by GP-HSMM and HSMM layers in our proposed method was 0.50 and 0.33, respectively, which are the smallest compared to that of the baseline methods.

翻译：不断变化的消费者需求和市场趋势促使企业日益倾向于采用强调灵活性与定制化的生产方式。因此，工厂工人必须从事比以往更复杂的任务。由此，生产力取决于每位工人的产品组装技能。分析工人行为对于工作改进至关重要。然而，人工分析耗时且无法提供快速准确的反馈。已有研究尝试利用机器学习实现分析的自动化，但大多数方法需要大量标注数据进行训练。为此，我们扩展了高斯过程隐半马尔可夫模型（GP-HSMM），使其能够无需预训练快速自动分析工人行为。该模型无需标注数据，即可自动将连续运动精确分割为运动类别。所提模型是一个将GP-HSMM与HSMM进行分层连接的概率模型，能够提取不同粒度的行为模式。此外，它通过GP-HSMM与HSMM之间的参数互推断实现精确的运动模式提取。我们将所提方法应用于实际生产现场工人组装产品的运动数据。采用归一化莱文斯坦距离（NLD）评估行为模式提取的准确性。NLD值越小，模式提取越精确。所提方法中GP-HSMM层和HSMM层提取的运动模式NLD分别为0.50和0.33，这是与基线方法相比的最小值。