Real-Time Limb Motion Tracking with a Single IMU Sensor for Physical Therapy Exercises
Real-Time Limb Motion Tracking with a Single IMU Sensor for Physical Therapy Exercises
复制标题
使用单个 IMU 传感器进行实时肢体运动跟踪，用于物理治疗练习
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. Gao
中科院分区：
文献类型：
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作者：
Wenchuan Wei;Keiko Kurita;Jilong Kuang;A. Gao
肢体锻炼在物理治疗中很常见，可改善手臂/腿部的运动范围 (RoM)、力量和灵活性。为了改善治疗效果并降低成本，运动跟踪系统已用于监测用户在进行锻炼时的运动并提供指导。传统的运动跟踪系统基于摄像头或惯性测量单元 (IMU) 传感器。基于摄像头的系统面临着遮挡和照明引起的问题。传统的基于IMU的系统需要至少两个IMU传感器来跟踪整个肢体的运动，使用起来不方便。在本文中，我们提出了一种新颖的肢体运动跟踪系统，该系统使用单个 9 轴 IMU 传感器，该传感器佩戴在肢体的远端关节（即手臂的手腕或腿部的脚踝）上。使用单个 IMU 传感器进行肢体运动跟踪是一个具有挑战性的问题，因为 1) 在根据加速度数据估计位置时，嘈杂的 IMU 数据会导致漂移问题，2) 单个 IMU 传感器仅测量一个关节的运动，但肢体运动由多个关节的运动组成。为了解决这些问题，我们提出了一种循环神经网络（RNN）模型，可以根据嘈杂的 IMU 数据实时估计远端关节以及肢体其他关节（例如肘或膝）的 3D 位置。我们提出的方法在跟踪手臂运动时，在留一受试者交叉验证中实现了高精度，手腕/肘关节的中位误差为 7.2/7.1 厘米，比最先进的方法高出 10% 以上。此外，所提出的模型是轻量级的，可以在移动设备上进行实时应用。临床相关性——这项工作在改善家庭物理治疗中的肢体运动监测和 RoM 测量方面具有巨大的潜力。它还具有成本效益，并且可以广泛立即应用。
Limb exercises are common in physical therapy to improve range of motion (RoM), strength, and flexibility of the arm/leg. To improve therapy outcomes and reduce cost, motion tracking systems have been used to monitor the user’s movements when performing the exercises and provide guidance. Traditional motion tracking systems are based on either cameras or inertial measurement unit (IMU) sensors. Camera-based systems face problems caused by occlusion and lighting. Traditional IMU-based systems require at least two IMU sensors to track the motion of the entire limb, which is not convenient for use. In this paper, we propose a novel limb motion tracking system that uses a single 9-axis IMU sensor that is worn on the distal end joint of the limb (i.e., wrist for the arm or ankle for the leg). Limb motion tracking using a single IMU sensor is a challenging problem because 1) the noisy IMU data will cause drift problem when estimating position from the acceleration data, 2) the single IMU sensor measures the motion of only one joint but the limb motion consists of motion from multiple joints. To solve these problems, we propose a recurrent neural network (RNN) model to estimate the 3D positions of the distal end joint as well as the other joints of the limb (e.g., elbow or knee) from the noisy IMU data in real time. Our proposed approach achieves high accuracy with a median error of 7.2/7.1 cm for the wrist/elbow joint in leave-one-subject-out cross validation when tracking the arm motion, outperforming the state-of-the-art approach by more than 10%. In addition, the proposed model is lightweight, enabling real-time applications on mobile devices.Clinical relevance— This work has great potential to improve limb exercises monitoring and RoM measurement in home-based physical therapy. It is also cost effective and can be made available widely for immediate application.
影响因子：
6.2
作者：
Mehta, Dushyant;Sridhar, Srinath;Theobalt, Christian
通讯作者：
Theobalt, Christian