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Research Article | Open Access

Interlimb and Intralimb Synergy Modeling for Lower Limb Assistive Devices: Modeling Methods and Feature Selection

Fengyan Liang1,2,3,†, Lifen Mo1,2,†, Yiou Sun1,2, Cheng Guo1,2, Fei Gao4, Wei-Hsin Liao5, Junyi Cao6, Binbin Li3, Zhenhua Song3(), Dong Wang1,2, Ming Yin1,2()
State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya, China
Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya, China
Department of Rehabilitation Medicine, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, China
Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China

†These author contributed equally to this work.

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Abstract

The concept of gait synergy provides novel human–machine interfaces and has been applied to the control of lower limb assistive devices, such as powered prostheses and exoskeletons. Specifically, on the basis of gait synergy, the assistive device can generate/predict the appropriate reference trajectories precisely for the affected or missing parts from the motions of sound parts of the patients. Optimal modeling for gait synergy methods that involves optimal combinations of features (inputs) is required to achieve synergic trajectories that improve human–machine interaction. However, previous studies lack thorough discussions on the optimal methods for synergy modeling. In addition, feature selection (FS) that is crucial for reducing data dimensionality and improving modeling quality has often been neglected in previous studies. Here, we comprehensively investigated modeling methods and FS using 4 up-to-date neural networks: sequence-to-sequence (Seq2Seq), long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrent unit (GRU). We also conducted complete FS using 3 commonly used methods: random forest, information gain, and Pearson correlation. Our findings reveal that Seq2Seq (mean absolute error: 0.404° and 0.596°, respectively) outperforms LSTM, RNN, and GRU for both interlimb and intralimb synergy modeling. Furthermore, FS is proven to significantly improve Seq2Seq’s modeling performance (P < 0.05). FS-Seq2Seq even outperforms methods used in existing studies. Therefore, we propose FS-Seq2Seq as a 2-stage strategy for gait synergy modeling in lower limb assistive devices with the aim of achieving synergic and user-adaptive trajectories that improve human–machine interactions.

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Cyborg and Bionic Systems
Volume 5,
2024
Article number: 0122
DOI: 10.34133/cbsystems.0122
Cite this article:
Liang F, Mo L, Sun Y, et al. Interlimb and Intralimb Synergy Modeling for Lower Limb Assistive Devices: Modeling Methods and Feature Selection. Cyborg and Bionic Systems, 2024, 5: 0122. https://doi.org/10.34133/cbsystems.0122
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