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.

翻译：精确的步态事件检测对于步态分析、康复和辅助技术至关重要，尤其是在外骨骼控制中，准确识别支撑相和摆动相至关重要。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）模型在检测足跟着地和脚尖离地事件中的性能，数据来自588名健全受试者的4363个步态周期。结果表明，在基于运动学的方法中，Zeni等人方法取得了最高准确率，而其他方法则表现出系统性偏差或需要针对数据集进行调整。LSTM模型的性能与Zeni等人方法相当，提供了一种无系统性偏差的数据驱动替代方案。这些发现凸显了基于深度学习的步态事件检测方法的潜力，同时强调了在临床人群和不同步态条件下进一步验证的必要性。未来研究将探索这些方法在病理人群（如中风后患者和膝骨关节炎患者）中的泛化能力，以及它们在不同步态条件和数据采集场景下的鲁棒性，以增强其在康复和外骨骼控制中的适用性。