GCCRR: A Short Sequence Gait Cycle Segmentation Method Based on Ear-Worn IMU

• URL: http://arxiv.org/abs/2409.00983v2
• Date: Mon, 30 Sep 2024 10:17:51 GMT
• Title: GCCRR: A Short Sequence Gait Cycle Segmentation Method Based on Ear-Worn IMU
• Authors: Zhenye Xu, Yao Guo,
• Abstract summary: This paper addresses the critical task of gait cycle segmentation using short sequences from ear-worn IMUs. We introduce the Gait Characteristic Curve Regression and Restoration (GCCRR) method, a novel two-stage approach for fine-grained gait phase segmentation. Our method employs Bi-LSTM-based deep learning algorithms for regression to ensure reliable segmentation for short gait sequences.
• Score: 3.2428847882267404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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