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Anthropometric Features Based Gait Pattern Prediction Using Random Forest for Patient-Specific Gait Training

  • Shixin Ren
  • Weiqun WangEmail author
  • Zeng-Guang Hou
  • Xu Liang
  • Jiaxing Wang
  • Liang Peng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)

Abstract

Using lower limb rehabilitation robots to help stroke patients recover their walking ability is becoming more and more popular presently. The natural and personalized gait trajectories designed for robot assisted gait training are very important for improving the therapeutic results. Meanwhile, it has been proved that human gaits are closely related to anthropometric features, which however has not been well researched. Therefore, a method based on anthropometric features for prediction of patient-specific gait trajectories is proposed in this paper. Firstly, Fourier series are used to fit gait trajectories, hence, gait patterns can be represented by the obtained Fourier coefficients. Then, human age, gender and 12 body parameters are used to design the gait prediction model. For the purpose of easy application on lower limb rehabilitation robots, the anthropometric features are simplified by an optimization method based on the minimal-redundancy-maximal-relevance criterion. Moreover, the relationship between the simplified features and human gaits is modeled by using a random forest algorithm, based on which the patient-specific gait trajectories can be predicted. Finally, the performance of the designed gait prediction method is validated on a dataset.

Keywords

Patient-specific gait Anthropometric features Random forest Gait prediction 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shixin Ren
    • 1
    • 2
  • Weiqun Wang
    • 1
    • 2
    Email author
  • Zeng-Guang Hou
    • 1
    • 3
  • Xu Liang
    • 1
    • 2
  • Jiaxing Wang
    • 1
    • 2
  • Liang Peng
    • 1
    • 2
  1. 1.The State Key Laboratory of Management and Control for Complex Systems, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.The CAS Center for Excellence in Brain Science and Intelligence TechnologyBeijingChina

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