Gunaratne Praveen Nuwantha, Tamura Hiroki
Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, 1-1 Gakuen Kibanadai-nishi, Miyazaki 889-2192, Japan.
Faculty of Engineering, University of Miyazaki, 1-1 Gakuen Kibanadai-nishi, Miyazaki 889-2192, Japan.
Sensors (Basel). 2025 Jun 5;25(11):3558. doi: 10.3390/s25113558.
As populations age, particularly in countries like Japan, mobility impairments related to ankle joint dysfunction, such as foot drop, instability, and reduced gait adaptability, have become a significant concern. Active ankle-foot orthoses (AAFO) offer targeted support during walking; however, most existing systems rely on rule-based or threshold-based control, which are often limited to sagittal plane movements and lacking adaptability to subject-specific gait variations. This study proposes an approach driven by neuromuscular activation using surface electromyography (EMG) and a Gated Recurrent Unit (GRU)-based deep learning model to predict plantar pressure distributions at the heel, midfoot, and toe regions during gait. EMG signals were collected from four key ankle muscles, and plantar pressures were recorded using a customized sandal-integrated force-sensitive resistor (FSR) system. The data underwent comprehensive preprocessing and segmentation using a sliding window method. Root mean square (RMS) values were extracted as the primary input feature due to their consistent performance in capturing muscle activation intensity. The GRU model successfully generalized across subjects, enabling the accurate real-time inference of critical gait events such as heel strike, mid-stance, and toe off. This biomechanical evaluation demonstrated strong signal compatibility, while also identifying individual variations in electromechanical delay (EMD). The proposed predictive framework offers a scalable and interpretable approach to improving real-time AAFO control by synchronizing assistance with user-specific gait dynamics.
随着人口老龄化，尤其是在日本这样的国家，与踝关节功能障碍相关的行动能力受损问题，如足下垂、不稳定和步态适应性降低，已成为一个重大关切。主动踝足矫形器（AAFO）在行走过程中提供有针对性的支撑；然而，现有的大多数系统依赖基于规则或基于阈值的控制，这些控制通常仅限于矢状面运动，并且缺乏对个体特定步态变化的适应性。本研究提出了一种由神经肌肉激活驱动的方法，使用表面肌电图（EMG）和基于门控循环单元（GRU）的深度学习模型来预测步态期间足跟、中足和脚趾区域的足底压力分布。从四块关键的踝关节肌肉收集EMG信号，并使用定制的集成凉鞋的力敏电阻（FSR）系统记录足底压力。数据使用滑动窗口方法进行了全面的预处理和分割。由于均方根（RMS）值在捕捉肌肉激活强度方面表现一致，因此被提取为主要输入特征。GRU模型成功地在不同受试者之间进行了泛化，能够准确实时推断足跟触地、站立中期和足趾离地等关键步态事件。这种生物力学评估显示出很强的信号兼容性，同时也识别出了机电延迟（EMD）的个体差异。所提出的预测框架提供了一种可扩展且可解释的方法，通过与用户特定的步态动力学同步辅助来改善实时AAFO控制。
