Abstract
A vast population of visually impaired individuals is currently facing intricate life challenges, particularly related to perceiving walking directions. Therefore, this paper proposes a novel deep learning method based on wearable sensors to address the problem of walking direction recognition. The information mining and fusion module, the multi-feature position information mining attention module, and the multi-feature content information mining attention module are proposed to comprehensively mine comprehensive information from walking data. To overcome the limitation of information gathered from a single type of sensor, this paper combines inertial sensors and pressure insoles for walking direction recognition. Experimental results demonstrate that compared to existing research methods, the proposed method in this paper achieves a higher recognition accuracy highlighting the superiority and effectiveness of this method.
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Data will be made available on request.
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Funding
This work was supported by National Natural Science Foundation of China under (Grant Nos., 61903170, 62173175, 61877033), and by the Natural Science Foundation of Shandong Province under grants Nos. ZR2019BF045, ZR2019MF021, ZR2019QF004, and by the Key Research and Development Project of Shandong Province of China, No.2019GGX101003.
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Guo, M., Qin, L., Yan, M. et al. Walking direction recognition based on deep learning with inertial sensors and pressure insoles. SIViP 19, 189 (2025). https://doi.org/10.1007/s11760-024-03753-6
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DOI: https://doi.org/10.1007/s11760-024-03753-6