用LSTM模型实现无偏差的步态事件精准检测
Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models
- 采用LSTM模型自动学习步态周期中的足跟触地与足尖离地
- 在588人共4363个步态周期上，LSTM性能媲美最优传统方法
- 适合康复与外骨骼控制场景，无需针对数据集调参
准确识别步态事件对步态分析、康复及辅助技术至关重要，尤其在外骨骼控制中，精确区分支撑相与摆动相尤为关键。本研究评估了七种基于运动学的方法和一种长短期记忆（LSTM）模型在588名健康受试者共4363个步态周期上的表现。结果显示，尽管Zeni等人方法在传统方法中精度最高，但其他方法存在系统性偏差或需针对特定数据集调整；而LSTM模型表现相当，且无系统性偏差，提供了一种数据驱动的替代方案。研究强调深度学习在步态事件检测中的潜力，同时指出需在临床人群及多种步态条件下进一步验证。未来将探索这些方法在卒中后患者和膝骨关节炎患者等病理人群中的泛化能力，以及在不同步态条件和数据采集环境下的鲁棒性，以提升其在康复与外骨骼控制中的应用价值。
原文摘要 · Abstract (English)
Accurate gait event detection is crucial for gait analysis, rehabilitation, and assistive technology, particularly in exoskeleton control, where precise identification of stance and swing phases is essential. This study evaluated the performance of seven kinematics-based methods and a Long Short-Term Memory (LSTM) model for detecting heel strike and toe-off events across 4363 gait cycles from 588 able-bodied subjects. The results indicated that while the Zeni et al. method achieved the highest accuracy among kinematics-based approaches, other methods exhibited systematic biases or required dataset-specific tuning. The LSTM model performed comparably to Zeni et al., providing a data-driven alternative without systematic bias. These findings highlight the potential of deep learning-based approaches for gait event detection while emphasizing the need for further validation in clinical populations and across diverse gait conditions. Future research will explore the generalizability of these methods in pathological populations, such as individuals with post-stroke conditions and knee osteoarthritis, as well as their robustness across varied gait conditions and data collection settings to enhance their applicability in rehabilitation and exoskeleton control.
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