Effectiveness of robotic exoskeletons for improving gait in children with cerebral palsy: A systematic review

Hunt, M.A.; Everaert, Laure; Brown, Mathew; Hatzidimitriadou, Eleni; Desloovere, Kaat

doi:10.1016/j.gaitpost.2021.09.055

Cited by 1 publication

(2 citation statements)

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“…Similarly, the stride time in Figure20(b) was also reduced by 57 time-steps in the corrected intervention.Furthermore, the predicted corrections seem to show an increased range of motion, such as increasing knee flexion, making it more similar to TD gait. This is illustrated in Figures20(c), 20(d), 20(e) where the range of motion of the joint angles increased by 28.4°, 13.97°, and 19.32°respectively, in the predicted corrections compared to the CP gait without intervention.These observations follow desired CP rehabilitation outcomes which include increased mean velocity and improvement in knee extension[1],[5],[9]. While these results are encouraging, they are preliminary observations and the effectiveness of the generated trajectories in enhancing the rehabilitation outcomes (such as reducing metabolic cost, and increasing gait speed), and the comfort of users should be evaluated in a clinical setting.12 VOLUME 4, 2016 This article has been accepted for publication in IEEE Access.…”

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confidence: 55%

“…Promising results have been reported on the effectiveness of exoskeletons in improving CP gait when used in rehabilitation. Benefits include: a reduction in metabolic cost during ambulation, improvements in knee extension and a reduction in crouch gait during stance, increased mean velocity, and increased cadence [1], [5], [9]. There are about fifteen single-joint and multi-joint lower limb exoskeletons primarily designed for children with Cerebral Palsy [8], including HAL [10], P-LEGS [11], Trexo [12], CPWalker [13], EExRoLEG [14] and WAKE-up [15].…”

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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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confidence: 55%