Body Segment Mechanical Signal Contributions to Continuous Prediction of Locomotor Transitions Performed under Varying Anticipation

Mahdieh Kazemimoghadam, Nicholas P. Fey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A reliable, flexible and simple source of information would benefit robust handling of predicting locomotion modes for assistive device control (e.g., prostheses). However, to date, the sources of mechanical signals have been mainly limited to the information acquired through embedded sensors in the device. It remains unclear whether biomechanical signals from unaffected or less affected locations (e.g., contralateral side or upper body) would be reliable sources of information. Furthermore, the possible influence of the anticipatory state of the task on recognition accuracy, emphasizes the need to identify reliable data sources for both anticipated and unanticipated tasks. Here, accelerographic and gyroscopic signals from the leading leg, trailing leg, trunk-pelvis, and their fusion were compared with respect to their ability to predict changes of direction (cuts), cut-to-stair transitions, and level-ground walking performed under varied task anticipation. We hypothesized that fusion of lower-and upper-body signals would provide better accuracy than unilateral information (i.e., trailing/leading leg), and recognition accuracy would diminish when tasks were unanticipated. Surprisingly, signal fusion appeared not to be advantageous to unilateral signals. Leading and trailing leg data demonstrated statistically identical performances, and trunk-pelvis signals showed significantly (α=0.05) inferior performance relative to unilateral data. While anticipated tasks were accurately predicted (≥90%) even as early as 500 ms prior to entering each locomotor transition, in unanticipated tasks, similar accuracy rates were achieved only after the mid-swing of the transitioning leg. The findings could provide insight into flexible, yet, dependable sensor sets for intent recognition frameworks during varying user cognitive states.

Original languageEnglish (US)
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5331-5334
Number of pages4
ISBN (Electronic)9781538613115
DOIs
StatePublished - Jul 2019
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: Jul 23 2019Jul 27 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
CountryGermany
CityBerlin
Period7/23/197/27/19

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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    Kazemimoghadam, M., & Fey, N. P. (2019). Body Segment Mechanical Signal Contributions to Continuous Prediction of Locomotor Transitions Performed under Varying Anticipation. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 5331-5334). [8856425] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8856425