Real-Time Walking Gait Estimation for Construction Workers using a Single Wearable Inertial Measurement Unit (IMU)
Real-Time Walking Gait Estimation for Construction Workers using a Single Wearable Inertial Measurement Unit (IMU)
复制标题
DOI：
10.1109/aim46487.2021.9517592
复制
发表时间：
2021-07
期刊：
影响因子：
--
通讯作者：
Siyu Chen;Srikanth Sagar Bangaru;Tarik Yigit;M. Trkov;Chao Wang;J. Yi
中科院分区：
文献类型：
--
作者：
Siyu Chen;Srikanth Sagar Bangaru;Tarik Yigit;M. Trkov;Chao Wang;J. Yi
实时的步态检测和姿态估计对于建筑工人的安全监测和预防与工作相关的肌肉骨骼疾病至关重要。提出了一种基于单个可穿戴惯性测量单元(IMU)的步态检测和姿态估计方法，用于在平坦和倾斜表面上行走的人体。步态检测算法建立在基于递归神经网络方法的基础上，并将其结果用于全身姿势估计。该检测方案还实时预测地形坡度信息。通过学习隐含空间中的运动流形，利用高斯过程动力学模型进行姿态估计。为了验证和验证该设计，在水平面和坡面上进行了不同行走模式和速度的大量实验。该算法可以检测96%的步态活动，估计的人体姿态误差在8.30°以内，检测延迟在18.6ms以内，只需在人体小腿上安装一个IMU。
Real-time gait detection and pose estimation are critical for safety monitoring and prevention of work-related musculoskeletal disorders for construction workers. We present a single wearable inertial measurement unit (IMU)-based gait detection and pose estimation for human walking on flat and sloped surfaces. The gait detection algorithm is built on a recurrent neural network-based method and its outcome is then used in the full-body pose estimation. The detection scheme also predicts the terrain slope information in real-time. The pose estimation is obtained through learned motion manifold in latent space with the Gaussian process dynamic model. Extensive experiments of different walking patterns and speeds on the level and sloped surfaces are conducted to validate and demonstrate the design. The proposed algorithm can detect gait activities with 96% accuracy, the estimated human pose errors are within 8.30 degs, and the detection latency is within 18.6 ms using only a single IMU attached to a human shank.