This paper addresses the critical task of gait cycle segmentation using short sequences from ear-worn IMUs, a practical and non-invasive approach for home-based monitoring and rehabilitation of patients with impaired motor function. While previous studies have focused on IMUs positioned on the lower limbs, ear-worn IMUs offer a unique advantage in capturing gait dynamics with minimal intrusion. To address the challenges of gait cycle segmentation using short sequences, we introduce the Gait Characteristic Curve Regression and Restoration (GCCRR) method, a novel two-stage approach designed for fine-grained gait phase segmentation. The first stage transforms the segmentation task into a regression task on the Gait Characteristic Curve (GCC), which is a one-dimensional feature sequence incorporating periodic information. The second stage restores the gait cycle using peak detection techniques. Our method employs Bi-LSTM-based deep learning algorithms for regression to ensure reliable segmentation for short gait sequences. Evaluation on the HamlynGait dataset demonstrates that GCCRR achieves over 80\% Accuracy, with a Timestamp Error below one sampling interval. Despite its promising results, the performance lags behind methods using more extensive sensor systems, highlighting the need for larger, more diverse datasets. Future work will focus on data augmentation using motion capture systems and improving algorithmic generalizability.

翻译：本文针对使用耳戴式惯性测量单元（IMU）短序列进行步态周期分割这一关键任务展开研究，该方法为运动功能受损患者的家庭监测与康复提供了一种实用且非侵入性的手段。以往研究多聚焦于佩戴在下肢的IMU，而耳戴式IMU能以最小侵入性捕捉步态动态，具有独特优势。为应对短序列步态周期分割的挑战，我们提出了步态特征曲线回归与恢复（GCCRR）方法，这是一种专为细粒度步态相位分割设计的新型两阶段方法。第一阶段将分割任务转化为对步态特征曲线（GCC）的回归任务，该曲线是融合了周期性信息的一维特征序列。第二阶段则利用峰值检测技术恢复步态周期。我们的方法采用基于双向长短期记忆网络（Bi-LSTM）的深度学习算法进行回归，以确保对短步态序列的可靠分割。在HamlynGait数据集上的评估表明，GCCRR实现了超过80\%的准确率，且时间戳误差低于一个采样间隔。尽管结果令人鼓舞，但其性能仍落后于使用更复杂传感器系统的方法，这突显了对更大规模、更多样化数据集的需求。未来工作将集中于利用运动捕捉系统进行数据增强，并提升算法的泛化能力。