Movement prediction for a lower limb exoskeleton using a conditional restricted Boltzmann machine

Chong, Eugene; Park, Frank Chongwoo

doi:10.1017/s0263574716000795

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…A GRBM can be regarded as a high-order RBM because multiplication can be performed among three units or more, with the weight matrix being a 3-way tensor among the input, hidden, and output layers. CRBMs are used in motion recognition, multiclass simultaneous labeling, and nonlinear time-sequence analysis; recently, interesting results have been achieved for gait recognition [41].…”

Section: Conditional Restricted Boltzmann Machinesmentioning

confidence: 99%

A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning

et al. 2021

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For more than a century, the methods for data representation and the exploration of the intrinsic structures of data have developed remarkably and consist of supervised and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high and the data can come in messy, incomplete, unlabeled, or corrupted forms. Consequently, discovering the hidden structure buried inside such data becomes highly challenging. From this perspective, exploratory data analysis plays a substantial role in learning the hidden structures that encompass the significant features of the data in an ordered manner by extracting patterns and testing hypotheses to identify anomalies. Unsupervised generative learning models are a class of machine learning models characterized by their potential to reduce the dimensionality, discover the exploratory factors, and learn representations without any predefined labels; moreover, such models can generate the data from the reduced factors’ domain. The beginner researchers can find in this survey the recent unsupervised generative learning models for the purpose of data exploration and learning representations; specifically, this article covers three families of methods based on their usage in the era of big data: blind source separation, manifold learning, and neural networks, from shallow to deep architectures.

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Section: Conditional Restricted Boltzmann Machinesmentioning

confidence: 99%

A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning

et al. 2021

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“…Therefore, the device should predict the operator's trajectory. Finally, it is crucial to generate correct actuator signals for intended trajectories in real time [8].…”

Section: B Movement Prediction In Exoskeleton Robotsmentioning

confidence: 99%

Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms

Jiang

Zhang

et al. 2019

Eng. Technol. Appl. Sci. Res.

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Exoskeletons are wearable devices for enhancing human physical performance and for studying actions and movements. They are worn on the body for additional power and load-carrying capacity. Exoskeletons can be controlled using signals from the muscles. In recent years, gait analysis has attracted increasing attention from fields such as animation, athletic performance analysis, and robotics. Gait patterns are unique, and each individual has his or her own distinct gait pattern characteristics. Gait analysis can monitor activity in sensitive areas. This paper uses various machine learning algorithms to predict the activity of subjects using exoskeletons. Here, localization data from the UIC machine learning repository are used to recognize activities with gait positions. The study also compares five machine learning methods and examines their efficiency and accuracy in activity prediction for three different subjects. The results for the various machine learning methods along with efficiency and accuracy results are discussed.

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“…The ML algorithms are a subfield of Artificial Intelligence (AI) concerned with the establishment of computer programmes that learn patterns from data [ 19 ]. Computational techniques related to ML have been successful in solving several aspects of biomechanics gait research problems [ 20 , 21 ], such as the gait classification [ 22 – 24 ], joint angle prediction [ 25 ] and energy expenditure minimisation in lower limb exoskeletons [ 26 ]. Tanghe et al .…”

Section: Introductionmentioning

confidence: 99%

Prediction of gait trajectories based on the Long Short Term Memory neural networks

et al. 2021

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The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82–5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.

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Movement prediction for a lower limb exoskeleton using a conditional restricted Boltzmann machine

Cited by 13 publications

References 30 publications

A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning

A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning

Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms

Prediction of gait trajectories based on the Long Short Term Memory neural networks

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