Manufacturing industries strive to improve production efficiency and product
quality by deploying advanced sensing and control systems. Wearable sensors are
emerging as a promising solution for achieving this goal, as they can provide
continuous and unobtrusive monitoring of workers' activities in the
manufacturing line. This paper presents a novel wearable sensing prototype that
combines IMU and body capacitance sensing modules to recognize worker
activities in the manufacturing line. To handle these multimodal sensor data,
we propose and compare early, and late sensor data fusion approaches for
multi-channel time-series convolutional neural networks and deep convolutional
LSTM. We evaluate the proposed hardware and neural network model by collecting
and annotating sensor data using the proposed sensing prototype and Apple
Watches in the testbed of the manufacturing line. Experimental results
demonstrate that our proposed methods achieve superior performance compared to
the baseline methods, indicating the potential of the proposed approach for
real-world applications in manufacturing industries. Furthermore, the proposed
sensing prototype with a body capacitive sensor and feature fusion method
improves by 6.35%, yielding a 9.38% higher macro F1 score than the proposed
sensing prototype without a body capacitive sensor and Apple Watch data,
respectively.

本文介绍了一种新颖的可穿戴传感原型，将 IMU 和体电容传感模块结合起来，用于识别制造业中工人的行为。我们提出并比较了早期和晚期传感器数据融合方法，针对多通道时间序列卷积神经网络和深度卷积 LSTM 模型。通过使用我们提出的传感原型和苹果手表在制造流水线的测试平台上收集和注释传感器数据，我们评估了所提出的硬件和神经网络模型。实验结果表明，我们提出的方法相对于基线方法具有更好的性能，表明所提出的方法在制造业的实际应用中具有潜力。此外，配备有体电容传感器和特征融合方法的传感原型相比于没有体电容传感器和苹果手表数据的传感原型分别提高了 6.35%，宏 F1 评分较高 9.38%。