Deep Generative Models with Data Augmentation to Learn Robust Representations of Movement Intention for Powered Leg Prostheses

Blair Hu*, Ann M. Simon, Levi Hargrove

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Intent recognition is a data-driven alternative to expert-crafted rules for triggering transitions between pre-programmed activity modes of a powered leg prosthesis. Movement-related signals from prosthesis sensors detected prior to movement completion are used to predict the upcoming activity. Usually, training data comprised of labeled examples of each activity are necessary; however, the process of collecting a sufficiently large and rich training dataset from an amputee population is tedious. In addition, covariate shift can have detrimental effects on a controller's prediction accuracy if the classifier's learned representation of movement intention is not robust enough. Our objective was to develop and evaluate techniques to learn robust representations of movement intention using data augmentation and deep neural networks. In an offline analysis of data collected from four amputee subjects across three days each, we demonstrate that our approach produced realistic synthetic sensor data that helped reduce error rates when training and testing on different days and different users. Our novel approach introduces an effective and generalizable strategy for augmenting wearable robotics sensor data, challenging a pre-existing notion that rehabilitation robotics can only derive limited benefit from state-of-the-art deep learning techniques typically requiring more vast amounts of data.

Original languageEnglish (US)
Article number8894130
Pages (from-to)267-278
Number of pages12
JournalIEEE Transactions on Medical Robotics and Bionics
Issue number4
StatePublished - Nov 2019
Externally publishedYes


  • Data augmentation
  • deep learning
  • intent recognition
  • prosthesis control
  • signal processing

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence
  • Human-Computer Interaction
  • Control and Optimization
  • Biomedical Engineering


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