  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.
