用简化疗法输入控制下肢外骨骼，实现自适应步态辅助。
Deep-Learning Control of Lower-Limb Exoskeletons via simplified Therapist Input
- 通过传感器数据概率推断步态状态，再由治疗师调整特征。
- 不同步态条件下均实现负交互功率，表明有效辅助。
- 适合康复训练中需快速调参的临床场景。
部分辅助型外骨骼在步态康复中具有重要潜力，能促进患者主动参与正常行走模式的(再)学习。传统控制方法依赖分层结构，因控制器复杂且需个体化参数调节，尤其在上下楼梯或坡道行走时校准繁琐。为此，本文提出一种三步数据驱动方法：（1）利用近期传感器数据概率推断步态状态（着地步长、着地步高、步行速度、步幅高度、步态相位）；（2）通过用户界面允许治疗师修改这些特征；（3）基于预测不确定性，使用调整后的步态特征预测目标关节姿态及弹簧阻尼系统模型刚度。在两名健康受试者上测试，涵盖跑步机行走及不同速度下的上下楼梯，有无通过界面外部修改步态特征。结果表明，运动学随步态特征变化，且在所有条件下均呈现负交互功率，说明外骨骼提供了有效辅助。
原文摘要 · Abstract (English)
Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of normative walking patterns. Typically, the control of interaction torques in partial-assistance exoskeletons relies on a hierarchical control structure. These approaches require extensive calibration due to the complexity of the controller and user-specific parameter tuning, especially for activities like stair or ramp navigation. To address the limitations of hierarchical control in exoskeletons, this work proposes a three-step, data-driven approach: (1) using recent sensor data to probabilistically infer locomotion states (landing step length, landing step height, walking velocity, step clearance, gait phase), (2) allowing therapists to modify these features via a user interface, and (3) using the adjusted locomotion features to predict the desired joint posture and model stiffness in a spring-damper system based on prediction uncertainty. We evaluated the proposed approach with two healthy participants engaging in treadmill walking and stair ascent and descent at varying speeds, with and without external modification of the gait features through a user interface. Results showed a variation in kinematics according to the gait characteristics and a negative interaction power suggesting exoskeleton assistance across the different conditions.
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