IMU Kinematics Using Deep Learning and Top-Down Optimization

The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of motion-tracking sensors in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in out-of-laboratory settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings.Data: We uploaded the sample data and code for the demo run. This includes pre-trained models and one-subject sample IMU data for each joint (hip, knee, and ankle) and activity (walking and running). To run the demo code, please follow:1. Download the demo.zip files in Downloads module and unzip it2. Unzip data.zip and models.zip in the directory3. Install the required dependencies listed in the requirements.txt file4. Run demo.py by specifying your target joint and activity. (e.g., python3 demo.py 'Hip' 'Walking')Code: https://github.com/CMU-MBL/JointAnglePrediction_JOBCitation: Eric Rapp*, Soyong Shin*, Wolf Thomsen, Reed Ferber, and Eni Halilaj. "Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework." Journal of Biomechanics 116 (2021): 110229.* equal contribution <br/><br/>This project includes the following software/data packages: <br/> <ul> <li> <a href="https://simtk.org/frs?group_id=1988#pack_2377">Demo run </a> : Please follow the instruction of running the demo in the project homepage. </li> </ul>

联合运动学参数的估算难题，构成了在生物力学领域广泛使用运动跟踪传感器的关键障碍。传统的传感器融合滤波器在很大程度上依赖于磁力计读数，而在不受控制的坏境中，这些读数可能会受到影响。细致的传感器与节段的对准以及校准策略亦属必要，但这可能给用户带来负担，并导致在实验室外的环境中产生进一步误差。本研究引入了一种新的框架，该框架结合了深度学习与自上而下的优化方法，能够直接从惯性数据中准确预测下肢关节角度，而无需依赖磁力计读数。数据：我们上传了用于演示运行的样本数据和代码，包括预训练模型以及每个关节（髋、膝、踝）和活动（行走和跑步）的单个受试者样本惯性测量单元（IMU）数据。运行演示代码的步骤如下：1. 在下载模块中下载demo.zip文件并解压；2. 在目录中解压data.zip和models.zip；3. 安装requirements.txt文件中列出的必需依赖项；4. 通过指定目标关节和活动来运行demo.py（例如，python3 demo.py 'Hip' 'Walking'）。代码：https://github.com/CMU-MBL/JointAnglePrediction_JOBCitation: Eric Rapp*、Soyong Shin*、Wolf Thomsen、Reed Ferber 和 Eni Halilaj.