两阶段个性化脑卒中幸存者外骨骼辅助行走步态相位估计：一项离线可行性研究
Two-Stage Personalized Gait Phase Estimation in Stroke Survivors During Exoskeleton-Assisted Walking: An Offline Feasibility Study
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中文总结 AI 辅助
本研究提出两阶段个性化步态相位估计框架，结合IMU对齐和模型适应，在脑卒中幸存者外骨骼行走中显著降低相位误差，验证了离线可行性与嵌入式实时性。
中文摘要 AI 辅助
本研究评估了使用功能性惯性测量单元（IMU）对齐和两阶段顺序适应健康步态预训练模型，为脑卒中幸存者进行个性化步态相位估计。估计器使用大腿安装的IMU信号。脚跟力敏电阻测量提供参考相位标签，用于离线适应和评估。第一阶段建立了蒸馏正则化的参与者特定模型，第二阶段使用低秩适应进行条件细化。在五名脑卒中幸存者中，使用动力膝关节外骨骼行走，通过留一受试者超参数选择和顺序测试后适应的第二阶段重放，评估了长短期记忆（LSTM）、时间卷积网络（TCN）和Transformer模型。相对于未适应的基线，第一阶段+第二阶段分别将平均参与者相位均方根误差降低了84.2%、77.0%和60.7%。Transformer实现了最低的最终误差（步态周期的2.90±1.13%）和脚跟触地时间误差（23.7±4.5毫秒）。策略特定的消融研究表明，每周期更新通常产生最低或接近最低的误差，而条件更新减少了更新频率，但精度差异很小。个性化后，对齐产生了模型相关的相位误差变化，同时保持或改善了脚跟触地检测，并减少了LSTM和Transformer的脚跟触地时间误差。并发嵌入式测试显示，TCN和Transformer在第二阶段更新期间保持了100Hz推理，没有错过截止时间，而LSTM在6.6%的推理中错过了10毫秒的截止时间。所有更新在0.8秒内完成。这些结果支持所提出的外骨骼辅助行走框架的离线可行性和嵌入式计算时序。
英文摘要
This study evaluated personalized gait phase estimation for stroke survivors using functional inertial measurement unit (IMU) alignment and two-stage sequential adaptation of models pre-trained on healthy gait. The estimator used signals from a thigh-mounted IMU. Heel force-sensitive resistor measurements provided reference phase labels for offline adaptation and evaluation. Stage 1 established a distillation-regularized participant-specific model, and Stage 2 performed conditional refinement using low-rank adaptation. Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Transformer models were evaluated in five stroke survivors walking with a powered knee exoskeleton using leave-one-subject-out hyperparameter selection and sequential test-then-adapt Stage 2 replay. Relative to the non-adapted baselines, Stage 1+2 reduced the mean participant-wise phase root mean square error by 84.2%, 77.0%, and 60.7%, respectively. The Transformer achieved the lowest final error (2.90 +- 1.13$% of the gait cycle) and heel-strike timing error (23.7 +- 4.5ms). Policy-specific ablations showed that every-cycle updates generally produced the lowest or near-lowest error, whereas conditional updating reduced the update frequency with small accuracy differences. After personalization, alignment produced model-dependent changes in phase error while preserving or improving heel-strike detection and reducing heel-strike timing error for the LSTM and Transformer. Concurrent embedded tests showed that the TCN and Transformer maintained 100-Hz inference during Stage 2 updates without deadline misses, whereas the LSTM missed the 10-ms deadline in 6.6% of inferences. All updates completed within 0.8s. These results support the offline feasibility and embedded computational timing of the proposed framework for exoskeleton-assisted walking.
发表机构
- Gwangju Institute of Science and Technology (GIST)（光州科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
