Evaluation of Machine Learning Algorithms for Worker’s Motion Recognition Using Motion Sensors
Evaluation of Machine Learning Algorithms for Worker’s Motion Recognition Using Motion Sensors
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DOI：
10.1061/9780784482438.007
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Kinam Kim;Jingdao Chen;Y. Cho
中科院分区：
文献类型：
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作者：
Kinam Kim;Jingdao Chen;Y. Cho
建构任务包括由一个或多个身体动作组成的各种活动。了解建筑工人动态变化的行为和状态对于有效地管理建筑工人的安全和生产力至关重要。虽然一些研究已经在活动识别方面取得了可喜的成果，但为了提高性能和降低实施成本，通过分析识别结果，确定运动传感器在工人身体上的最佳位置，还需要进一步的研究。本研究提出了一个基于仿真的评估，多个运动传感器连接到工人执行典型的施工任务。一组17个惯性测量单元（IMU）传感器用于收集整个身体的运动传感器数据。通过模拟具有不同传感器组合和特征的几种场景，利用多种机器学习算法对工人的运动进行分类。通过仿真，对放置在人体不同位置的每个IMU传感器进行测试，以评估其对工人不同活动类型的识别精度。然后，根据活动识别性能衡量传感器位置的有效性，以确定每个位置的相对优势。在此基础上，可以减少所需的传感器数量，保持识别性能。本研究的结果有助于使用简单的运动传感器进行活动识别的实际实施，以提高个体工人的安全和生产力。
Construction tasks involve various activities composed of one or more body motions. It is essential to understand the dynamically changing behavior and state of construction workers to manage construction workers effectively with regards to their safety and productivity. While several research efforts have shown promising results in activity recognition, further research is still necessary to identify the best locations of motion sensors on a worker’s body by analyzing the recognition results for improving the performance and reducing the implementation cost. This study proposes a simulation-based evaluation of multiple motion sensors attached to workers performing typical construction tasks. A set of 17 inertial measurement unit (IMU) sensors is utilized to collect motion sensor data from an entire body. Multiple machine learning algorithms are utilized to classify the motions of the workers by simulating several scenarios with different combinations and features of the sensors. Through the simulations, each IMU sensor placed in different locations of a body is tested to evaluate its recognition accuracy toward the worker’s different activity types. Then, the effectiveness of sensor locations is measured regarding activity recognition performance to determine relative advantage of each location. Based on the results, the required number of sensors can be reduced maintaining the recognition performance. The findings of this study can contribute to the practical implementation of activity recognition using simple motion sensors to enhance the safety and productivity of individual workers.