Ma Haozhe, Li Chunguang, Zhu Yufei, Peng Yaoxing, Sun Lining
Key Laboratory of Robotics and System of Jiangsu, School of Mechanical and Electric Engineering, Soochow University, Suzhou, China.
Front Hum Neurosci. 2023 Jul 24;17:1205858. doi: 10.3389/fnhum.2023.1205858. eCollection 2023.
Accurate recognition of patients' movement intentions and real-time adjustments are crucial in rehabilitation exoskeleton robots. However, some patients are unable to utilize electromyography (EMG) signals for this purpose due to poor or missing signals in their lower limbs. In order to address this issue, we propose a novel method that fits gait parameters using cerebral blood oxygen signals. Two types of walking experiments were conducted to collect brain blood oxygen signals and gait parameters from volunteers. Time domain, frequency domain, and spatial domain features were extracted from brain hemoglobin. The AutoEncoder-Decoder method is used for feature dimension reduction. A regression model based on the long short-term memory (LSTM) model was established to fit the gait parameters and perform incremental learning for new individual data. Cross-validation was performed on the model to enhance individual adaptivity and reduce the need for individual pre-training. The coefficient of determination (R2) for the gait parameter fit was 71.544%, with a mean square error (RMSE) of less than 3.321%. Following adaptive enhancement, the coefficient of R2 increased by 6.985%, while the RMSE decreased by 0.303%. These preliminary results indicate the feasibility of fitting gait parameters using cerebral blood oxygen information. Our research offers a new perspective on assisted locomotion control for patients who lack effective myoelectricity, thereby expanding the clinical application of rehabilitation exoskeleton robots. This work establishes a foundation for promoting the application of Brain-Computer Interface (BCI) technology in the field of sports rehabilitation.
在康复外骨骼机器人中，准确识别患者的运动意图并进行实时调整至关重要。然而，一些患者由于下肢信号不佳或缺失，无法利用肌电图（EMG）信号来实现这一目的。为了解决这个问题，我们提出了一种利用脑血氧信号拟合步态参数的新方法。进行了两种类型的步行实验，以收集志愿者的脑血氧信号和步态参数。从脑血红蛋白中提取时域、频域和空间域特征。使用自动编码器 - 解码器方法进行特征降维。建立了基于长短期记忆（LSTM）模型的回归模型，以拟合步态参数并对新的个体数据进行增量学习。对模型进行交叉验证，以增强个体适应性并减少个体预训练的需求。步态参数拟合的决定系数（R2）为71.544%，均方误差（RMSE）小于3.321%。经过自适应增强后，R2系数增加了6.985%，而RMSE降低了0.303%。这些初步结果表明利用脑血氧信息拟合步态参数的可行性。我们的研究为缺乏有效肌电信号的患者的辅助运动控制提供了新的视角，从而扩展了康复外骨骼机器人的临床应用。这项工作为推动脑机接口（BCI）技术在运动康复领域的应用奠定了基础。
