7 research outputs found

    Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

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    The rate of development of robotic technologies has been meteoric, as a result of compounded advancements in hardware and software. Amongst these robotic technologies are active exoskeletons and orthoses, used in the assistive and rehabilitative fields. Artificial intelligence techniques are increasingly being utilised in gait analysis and prediction. This review paper systematically explores the current use of intelligent algorithms in gait analysis for robotic control, specifically the control of active lower limb exoskeletons and orthoses. Two databases, IEEE and Scopus, were screened for papers published between 1989 to May 2020. 41 papers met the eligibility criteria and were included in this review. 66.7% of the identified studies used classification models for the classification of gait phases and locomotion modes. Meanwhile, 33.3% implemented regression models for the estimation/prediction of kinematic parameters such as joint angles and trajectories, and kinetic parameters such as moments and torques. Deep learning algorithms have been deployed in ∼15% of the machine learning implementations. Other methodological parameters were reviewed, such as the sensor selection and the sample sizes used for training the models

    Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation

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    The coordinated rehabilitation of the upper limb is important for the recovery of the daily living abilities of stroke patients. However, the guidance of the joint coordination model is generally lacking in the current robot-assisted rehabilitation. Modular robots with soft joints can assist patients to perform coordinated training with safety and compliance. In this study, a novel coordinated path planning and impedance control method is proposed for the modular exoskeleton elbow–wrist rehabilitation robot driven by pneumatic artificial muscles (PAMs). A convolutional neural network-long short-term memory (CNN-LSTM) model is established to describe the coordination relationship of the upper limb joints, so as to generate adaptive trajectories conformed to the coordination laws. Guided by the planned trajectory, an impedance adjustment strategy is proposed to realize active training within a virtual coordinated tunnel to achieve the robot-assisted upper limb coordinated training. The experimental results showed that the CNN-LSTM hybrid neural network can effectively quantify the coordinated relationship between the upper limb joints, and the impedance control method ensures that the robotic assistance path is always in the virtual coordination tunnel, which can improve the movement coordination of the patient and enhance the rehabilitation effectiveness

    Deep learning for gait prediction: an application to exoskeletons for children with neurological disorders

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    Cerebral Palsy, a non-progressive neurological disorder, is a lifelong condition. While it has no cure, clinical intervention aims to minimise the impact of the disability on individuals' lives. Wearable robotic devices, like exoskeletons, have been rapidly advancing and proving to be effective in rehabilitating individuals with gait pathologies. The utilization of artificial intelligence (AI) algorithms in controlling exoskeletons, particularly at the supervisory level, has emerged as a valuable approach. These algorithms rely on input from onboard sensors to predict gait phase, user intention, or joint kinematics. Using AI to improve the control of robotic devices not only enhances human-robot interaction but also has the potential to improve user comfort and functional outcomes of rehabilitation, and reduce accidents and injuries. In this research study, a comprehensive systematic literature review is conducted, exploring the various applications of AI in lower-limb robotic control. This review focuses on methodological parameters such as sensor usage, training demographics, sample size, and types of models while identifying gaps in the existing literature. Building on the findings of the review, subsequent research leveraged the power of deep learning to predict gait trajectories for the application of rehabilitative exoskeleton control. This study addresses a gap in the existing literature by focusing on predicting pathological gait trajectories, which exhibit higher inter- and intra-subject variability compared to the gait of healthy individuals. The research focused on the gait of children with neurological disorders, particularly Cerebral Palsy, as they stand to benefit greatly from rehabilitative exoskeletons. State-of-the-art deep learning algorithms, including transformers, fully connected neural networks, convolutional neural networks, and long short-term memory networks, were implemented for gait trajectory prediction. This research presents findings on the performance of these models for short-term and long-term recursive predictions, the impact of varying input and output window sizes on prediction errors, the effect of adding variable levels of Gaussian noise, and the robustness of the models in predicting gait at speeds within and outside the speed range of the training set. Moreover, the research outlines a methodology for optimising the stability of long-term forecasts and provides a comparative analysis of gait trajectory forecasting for typically developing children and children with Cerebral Palsy. A novel approach to generating adaptive trajectories for children with Cerebral Palsy, which can serve as reference trajectories for position-controlled exoskeletons, is also presented

    Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

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    Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017–2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed

    Physical Diagnosis and Rehabilitation Technologies

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    The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

    Prediction of Limb Joint Angles Based on Multi-Source Signals by GS-GRNN for Exoskeleton Wearer

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    To enable exoskeleton wearers to walk on level ground, estimation of lower limb movement is particularly indispensable. In fact, it allows the exoskeleton to follow the human movement in real time. In this paper, the general regression neural network optimized by golden section algorithm (GS-GRNN) is used to realize prediction of the human lower limb joint angle. The human body hip joint angle and the surface electromyographic (sEMG) signals of the thigh muscles are taken as the inputs of a neural network to predict joint angles of lower limbs. To improve the prediction accuracy in different gait phases, the plantar pressure signals are also added into the input. After that, the error between the prediction result and the actual data decreases significantly. Finally, compared with the prediction result of the BP neural network, GRNN shows splendid prediction performance for its less processing time and higher prediction accuracy
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