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

Published online by Cambridge University Press:  28 November 2016

Eunsuk Chong and
F. C. Park
Eunsuk Chong
Affiliation:
Robotics Laboratory, Seoul National University, Seoul 08826, Korea E-mail: bear3498@snu.ac.kr
F. C. Park*
Affiliation:
Robotics Laboratory, Seoul National University, Seoul 08826, Korea E-mail: bear3498@snu.ac.kr
*
*Corresponding author. E-mail: fcp@snu.ac.kr
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Summary

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We propose a novel class of unsupervised learning-based algorithms that extend the conditional restricted Boltzmann machine to predict, in real-time, a lower limb exoskeleton wearer's intended movement type and future trajectory. During training, our algorithm automatically clusters unlabeled exoskeletal measurement data into movement types. Our predictor then takes as input a short time series of measurements, and outputs in real-time both the movement type and the forward trajectory time series. Physical experiments with a prototype exoskeleton demonstrate that our method more accurately and stably predicts both movement type and the forward trajectory compared to existing methods.

Keywords

Lower limb exoskeletonUnsupervised learningConditional restricted Boltzmann machineMovement predictionTrajectory generation

Information

Type
Articles
Information
Robotica , Volume 35 , Issue 11 , November 2017, pp. 2177 - 2200
Copyright
Copyright © Cambridge University Press 2016 

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