Abstract Construction tasks involve various activities composed of one or more body motions. As construction projects are labor-intensive and heavily rely on manual tasks, understanding the ever-changing behavior and activities is essential to manage construction workers effectively regarding their safety and productivity. While several research efforts have shown promising results in automated motion and activity recognition of the workers using motion sensors, there is still a lack of understanding about how motion sensors' numbers and their locations affect the performance of the recognition, which can contribute to improving the recognition performance and reducing the implementation cost. Moreover, further research is necessary to seek the motion recognition model that accurately identifies various motions using motion sensors attached to the workers' bodies. This study proposes a construction worker's motion recognition model using the Long Short-Term Memory (LSTM) network based on an evaluation of the effectiveness of motion sensors' numbers and locations to maximize motion recognition performance. The evaluation is conducted by generating different datasets containing motion sensor data collected from the sensors located on different body parts. Comparing the performance of five machine learning models trained using the datasets, the desired numbers and locations of motion sensors are identified. The quasi-experimental test with multiple subjects is conducted to validate the findings of the evaluation. Based on the findings, the LSTM network for recognizing construction workers' motions is developed. The LSTM network classifies various motions of the workers that can be utilized as primitive elements for monitoring the workers regarding their safety and productivity.

中文翻译：

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用于基于深度学习的工人运动识别的有效惯性传感器数量和身体上的位置

摘要 施工任务涉及由一种或多种身体运动组成的各种活动。由于建筑项目是劳动密集型的，并且严重依赖手工任务，因此了解不断变化的行为和活动对于有效管理建筑工人的安全和生产力至关重要。虽然一些研究工作已经在使用运动传感器对工人进行自动运动和活动识别方面显示出有希望的结果，但仍然缺乏对运动传感器的数量及其位置如何影响识别性能的了解，这有助于提高识别性能。识别性能并降低实施成本。而且，需要进一步研究以寻求使用连接到工人身体的运动传感器准确识别各种运动的运动识别模型。本研究基于对运动传感器数量和位置的有效性评估，提出了使用长短期记忆 (LSTM) 网络的建筑工人运动识别模型，以最大限度地提高运动识别性能。通过生成包含从位于不同身体部位的传感器收集的运动传感器数据的不同数据集来进行评估。比较使用数据集训练的五个机器学习模型的性能，确定了所需的运动传感器数量和位置。对多个受试者进行准实验测试以验证评估结果。根据调查结果，开发了用于识别建筑工人动作的 LSTM 网络。LSTM 网络对工人的各种动作进行分类，这些动作可用作监控工人安全和生产力的原始元素。