Accelerometry-enabled measurement of walking performance with a robotic exoskeleton: A pilot study

Luca Lonini, Nicholas Shawen, Kathleen Scanlan, William Z. Rymer, Konrad P. Kording, Arun Jayaraman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Background: Clinical scores for evaluating walking skills with lower limb exoskeletons are often based on a single variable, such as distance walked or speed, even in cases where a host of features are measured. We investigated how to combine multiple features such that the resulting score has high discriminatory power, in particular with few patients. A new score is introduced that allows quantifying the walking ability of patients with spinal cord injury when using a powered exoskeleton. Methods: Four spinal cord injury patients were trained to walk over ground with the ReWalk™ exoskeleton. Body accelerations during use of the device were recorded by a wearable accelerometer and 4 features to evaluate walking skills were computed. The new score is the Gaussian naïve Bayes surprise, which evaluates patients relative to the features' distribution measured in 7 expert users of the ReWalk™. We compared our score based on all the features with a standard outcome measure, which is based on number of steps only. Results: All 4 patients improved over the course of training, as their scores trended towards the expert users' scores. The combined score (Gaussian naïve surprise) was considerably more discriminative than the one using only walked distance (steps). At the end of training, 3 out of 4 patients were significantly different from the experts, according to the combined score (p <.001, Wilcoxon Signed-Rank Test). In contrast, all but one patient were scored as experts when number of steps was the only feature. Conclusion: Integrating multiple features could provide a more robust metric to measure patients' skills while they learn to walk with a robotic exoskeleton. Testing this approach with other features and more subjects remains as future work.

Original languageEnglish (US)
Article number35
JournalJournal of neuroengineering and rehabilitation
Volume13
Issue number1
DOIs
StatePublished - 2016

Keywords

  • Lower limb exoskeleton
  • Naive Bayes
  • Outcome measure
  • Paraplegia
  • Spinal cord injury (SCI)
  • Walking skills
  • Wearable accelerometer

ASJC Scopus subject areas

  • Rehabilitation
  • Health Informatics

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