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.

翻译：制造行业通过部署先进的传感与控制系统，致力于提升生产效率和产品质量。可穿戴传感器作为一种有前景的解决方案，能够对生产线上工人的活动进行连续且无干扰的监测。本文提出了一种新型可穿戴感知原型，该原型结合了惯性测量单元（IMU）与人体电容传感模块，用于识别生产线上的工人活动。为处理这些多模态传感器数据，我们提出并比较了针对多通道时间序列卷积神经网络和深度卷积长短期记忆网络的早期与晚期传感器数据融合方法。我们通过在制造生产线测试平台中使用所提出的感知原型和苹果手表（Apple Watch）采集并标注传感器数据，对所提出的硬件与神经网络模型进行了评估。实验结果表明，相较于基线方法，我们提出的方法取得了更优的性能，显示出该方法在制造行业实际应用中的潜力。此外，集成了人体电容传感器与特征融合方法所提出的感知原型，其性能提升了6.35%，相比未使用人体电容传感器的原型和苹果手表数据，宏F1分数分别提高了9.38%。