Accelerometry-enabled measurement of walking performance with a robotic exoskeleton: a pilot study
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RESEARCH
Open Access
Accelerometry-enabled measurement of walking performance with a robotic exoskeleton: a pilot study Luca Lonini1,2, Nicholas Shawen1, Kathleen Scanlan1, William Z. Rymer3, Konrad P. Kording2,3 and Arun Jayaraman1,2*
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. Keywords: Spinal cord injury (SCI), Lower limb exoskeleton, Outcome measure, Walking skills, Paraplegia, Wearable accelerometer, Naive Bayes
Background Clinical scores of walking ability are crucial in many areas of physical rehabilitation to assess the efficacy of a therapeutic intervention or an assistive device, as well as to discriminate the ability between different patients [1, 2]. One domain of interest is evaluating * Correspondence: [email protected] 1 Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Rehabilitation Institute of Chicago, 345 E Superior St, Chicago, IL 60611, USA 2 Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611, USA Full list of author information is available at the end of the article
functional ambulation in individuals who suffered a spinal cord injury (SCI). Even though many outcome measures target the SCI population [3, 4], currently there exist no specific measures targeting the ability of a patient to use a lower limb r
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