This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.

Original languageEnglish (US)
Pages (from-to)8-25
Number of pages18
JournalJournal of Information Processing Systems
Issue number1
StatePublished - 2017


  • Augmented motor learning and training
  • Multimodal systems and feedback
  • Rehabilitative technologies

ASJC Scopus subject areas

  • Software
  • Information Systems


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