ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors
ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors
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DOI：
10.1016/j.autcon.2021.103653
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发表时间：
2021-03-27
影响因子：
10.3
通讯作者：
Aghazadeh, Fereydoun
中科院分区：
文献类型：
--
作者：
Bangaru, Srikanth Sagar;Wang, Chao;Aghazadeh, Fereydoun
建筑工人的活动识别对工人绩效和安全评估至关重要。随着可穿戴传感技术的发展，许多研究人员开发了基于运动学传感器的工人活动识别方法，具有较高的精度。然而，先前研究的局限性仍然存在于使用智能手机进行实际实施的挑战，分类活动较少，识别的动作和身体部位有限。本研究提出一种基于人工神经网络的建筑工人自动化活动识别方法，可以识别复杂的建筑活动。提出的方法讨论了数据采集、数据融合和人工神经网络（ANN）模型的开发。通过对脚手架建造者活动的案例研究，验证了所提出方法的可行性，并与其他现有方法进行了比较。结果表明，该模型可以识别15种脚手架建造者的活动，准确率为94%，加权精度、召回率和F1分数为0.94。
Construction worker activity recognition is essential for worker performance and safety assessment. With the development of wearable sensing technologies, many researchers developed kinematic sensor-based worker activity recognition methods with considerable accuracy. However, the limitations of the previous studies remain at the challenge of using smartphones for practical implementation, fewer classified activities, and limited recognized motions and body parts. This study proposes an ANN-based automated construction worker activity recognition method that can recognize complex construction activities. The proposed methodology discusses data acquisition, data fusion, and artificial neural network (ANN) model development. A case study of scaffold builder activities was investigated to validate the proposed methodology's feasibility and evaluate its performance compared to other existing methods. The results show that the proposed model can recognize fifteen scaffold builder activities with an accuracy of 94% with 0.94 weighted precision, recall, and F1 Score.