Accelerometry-enabled measurement of walking performance with a robotic exoskeleton: a pilot study. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Lonini, Luca; Shawen, Nicholas; Scanlan, Kathleen; Rymer, William; Kording, Konrad; Jayaraman, Arun
• Is Part Of: Journal of neuroengineering and rehabilitation. Volume 13:Issue 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-12
• Subjects: Spinal cord injury (SCI) -- Lower limb exoskeleton -- Outcome measure -- Walking skills -- Paraplegia -- Wearable accelerometer -- Naive Bayes; Nervous system -- Diseases -- Patients -- Rehabilitation -- Periodicals; Nervous system -- Wounds and injuries -- Rehabilitation -- Periodicals; Biomedical engineering; 616.8043005
• DOI: 10.1186/s12984-016-0142-9
• Languages: English
• ISSNs: 1743-0003

Abstract

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.