| Autor: |
Yunru Ma, Kumar Mithraratne, Nichola C. Wilson, Xiangbin Wang, Ye Ma, Yanxin Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Sensors, Vol 19, Iss 7, p 1660 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
1424-8220 |
| DOI: |
10.3390/s19071660 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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