Although walking along curved paths is common in daily life, most gait studies have focused on walking along straight paths. Thus, this study thoroughly investigated the use of a convolutional neural network (CNN) for gait phase (GP) estimation during curved walking. First, the thigh angles were acquired in three-dimensional space. Subsequently, several combinations of the angles were used as the input sets of the CNN. Second, three CNN models (i.e., no-fusion, late fusion, and early fusion) were created, differing in how left and right thigh angles were integrated. When sufficient computational resources were available (e.g., workstation), the early-fusion model using all angular components achieved the highest prediction accuracy. In contrast, under computational constraints (e.g., microcontroller units), the early-fusion model with inputs limited to sagittal and transverse angles provided the best trade-off between accuracy and inference speed. Moreover, this study revealed that GP prediction accuracy strongly depends on the curvature of the path. The results of this study can be used to design a CNN model for walking along curved paths for various applications such as clinical studies, rehabilitation, and wearable robots.


* Funding
- The National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-02214162)
- The Institute of Civil-Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade,
    Industry and Energy of Korean government under grant (No. 9991008623, 23-SF-RO-10)
- The Chung-Ang University Graduate Research Scholarship in 2024. (Corresponding authors: G. Lee and W. Nam).