用肌电与惯性传感器数据，提升外骨骼对步态动作的实时识别准确率。
Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals
- 融合8个肌电与3个惯性传感器信号，构建多模态输入框架。
- 卷积网络在5类动作上达96.5%准确率，优于LSTM的87.5%。
- 仅需每类10样本即可适配新用户，且抗传感器失效能力强。
踝关节外骨骼因其在提升移动能力与降低跌倒风险方面的潜力而受到广泛关注，尤其适用于老年人群。其性能依赖于通过传感器输入实现用户意图动作的精准实时预测。本文提出一种新型运动预测框架，集成三个惯性测量单元（IMUs）和八个表面肌电（sEMG）传感器，同步采集运动学与肌肉活动数据。为模拟无障碍环境中的日常行为，采集了涵盖多种典型动作的完整数据集。实验结果表明，卷积神经网络（CNN）在五类运动任务上的分类准确率达96.5 ± 0.8%，略优于长短期记忆网络（LSTM）的87.5 ± 2.9%。此外，系统展现出优异的迁移学习能力，仅需每个类别十次样本即可完成微调，实现对新受试者的高精度动作分类。模型在部分传感器信号缺失的情况下仍保持稳定性能，具备实际应用中的鲁棒性。这些成果凸显深度学习算法在提升踝关节外骨骼功能性和安全性方面的潜力，有助于其在日常生活中的广泛应用。
原文摘要 · Abstract (English)
Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility and reduce fall risks, particularly among the aging population. The efficacy of these devices relies on accurate real-time prediction of the user's intended movements through sensor-based inputs. This paper presents a novel motion prediction framework that integrates three Inertial Measurement Units (IMUs) and eight surface Electromyography (sEMG) sensors to capture both kinematic and muscular activity data. A comprehensive set of activities, representative of everyday movements in barrier-free environments, was recorded for the purpose. Our findings reveal that Convolutional Neural Networks (CNNs) slightly outperform Long Short-Term Memory (LSTM) networks on a dataset of five motion tasks, achieving classification accuracies of $96.5 \pm 0.8 \%$ and $87.5 \pm 2.9 \%$, respectively. Furthermore, we demonstrate the system's proficiency in transfer learning, enabling accurate motion classification for new subjects using just ten samples per class for finetuning. The robustness of the model is demonstrated by its resilience to sensor failures resulting in absent signals, maintaining reliable performance in real-world scenarios. These results underscore the potential of deep learning algorithms to enhance the functionality and safety of ankle exoskeletons, ultimately improving their usability in daily life.
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