发表机构
GIST（光州科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于稀疏校准的个性化核方法，利用可穿戴IMU联合估计步态相位与速度，通过用户特定基线与主成分模型降低相位误差，并在嵌入式硬件上实现实时运行，但需更大规模验证。
AI 中文摘要
本文提出了一种基于稀疏校准的个性化方法，用于从可穿戴惯性测量单元（IMUs）中联合估计步态相位和步行速度。利用一个包含20名受试者的运动数据集，从双侧大腿和小腿轨迹构建了一个包含28种速度的参考库。该方法并非直接应用群体平均核，而是将基于三个校准速度估计的用户特定基线与基线中心运动学偏差的主成分（PC）模型相结合。离线验证表明，与群体核相比，个性化核降低了相位误差。在单参与者在线试点评估中，个性化核在数值上产生了更低的平均相位和速度误差，并在嵌入式硬件上实时运行。然而，速度效应不显著，且成对相位差异在多重比较校正后不再显著。试点评估验证了可穿戴实现的可行性，同时指出了参考数据与在线数据集之间的不匹配，以及需要更大规模队列验证的必要性。
英文摘要
This paper presents sparse calibration-based personalization for kernel-based gait phase and walking speed co-estimation from wearable inertial measurement units (IMUs). A 28-speed reference library was constructed from bilateral thigh and shank trajectories in a 20-subject locomotion dataset. Rather than directly applying population-average kernels, the method combines a user-specific baseline estimated from three calibration speeds with a principal-component (PC) model of baseline-centered kinematic deviations. Offline validation showed that personalized kernels reduced phase error relative to the population kernel. In a single-participant online pilot evaluation, the personalized kernels produced numerically lower mean phase and speed errors and ran in real time on embedded hardware. However, the speed effect was not significant, and pairwise phase differences did not remain significant after multiple-comparison correction. The pilot evaluation establishes wearable implementation feasibility while indicating reference-to-online dataset mismatch and the need for larger-cohort validation.
Comments6 pages, 4 figures, The 26th International Conference on Control, Automation, and Systems (ICCAS 2026)
