Background Manual tuning of robotic lower limb prostheses can be time consuming for both the patient and the clinician and requires in-person visits to a clinic. An automated process for the tuning parameters of a robotic lower limb prosthesis could result in a substantial savings in healthcare resources. A critical challenge to an automated parameter tuning algorithm is the quantification of a person's gait quality. There is not good agreement in the literature of an objective outcome measure

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... hat can rapidly assess gait quality in lower limb amputees. As a first step, we investigated the ability of four common gait quality metrics to detect differences in gait quality: Prosthetic Observational Gait Score (POGS), Gait Deviation Index (GDI), Lateral Sway, and Impulse Asymmetry. Methods We systematically applied four unilateral lower limb joint constraint conditions (baseline/no constraint, ankle constraint, knee constraint, and knee + ankle constraint) to nine able-bodied participants walking at three different speeds (0.7, 0.85 and 1.0 m/s). We calculated and compared the resulting GDI, POGS, Lateral Sway and Impulse Asymmetry scores across all conditions. We performed a 2-way ANOVA statistical analysis to compare sensitivity of the metrics to the various conditions with significance defined by an alpha-level = 0.05. Results The Lateral Sway metric distinguished three joint constraint conditions and two of the speed conditions. Both GDI and POGS were able to distinguish four out of six possible constraint-speed conditions, while Impulse Asymmetry was only able to detect differences between three of the six constraint-speed conditions. Conclusions No single gait quality metric could distinguish every condition. Accordingly, a single metric of gait quality may be inadequate for tuning a prosthesis and therefore multiple metrics and sensors may provide the best results for tuning a prosthesis to the most natural gait pattern for an individual. Compared to the more complex gait measures, Lateral Sway performed well as a simple metric that might easily be operationalized into a real-time parameter tuning controller.