Human performs bipedal gait with synchronized arm swing. Apart from the observation that arm movement during gait is the outcome of a mechanical and energetic optimization, the synergetic upper and lower limb movement...Human performs bipedal gait with synchronized arm swing. Apart from the observation that arm movement during gait is the outcome of a mechanical and energetic optimization, the synergetic upper and lower limb movement during gait is a neutrally coordinated motor output, that is, the interlimb movement is neutrally coupled. Patients with injuries to the central nervous system demonstrate the interlimb neural coupling.Researches on central pattern generator and the reflex studies reveal that the interlimb neural coupling is a quadrupedal heritage. Based on the theory of the interlimb neural coupling, both the upper and lower limbs should be practiced synchronously during regular gait training to promote walking rehabilitation for patients with gait disorders. Further development of a gait robotic system with synchronized arm swing is required to test the clinical application of the neural coupling in gait restoration.展开更多
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 syn...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 FSSeq2Seq 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.展开更多
文摘Human performs bipedal gait with synchronized arm swing. Apart from the observation that arm movement during gait is the outcome of a mechanical and energetic optimization, the synergetic upper and lower limb movement during gait is a neutrally coordinated motor output, that is, the interlimb movement is neutrally coupled. Patients with injuries to the central nervous system demonstrate the interlimb neural coupling.Researches on central pattern generator and the reflex studies reveal that the interlimb neural coupling is a quadrupedal heritage. Based on the theory of the interlimb neural coupling, both the upper and lower limbs should be practiced synchronously during regular gait training to promote walking rehabilitation for patients with gait disorders. Further development of a gait robotic system with synchronized arm swing is required to test the clinical application of the neural coupling in gait restoration.
基金supported by the National Natural Science Foundation of China(nos.32360196,and 32160204)the Key R&D Project of Hainan Province(grant nos.ZDYF2022SHFZ302 and ZDYF2022SHFZ275)+6 种基金the Major Science and Technology Projects of Hainan Province(grant no.ZDKJ2021032)Hainan Province Clinical Medical Center(no.0202067)Science,Technology,and Innovation Commission of Shenzhen Municipality(STICproject no.SGDX20220530111005036)Basic and Applied Basic Research Fund of Guangdong Province:Regional Joint Fund Project Youth Fund(project no.2021A1515110356)Shenzhen Science and Technology Plan Project(project no.JCYJ20220818101407016)by the Project of Sanya Yazhou Bay Science and Technology City(no.SCKJJYRC-2023-27).
文摘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 FSSeq2Seq 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.
