Sivakumar Saaveethya, Gopalai Alpha Agape, Lim King Hann, Gouwanda Darwin, Chauhan Sunita
School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia.
Faculty of Engineering and Science, Curtin University Malaysia, Miri, Malaysia.
Sci Rep. 2021 May 13;11(1):10306. doi: 10.1038/s41598-021-89580-y.
This paper presents a wavelet neural network (WNN) based method to reduce reliance on wearable kinematic sensors in gait analysis. Wearable kinematic sensors hinder real-time outdoor gait monitoring applications due to drawbacks caused by multiple sensor placements and sensor offset errors. The proposed WNN method uses vertical Ground Reaction Forces (vGRFs) measured from foot kinetic sensors as inputs to estimate ankle, knee, and hip joint angles. Salient vGRF inputs are extracted from primary gait event intervals. These selected gait inputs facilitate future integration with smart insoles for real-time outdoor gait studies. The proposed concept potentially reduces the number of body-mounted kinematics sensors used in gait analysis applications, hence leading to a simplified sensor placement and control circuitry without deteriorating the overall performance.
本文提出了一种基于小波神经网络（WNN）的方法，以减少步态分析中对可穿戴运动传感器的依赖。由于多个传感器放置和传感器偏移误差所带来的缺点，可穿戴运动传感器阻碍了实时户外步态监测应用。所提出的WNN方法使用从足部动力学传感器测量的垂直地面反作用力（vGRF）作为输入，来估计踝关节、膝关节和髋关节角度。显著的vGRF输入是从主要步态事件间隔中提取的。这些选定的步态输入有助于未来与智能鞋垫集成，用于实时户外步态研究。所提出的概念有可能减少步态分析应用中使用的身体安装运动传感器的数量，从而在不降低整体性能的情况下简化传感器放置和控制电路。
