Quantitative Analysis of Lower Limb Motion in Parkinson’s Disease Based on Inertial Sensors
Ruichen Liu, Zhelong Wang, Hongyu Zhao, Sen Qiu, Cui Wang, Xin Shi, Fang Lin
Abstract
This study proposed a wearable inertial motion capture (Mocap) method for Parkinson’s lower limb motion reconstruction and analysis. To eliminate the error accumulation caused by sensor noise, the error state Kalman filter is used for attitude estimation. The inertial Mocap method proposed in this study is compared with the OptiTrack system to verify the motion tracking effect. The accuracy of different phase-detection methods is compared, and the highest recognition accuracy is 99.11% using the long short-term memory (LSTM) method. In addition, this study collected subjects in healthy control (HC), Parkinson’s disease (PD), and freezing of gait (FOG) groups for comparative analysis. The experimental results revealed differences in motion characteristics and kinematics between PD and healthy subjects. Thus, the quantitative analysis results can be used as an important reference for the clinical application of PD.