Abstract
The aim of this study is to evaluate if Kinect is a valid and reliable clinical gait analysis tool for children with cerebral palsy (CP), and whether linear regression and long short-term memory (LSTM) recurrent neural network methods can improve its performance. A gait analysis was conducted on ten children with CP, on two occasions. Lower limb joint kinematics computed from the Kinect and a traditional marker-based Motion Analysis system were investigated by calculating the root mean square errors (RMSE), the coefficients of multiple correlation (CMC), and the intra-class correlation coefficients (ICC2,k). Results showed that the Kinect-based kinematics had an overall modest to poor correlation (CMC—less than 0.001 to 0.70) and an angle pattern similarity with Motion Analysis. After the calibration, RMSE on every degree of freedom decreased. The two calibration methods indicated similar levels of improvement in hip sagittal (CMC—0.81 ± 0.10 vs. 0.75 ± 0.22)/frontal (CMC—0.41 ± 0.35 vs. 0.42 ± 0.37) and knee sagittal kinematics (CMC—0.85±0.07 vs. 0.87 ± 0.12). The hip sagittal (CMC—0.97±0.05) and knee sagittal (CMC—0.88 ± 0.12) angle patterns showed a very good agreement over two days. Modest to excellent reliability (ICC2,k—0.45 to 0.93) for most parameters renders it feasible for observing ongoing changes in gait kinematics.
Highlights
Three-dimensional gait analysis (3DGA) is a useful tool for providing quantitative information for treatment decision making and outcome assessment, especially for children with cerebral palsy (CP) [1,2]
The Bland–Altman plots for every kinematic parameter are presented in Figure S1 (Supplementary Materials); mean difference, limits of agreement (LoA), Upper and Lower LoA are given in Tables S1–S3 (Supplementary Materials)
We can see that only the coefficients of multiple correlation (CMC) values for the knee flexion/extension angle reached moderate similarity
Summary
Three-dimensional gait analysis (3DGA) is a useful tool for providing quantitative information for treatment decision making and outcome assessment, especially for children with cerebral palsy (CP) [1,2]. High financial burden for equipment purchasing and maintenance, technical expertise requirements for experimental operation, and data processing, limit its clinical applications. Due to its marker-based optoelectronic tracking strategies, participants might become conscious of being observed which might lead to over-performance, rather than their natural daily gait [3]. Accurately placing markers is a challenge for children who are restless and might be less compliant. The shortcomings of traditional 3DGA systems necessitate the development of cost-effective and user-friendly motion capture tools for clinical practice, in particular for lesser developed areas, community health service centers, and home-based observation.
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