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首页> 外文会议>International conference on neural information processing;Annual conference of Asia-Pacific Neural Network Society >Anthropometric Features Based Gait Pattern Prediction Using Random Forest for Patient-Specific Gait Training
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Anthropometric Features Based Gait Pattern Prediction Using Random Forest for Patient-Specific Gait Training

机译：基于人体特征的步态模式预测，使用随机森林进行患者特定的步态训练

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摘要

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.
机译：目前，使用下肢康复机器人来帮助中风患者恢复步行能力。设计用于机器人辅助步态训练的自然且个性化的步态轨迹对于改善治疗效果非常重要。同时，已经证明人的步态与人体测量特征密切相关，但是尚未进行充分的研究。因此，本文提出了一种基于人体特征的预测患者特定步态轨迹的方法。首先，使用傅里叶级数拟合步态轨迹，因此，步态模式可以通过获得的傅里叶系数来表示。然后，根据年龄，性别和12个身体参数来设计步态预测模型。为了易于在下肢康复机器人上应用，通过基于最小冗余-最大相关性准则的优化方法简化了人体测量特征。此外，通过使用随机森林算法对简化特征与人的步态之间的关系进行建模，基于该算法可以预测患者特定的步态轨迹。最后，在数据集上验证了设计的步态预测方法的性能。

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