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Research Article
Parametric generation of three-dimensional gait for robot-assisted rehabilitation
Di Shi, Wuxiang Zhang, Xilun Ding, Lei Sun
Biology Open 2020 9: bio047332 doi: 10.1242/bio.047332 Published 5 March 2020
Di Shi
1School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
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  • ORCID record for Di Shi
Wuxiang Zhang
1School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
2Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China
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  • For correspondence: zhangwuxiang@buaa.edu.cn
Xilun Ding
1School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
2Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China
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Lei Sun
3Beijing Institute Traumatology & Orthopedics, Beijing Jishuitan Hospital, Beijing 100035, China
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    Fig. 1.

    Boxplots of individual mean durations in percent of the total gait cycle versus speed. The curves represent the linear regression lines. The results of Wilcoxon matched pairs signed rank tests with Bonferroni correction are shown for each adjacent speed pair (*P<0.00076).

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    Fig. 2.

    Validation of the generated reference trajectories. RMSE and correlation coefficients between actual and generated trajectories are averaged across participants for each walking speed.

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    Fig. 3.

    Overview of the gait cycle and sub-phases analyzed in this study. Total gait cycle, first double support phase (DSP1), first single support phase (SSP1), second double support phase (DSP2), initial-contact, opposite toe-off, opposite initial-contact, and toe-off.

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    Fig. 4.

    Selection of gait features. The dots indicate the amplitude of the gait event (e1, e2, e3 and e4) and the extreme values of the position (f1 and f2), all extracted as the gait features. A1, A2 and A3 indicate dorsiflexion (+)/plantarflexion, inversion (+)/eversion and internal (+)/external rotation angles of the ankle joint.

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    Fig. 5.

    Helen Hayes marker set. To track the motion of the subject, 19 reflective markers with a diameter of 20 mm were affixed to specified locations. The hip joint center (HJC), knee joint center (KJC) and ankle joint center (AJC) of both legs were calculated using the Cortex software package.

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Keywords

  • Gait analysis
  • Gait generation
  • Robot-assisted rehabilitation
  • Regression analysis
  • Three-dimensional gait

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Research Article
Parametric generation of three-dimensional gait for robot-assisted rehabilitation
Di Shi, Wuxiang Zhang, Xilun Ding, Lei Sun
Biology Open 2020 9: bio047332 doi: 10.1242/bio.047332 Published 5 March 2020
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Research Article
Parametric generation of three-dimensional gait for robot-assisted rehabilitation
Di Shi, Wuxiang Zhang, Xilun Ding, Lei Sun
Biology Open 2020 9: bio047332 doi: 10.1242/bio.047332 Published 5 March 2020

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