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

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

Figures

Fig. 1

ReWalk™ exoskeleton and measured trunk angles. a Schematic of the ReWalk™ exoskeleton suit. The tri-axial wearable accelerometer attached on the right flank of the robot recorded the body accelerations while the subject walked with the device. Features to score walking quality were computed from the accelerations (see text). b The trunk angles in the frontal (x-y) and lateral (y-z) plane during walking (φ, frontal, blue line; α, lateral, green line). c The power spectral density plots of the trunk angles: the x-value of each maximum corresponds to the frequency of the oscillations in the plane. The step frequency corresponds to the maximum in the x-y plane

Fig. 2

Feature distributions and z-scores across expert subjects. aGaussian probability distributions of the features fitted to the data from expert subjects. Each dot is one subject. Probability values are normalized to 1. b All features are combined into a single score and each expert subject is scored relative to all other experts. The resulting z-score represents how many standard deviations a subject is from the experts mean performance. Z-scores for expert subjects are all within ±2 except for one subject (see text) indicating that their performances were similar. If a patient’s score lies within this range, the patient is considered equivalent to an expert user

Fig. 3

Improvements across training for individual patients. Each plot displays an individual feature and each color denotes a different patient. Data is averaged over 2 sessions (1 block). Solid lines are the least squares linear fit to the data. The green dotted line and the shaded areas indicate mean ±2 standard deviations across expert subjects. Error bars are ±1 standard deviation from the mean

Fig. 4

Patient z-scores during training. Patients’ improvements across training when (left) steps are used as the only feature or (right) all four features are combined to compute the z-score. Data is mean z-score across 2 sessions. The green lines indicate ± 2 standard deviations from the mean z-score of the expert subjects. Patients’ separation increases when the combined z-score is used. Error bars are one standard deviation from the mean

Fig. 5

Patient scores at the end of training. Using multiple features (right) improves patients’ discriminability from the experts (green shaded area indicates ± 2 standard deviations from the mean experts’ score) as compared to using steps as the only feature (left). Data is mean z-score over 2 sessions and error bars are 1 standard deviation from the mean. Asterisk (*) indicates the score is significantly below –2 (Wilcoxon signed rank test, p < 0.001), i.e. the patient has not reached the experts’ level