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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
A Multimodal Framework Based on Integration of Cortical and Muscular Activities for Decoding Human Intentions About Lower Limb Motions
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| 英文论文题目: |
A Multimodal Framework Based on Integration of Cortical and Muscular Activities for Decoding Human Intentions About Lower Limb Motions
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| 论文题目英文: |
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| 作者: |
Cui, Chengkun
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| 论文出处: |
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| 刊物名称: |
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
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| 年: |
2017
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| 卷: |
11 |
| 期: |
4 |
| 页: |
889-899 |
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| 摘要: |
In this study, a multimodal fusion framework based on three different modal biosignals is developed to recognize human intentions related to lower limb multi-joint motions which commonly appear in daily life. Electroencephalogram (EEG), electromyogram (EMG) and mechanomyogram (MMG) signals were simultaneously recorded from twelve subjects while performing nine lower limb multi-joint motions. These multimodal data are used as the inputs of the fusion framework for identification of different motion intentions. Twelve fusion techniques are evaluated in this framework and a large number of comparative experiments are carried out. The results show that a support vector machine-based three-modal fusion scheme can achieve average accuracies of 98.61%, 97.78% and 96.85%, respectively, under three different data division forms. Furthermore, the relevant statistical tests reveal that this fusion scheme brings significant accuracy improvement in comparison with the cases of two-modal fusion or only a single modality. These promising results indicate the potential of the multimodal fusion framework for facilitating the future development of human-robot interaction for lower limb rehabilitation. |
| 英文摘要: |
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