Research on Adaptive Enhancement Method of Rehabilitation Training Participation Based on Bayesian Optimization

ZENG Hong; CHEN Qingqing; LI Xiao; ZHANG Jianxi; SONG Aiguo

doi:10.11999/JEIT221122

Volume 45 Issue 8

Aug. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(8): 2770-2779

ZENG Hong, CHEN Qingqing, LI Xiao, ZHANG Jianxi, SONG Aiguo. Research on Adaptive Enhancement Method of Rehabilitation Training Participation Based on Bayesian Optimization[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2770-2779. doi: 10.11999/JEIT221122

Citation:

ZENG Hong, CHEN Qingqing, LI Xiao, ZHANG Jianxi, SONG Aiguo. Research on Adaptive Enhancement Method of Rehabilitation Training Participation Based on Bayesian Optimization[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2770-2779. doi: 10.11999/JEIT221122

Citation:

ZENG Hong, CHEN Qingqing, LI Xiao, ZHANG Jianxi, SONG Aiguo. Research on Adaptive Enhancement Method of Rehabilitation Training Participation Based on Bayesian Optimization[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2770-2779. doi: 10.11999/JEIT221122

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Research on Adaptive Enhancement Method of Rehabilitation Training Participation Based on Bayesian Optimization

doi: 10.11999/JEIT221122

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (62173089)

Received Date: 2022-08-29
Rev Recd Date: 2022-10-30

Available Online: 2022-11-05

Publish Date: 2023-08-21

Abstract

Abstract

For the problems that the existing evaluation index of patient active engagement is complicated to model and training intensity does not match the exercise ability and participation of the participants. A challenge force controller based on Bayesian optimization to enhance adaptively the participation of rehabilitation training is proposed. Firstly, muscle activation based on surface ElectroMyoGram (sEMG) signal is used to evaluate the participant's participation. Secondly, the resistance training mode based on trajectory error amplification is used to train the upper limb, and a comprehensive objective function combining normalized intensity and muscle activation is constructed. Then, Bayesian optimization method is used to update the resistance coefficient and dead zone width of the challenge force field in each training, and optimize the objective function continuously to improve the smoothness of the motion trajectory, while maintaining the participants' participation in training. Finally, 16 healthy subjects are randomly divided into experimental group and control group and trained with their non-handedness to verify the effectiveness of the proposed method. The experimental results show that the muscle activation of the experimental group is 2.51% higher than that of the control group. After training, the improvement of exercise ability in the experimental group is significantly better than that in the control group (59.8% vs 40.7%), which verifies that the adaptive rehabilitation training engagement strategy proposed has more advantages than the fixed parameter strategy.
- Adaptive resistance training,
- Upper-limb rehabilitation,
- Surface ElectroMyoGram (sEMG),
- Muscle activation,
- Bayesian optimization

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References(27)

References

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