An adaptive gait learning strategy for lower limb exoskeleton robot

Chen, Chunjie; Liu, Duxin; Wang, Xuesong; Wang, Can; Wang, Xinyu

doi:10.1109/rcar.2017.8311855

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…We can identify motion patterns in individuals, even when the movements are not part of the "correct" mode sequence in a specified task. Secondly, we do not require pre-labeling of phase transitions in the training data, which much prior work has relied on [8], [21], and are able to train our segmentation algorithm on small amounts of data (experiments in this study used 20second sets of walking data sampled at 500Hz). This means that gait partitions could be generated for individual patients and updated continually as their impairment changes over time.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Much work in this domain has been done in the context of gait analysis and gait phase identification. Real-time, closedform expressions for distinguishing between hybrid dynamic modes can be obtained using supervised machine learning techniques, such as neural networks [7], [8], Hidden Markov Models [9], or Gaussian Mixture Models [10] from selected *This work was supported by the National Science Foundation (NSF) under grant CBET-1637764. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.…”

Section: Introductionmentioning

confidence: 99%

“…Much work in this domain has been done in the context of gait analysis and gait phase identification. Real-time, closedform expressions for distinguishing between hybrid dynamic modes can be obtained using supervised machine learning techniques, such as neural networks [7], [8], Hidden Markov Models [9], or Gaussian Mixture Models [10] from selected sensory information, such as ground contact forces, joint positions, inertial data, or muscle signals. Most of these studies rely heavily on ankle data for phase identification, which is often not present in lower-limb assistive devices, such as the one used in this study.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Gait Segmentation for Walking Assistance in a Lower-Limb Assistive Device

Kalinowska¹,

Berrueta²,

Zoss

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Hybrid systems, such as bipedal walkers, are challenging to control because of discontinuities in their nonlinear dynamics. Little can be predicted about the systems' evolution without modeling the guard conditions that govern transitions between hybrid modes, so even systems with reliable state sensing can be difficult to control. We propose an algorithm that allows for determining the hybrid mode of a system in real-time using data-driven analysis. The algorithm is used with data-driven dynamics identification to enable model predictive control based entirely on data. Two examples-a simulated hopper and experimental data from a bipedal walker-are used. In the context of the first example, we are able to closely approximate the dynamics of a hybrid SLIP model and then successfully use them for control in simulation. In the second example, we demonstrate gait partitioning of human walking data, accurately differentiating between stance and swing, as well as selected subphases of swing. We identify contact events, such as heel strike and toe-off, without a contact sensor using only kinematics data from the knee and hip joints, which could be particularly useful in providing online assistance during walking. Our algorithm does not assume a predefined gait structure or gait phase transitions, lending itself to segmentation of both healthy and pathological gaits. With this flexibility, impairment-specific rehabilitation strategies or assistance could be designed.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Much work in this domain has been done in the context of gait analysis and gait phase identification. Real-time, closedform expressions for distinguishing between hybrid dynamic modes can be obtained using supervised machine learning techniques, such as neural networks [7], [8], Hidden Markov Models [9], or Gaussian Mixture Models [10] from selected sensory information, such as ground contact forces, joint positions, inertial data, or muscle signals. Most of these studies rely heavily on ankle data for phase identification, which is often not present in lower-limb assistive devices, such as the one used in this study.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Gait Segmentation for Walking Assistance in a Lower-Limb Assistive Device

Kalinowska¹,

Berrueta²,

Zoss

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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“…To address the above problems, this study proposes a SVM gait prediction algorithm based on particle swarm optimization (PSO) [8] to improve the accuracy of gait prediction. The research goal is to achieve gait prediction accuracy of more than 95%.…”

Section: Introductionmentioning

confidence: 99%

Gait Prediction Analysis Based on Particle Swarm Optimization Algorithm Support Vector Machine

Sui,

Yang,

Zhang

et al. 2024

Advances in Transdisciplinary Engineering

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Gait is an emerging biometric trait, and gait prediction is a process that enables the prediction of the motion state of the human lower limb based on image features, joint angles, or human kinematic metrics. This article proposes a support vector machine (SVM) model based on particle swarm optimization (PSO) algorithm. Specifically, the particle swarm optimization (PSO) algorithm is used to optimize the penalty factor and kernel function parameters of the support vector machine (SVM). Meanwhile, by utilizing MediaPipe in conjunction with a motion camera within a neural network application, it is possible to generate lower limb keypoint features. This can be used to obtain datasets for training and testing models. The experimental prediction results show that the model has an accuracy of 96.29% for the lower limb gait prediction results, which has high prediction accuracy and can be used in the control scenarios of lower limb exoskeleton robots.

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“…The aim of the present work consists of the analysis of a phase identification performed using neural networks, using the data from (Camardella et al, 2021). This is motivated by the perspective that machine learning will be an important tool in managing the complexity of human robot interaction (HRI) in assistive devices (Argall, 2013;Broad et al, 2017;Chen et al, 2017;Kurkin et al, 2018;Na et al, 2019). Because the learned estimator of the gait phase is affecting the system itself after the learning through the control system, a slight decrease in performance can be expected.…”

Section: Introductionmentioning

confidence: 99%

Identification of Gait Phases with Neural Networks for Smooth Transparent Control of a Lower Limb Exoskeleton

Lippi¹,

Camardella²,

Filippeschi³

et al. 2021

Preprint

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Lower limbs exoskeletons provide assistance during standing, squatting, and walking. Gait dynamics, in particular, implies a change in the configuration of the device in terms of contact points, actuation, and system dynamics in general. In order to provide a comfortable experience and maximize performance, the exoskeleton should be controlled smoothly and in a transparent way, which means respectively, minimizing the interaction forces with the user and jerky behavior due to transitions between different configurations. A previous study showed that a smooth control of the exoskeleton can be achieved using a gait phase segmentation based on joint kinematics. Such a segmentation system can be implemented as linear regression and should be personalized for the user after a calibration procedure. In this work, a nonlinear segmentation function based on neural networks is implemented and compared with linear regression. An on-line implementation is then proposed and tested with a subject.

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An adaptive gait learning strategy for lower limb exoskeleton robot

Cited by 8 publications

References 18 publications

Data-Driven Gait Segmentation for Walking Assistance in a Lower-Limb Assistive Device

Data-Driven Gait Segmentation for Walking Assistance in a Lower-Limb Assistive Device

Gait Prediction Analysis Based on Particle Swarm Optimization Algorithm Support Vector Machine

Identification of Gait Phases with Neural Networks for Smooth Transparent Control of a Lower Limb Exoskeleton

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