Effective inertial sensor quantity and locations on a body for deep learning-based worker's motion recognition
Effective inertial sensor quantity and locations on a body for deep learning-based worker's motion recognition
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DOI：
10.1016/j.autcon.2020.103126
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发表时间：
2020-05-01
影响因子：
10.3
通讯作者：
Cho, Yong K.
中科院分区：
文献类型：
--
作者：
Kim, Kinam;Cho, Yong K.
构建任务涉及由一个或多个身体运动组成的各种活动。由于建筑项目是劳动密集型的，并且严重依赖手工作业，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的工人的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理解，这有助于提高识别性能并降低实现成本。此外，还需要进一步的研究来寻求使用附着在工人身体上的运动传感器来准确地识别各种运动的运动识别模型。这项研究提出了一种使用长短期记忆（LSTM）网络的建筑工人运动识别模型，该模型基于对运动传感器数量和位置的有效性的评估，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上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。通过比较使用数据集训练的五个机器学习模型的性能，确定了运动传感器的所需数量和位置。多个科目的准实验测试进行验证的评价结果。基于这些发现，开发了用于识别建筑工人运动的LSTM网络。LSTM网络对工人的各种运动进行分类，这些运动可以用作监控工人安全和生产力的基本元素。
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.