Current gait analysis faces challenges in various aspects, including limited and poorly labeled data within existing wearable electronics databases, difficulties in collecting patient data due to privacy concerns, and the inadequacy of the Zero-Velocity Update Technique (ZUPT) in accurately analyzing pathological gait patterns. To address these limitations, we introduce GaitMotion, a novel machine-learning framework that employs few-shot learning on a multitask dataset collected via wearable IMU sensors for real-time pathological gait analysis. GaitMotion enhances data quality through detailed, ground-truth-labeled sequences and achieves accurate step and stride segmentation and stride length estimation, which are essential for diagnosing neurological disorders. We incorporate a generative augmentation component, which synthesizes rare or underrepresented pathological gait patterns. GaitMotion achieves a 65\% increase in stride length estimation accuracy compared to ZUPT. In addition, its application to real patient datasets via transfer learning confirms its robust predictive capability. By integrating generative AI into wearable gait analysis, GaitMotion not only refines the precision of pathological gait forecasting but also demonstrates a scalable framework for leveraging synthetic data in biomechanical pattern recognition, paving the way for more personalized and data-efficient digital health services.

翻译：当前步态分析面临多方面的挑战，包括现有可穿戴电子设备数据库中数据有限且标注质量不佳、因隐私问题导致患者数据收集困难，以及零速度更新技术在准确分析病理步态模式方面存在不足。为应对这些局限，我们提出了GaitMotion——一种新颖的机器学习框架，该框架通过对可穿戴IMU传感器采集的多任务数据集进行少样本学习，实现实时病理步态分析。GaitMotion通过精细的真实标注序列提升数据质量，并实现了精确的步态周期步幅分割与步长估计，这对神经系统疾病的诊断至关重要。我们引入了生成式增强模块，能够合成罕见或代表性不足的病理步态模式。与零速度更新技术相比，GaitMotion在步长估计准确率上提升了65%。此外，通过迁移学习在真实患者数据集上的应用验证了其强大的预测能力。通过将生成式人工智能整合到可穿戴步态分析中，GaitMotion不仅提升了病理步态预测的精度，更展示了一个利用合成数据进行生物力学模式识别的可扩展框架，为更个性化、数据效率更高的数字健康服务铺平了道路。