Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2186-2190. doi: 10.1109/EMBC46164.2021.9630438.
Elderly health monitoring, rehabilitation training, and sport supervision could benefit from continuous assessment of joint angle, and angular velocity to identify the joint movement patterns. However, most of the measurement systems are designed based on special kinematic sensors to estimate angular velocities. The study aims to measure the lower limb joint angular velocity based on a 2D vision camera system during squat and walking on treadmill action using deep convolution neural network (CNN) architecture. Experiments were conducted on 12 healthy adults, and six digital cameras were used to capture the videos of the participant actions in lateral and frontal view. The normalized cross-correlation (Cc) analysis was performed to obtain a degree of symmetry of the ground truth and estimated angular velocity waveform patterns. Mean Cc for angular velocity estimation by deep CNN model has higher than 0.90 in walking on the treadmill and 0.89 in squat action. Furthermore, joint-wise angular velocities at the hip, knee, and ankle joints were observed and compared. The proposed system gets higher estimation performance under the lateral view and the frontal view of the camera. This study potentially eliminates the requirement of wearable sensors and proves the applicability of using video-based system to measure joint angular velocities during squat and walking on a treadmill actions.
老年人健康监测、康复训练和运动监督可以受益于对关节角度和角速度的连续评估，以识别关节运动模式。然而，大多数测量系统都是基于特殊的运动传感器来估计角速度而设计的。本研究旨在使用深度卷积神经网络（CNN）架构，基于二维视觉相机系统在深蹲和跑步机行走动作期间测量下肢关节角速度。对 12 名健康成年人进行了实验，并使用六台数码相机从侧面和正面拍摄参与者动作的视频。通过归一化互相关（Cc）分析，获得地面真实和估计角速度波形模式的对称程度。通过深度 CNN 模型进行角速度估计的平均 Cc 在跑步机行走时高于 0.90，在深蹲动作时高于 0.89。此外，还观察和比较了髋关节、膝关节和踝关节的关节角速度。该系统在相机的侧面和正面视图下具有更高的估计性能。这项研究可能消除了对可穿戴传感器的需求，并证明了使用基于视频的系统在深蹲和跑步机行走动作期间测量关节角速度的适用性。
