基于计算机视觉的穿戴式传感器关节运动学估计个性化方法及其在髋关节外骨骼中的应用
Personalization of Wearable Sensor-Based Joint Kinematic Estimation Using Computer Vision for Hip Exoskeleton Applications
AI总结：
针对穿戴式传感器关节运动学估计难以适应新步态且摄像头不可用的问题，提出基于计算机视觉的深度学习自适应框架，仅需1–2个步态周期即可通过迁移学习将TCN适配到僵硬膝步态，显著降低估计误差，并有望用于外骨骼等穿戴式机器人。
摘要：
准确的下肢关节运动学估计对于患者监测、康复和外骨骼控制等应用至关重要。虽然以往研究已采用基于穿戴式传感器的深度学习（DL）模型来估计关节运动学，但这些方法通常需要大量新数据集才能适应未见过的步态模式。与此同时，计算机视觉领域的研究者已推进了人体姿态估计模型，这类模型易于部署且能够进行实时推理。然而，在无法使用摄像头的场景中，此类模型并不可行。为了解决这些局限性，我们提出了一种基于计算机视觉的深度学习自适应框架，用于实时关节运动学估计。该框架仅需一个小型数据集（即1–2个步态周期），并且不依赖专业动作捕捉设备。利用迁移学习，我们将时间卷积网络（TCN）适配到僵硬膝步态数据，使模型与仅在健全人和僵硬膝数据集上训练的TCN相比，分别进一步将均方根误差降低了9.7%和19.9%。我们的框架表明，经智能手机摄像头训练的深度学习模型有潜力在临床人群中跨新用户实时估计关节运动学，并在穿戴式机器人中具有应用前景。
英文摘要：
Accurate lower-limb joint kinematic estimation is critical for applications such as patient monitoring, rehabilitation, and exoskeleton control. While previous studies have employed wearable sensor-based deep learning (DL) models for estimating joint kinematics, these methods often require extensive new datasets to adapt to unseen gait patterns. Meanwhile, researchers in computer vision have advanced human pose estimation models, which are easy to deploy and capable of real-time inference. However, such models are infeasible in scenarios where cameras cannot be used. To address these limitations, we propose a computer vision-based DL adaptation framework for real-time joint kinematic estimation. This framework requires only a small dataset (i.e., 1-2 gait cycles) and does not depend on professional motion capture setups. Using transfer learning, we adapted our temporal convolutional network (TCN) to stiff knee gait data, allowing the model to further reduce root mean square error by 9.7% and 19.9% compared to a TCN trained on only able-bodied and stiff knee datasets, respectively. Our framework demonstrates a potential for smartphone camera-trained DL models to estimate real-time joint kinematics across novel users in clinical populations with applications in wearable robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
