Individualized Gait Generation for Rehabilitation Robots Based on Recurrent Neural Networks

Zhou, Zikang; Liang, Binghong; Huang, Guowei; Liu, Biao; Nong, Jinjin; Xie, Longhan

doi:10.1109/tnsre.2020.3045425

Cited by 21 publications

(9 citation statements)

References 34 publications

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“…However, this may not be the most suited trajectory for the user, since it may not take into account their individual parameters, such as height and limb length, which have all been shown to influence gait [23]. Several studies worked on generating normalised gait cycles based on body parameters [24], [25]; while this approach provides more individualised gait trajectories to follow, it does not take into consideration the stride-to-stride variability during gait nor the asymmetry between the left and right joints.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Models for Stable Gait Prediction Applied to Exoskeleton Reference Trajectories for Children With Cerebral Palsy

2023

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Gait trajectory prediction models have several applications in exoskeleton control; they can be used as feed-forward input to low-level controllers and to generate reference/target trajectories for position-controlled exoskeletons. In our study, we implement four deep learning models (LSTM, FCN, CNN and Transformer) that perform one-step-ahead gait trajectory prediction after training on gait patterns of typically developing children. We propose a methodology that optimises for stability in long-term forecasts, and evaluate the performance of the models on typically developing (TD) and Cerebral Palsy (CP) gait during recursive prediction of 200 time-steps in the future (which may lead to propagation of errors) and in the presence of varying levels of Gaussian noise (1%-5%). Results on TD gait show that the FCN and Transformer, with mean absolute errors (MAEs) for one-step-ahead predictions between 1.17°-1.63°, are the most suitable for the intended application. We also proposed an approach for generating adaptive trajectories that can be used as reference trajectories for position-controlled exoskeletons. Gait patterns from children with Cerebral Palsy were fed into gait trajectory prediction models trained on typically developing gait only, to generate corrective patterns. Preliminary results show that the gait patterns of typically developing children were introduced onto the generated trajectories.

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Section: Introductionmentioning

confidence: 99%

“…Nevertheless, this approach is restricted to hemiparetic individuals and not those who have both limbs affected. Meanwhile, Zhou et al [25] use RNNs to generate normalised gait trajectories based on anthropometrics, as well as gait speed, yet their approach doesn't accommodate for the kinematic asymmetry of the left and right joints.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Models for Stable Gait Prediction Applied to Exoskeleton Reference Trajectories for Children With Cerebral Palsy

2023

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“…Luu et al [15] proposed an individual-specific gait pattern prediction model based on generalized regression neural networks. Zhou et al [16] proposed a method for individualized gait generation based on recurrent neural Networks. Semwal et al [17] [18] presented an approach of modelling joint trajectories of biped locomotion using hybrid automata.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Design of a Six-Bar Lower-Limb Rehabilitation Mechanism Based on Gait Trajectory Prediction

Song

Zhao

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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For patients who need lower-limb kinetism rehabilitation training, this paper proposes an effective datadriven approach seeking the design of 1-degree-of-freedom (DOF) six-bar rehab mechanism through gait prediction by body parameters. First, gait trajectories from 79 healthy volunteers are collected along with their body parameters. Then, the normalized gait samples are clustered and regressed into a limited number of representative trajectories with K-means algorithm, and the cluster index is recorded as the label for each trajectory. Next, a genetic-algorithmoptimized support vector machine method is adopted to establish a classifier for the trajectories, obtaining the correspondence between body parameters and cluster labels of gait trajectories. As a result, once a group of body parameters are input into the classifier, the suitable gait trajectory can be predicted for the specific patient. A GA-BFGS algorithm is developed for 1-DOF six-bar mechanism synthesis and a GUI design software is presented that shows how the data-driven design process is realized. The novelty of this paper is using clustering and prediction technique to accomplish the patient-mechanism matching, so that simple, low-priced 1-DOF mechanisms could be adopted for large number of various patients without expensive customized design for each individual. In the end, a gait rehab device design example is provided, and a prototype device driven by a constant speed motor is presented, which illustrates the feasibility of the proposed method.

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“…Nonetheless, the dataset they used did not include the crucial parameter of gait speed, resulting in limitations in the generated gait profiles for rehabilitation training. [15] proposed a personalized gait pattern generation method based on recurrent neural networks (RNNs), which established a mapping from body parameters and gait parameters to gait patterns, yielding favorable prediction results. However, due to the "black-box" nature of RNNs, the underlying relationship between body parameters and gait patterns remained elusive.…”

Section: Introductionmentioning

confidence: 99%

Personalized lower limb gait reconstruction modeling based on RFA-ProMP

Chunhong,

Kang,

Zhiqin

et al. 2023

Preprint

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To generate personalized gait reference trajectories for lower limb rehabilitation exoskeletons tailored to patients with different body types, we propose a combination of a random forest algorithm (RFA) with an attention mechanism and a mimicry learning method called Probabilistic Movement Primitives (ProMP). This approach aims to establish a personalized lower limb gait reconstruction model. Firstly, the RFA is utilized to map the individual body characteristic parameters and the relationship between gait speed and gait. It predicts the gait characteristics under a specified gait speed. Then, the ProMP is introduced to learn the probability distribution of normal gait data and perform the lower limb gait reconstruction based on the predicted gait characteristics. Experimental results demonstrate that the hip and knee curves reconstructed by RFA-ProMP exhibit the smallest error. Furthermore, the generated reference trajectory aligns with the original probabilistic reference trajectory. This method proves effective in generating personalized gait curves for the lower limbs.

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Individualized Gait Generation for Rehabilitation Robots Based on Recurrent Neural Networks

Cited by 21 publications

References 34 publications

Deep Learning Models for Stable Gait Prediction Applied to Exoskeleton Reference Trajectories for Children With Cerebral Palsy

Deep Learning Models for Stable Gait Prediction Applied to Exoskeleton Reference Trajectories for Children With Cerebral Palsy

Data-Driven Design of a Six-Bar Lower-Limb Rehabilitation Mechanism Based on Gait Trajectory Prediction

Personalized lower limb gait reconstruction modeling based on RFA-ProMP

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