提出通用力矩特征方法，提升髋外骨骼力矩估计的个性化与适应性。
Joint Moment Estimation for Hip Exoskeleton Control: A Generalized Moment Feature Generation Method
- 构建不变于个体差异的通用力矩特征，通过解码器还原关节力矩。
- 在28种步行速度下误差仅0.1180 Nm/kg，较基准模型提升6.5%~8.3%。
- 仅用编码器数据即可实现20.5%代谢降低，适合无额外传感器的外骨骼系统。
行走时髋关节力矩是髋外骨骼辅助控制的核心基础。近期研究显示，即时估计关节力矩相比基于步态预测生成助力扭矩具有传感器要求低、适应变速行走等优势。然而现有方法仍缺乏个性化，导致新用户估计精度下降。为此，本文提出基于通用力矩特征（GMF）的髋关节力矩估计方法。构建GMF生成器，学习对个体差异不变的力矩特征表示，并通过专用解码器还原为实际力矩。利用该特征表示，采用基于GRU的神经网络，仅需外骨骼编码器获取的关节运动学数据即可预测GMF。该方法在足式跑步机数据集上28种步行速度条件下达到0.1180 Nm/kg的均方根误差，较无体征参数融合模型提升6.5%，较传统体征融合模型提升8.3%。进一步在仅配备编码器的髋外骨骼上应用，相较于无辅助状态，在平地行走中实现平均20.5%的代谢率降低（p<0.01）。
原文摘要 · Abstract (English)
Hip joint moments during walking are the key foundation for hip exoskeleton assistance control. Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation, such as simple sensor requirements and adaptability to variable walking speeds. However, existing joint moment estimation methods still suffer from a lack of personalization, leading to estimation accuracy degradation for new users. To address the challenges, this paper proposes a hip joint moment estimation method based on generalized moment features (GMF). A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder. Utilizing this well-featured representation, a GRU-based neural network is used to predict GMF with joint kinematics data, which can easily be acquired by hip exoskeleton encoders. The proposed estimation method achieves a root mean square error of 0.1180 Nm/kg under 28 walking speed conditions on a treadmill dataset, improved by 6.5% compared to the model without body parameter fusion, and by 8.3% for the conventional fusion model with body parameter. Furthermore, the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5% metabolic reduction (p<0.01) for users compared to assist-off condition in level-ground walking.
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