Quantifying movement fluency in amputees in key functional tasks.

Objective: Sit-to-stand (STS) and sit-to-walk (STW) movements are key functional tasks to master following lower limb (LL) amputation. They are core to activities of daily living, enabling patients to regain independence. Physiotherapists assess movement fluency (hesitation and smoothness) by observing STS and STW however, this relies on extensive experience and lacks objectivity. This study aimed to establish objective, accessible and scalable quantitative measurements of movement fluency in amputees using instrumented movement analysis. Approach: 12 transfemoral amputees (6 limited community and 6 community ambulators) and 6 typical individuals completed walking, STS and STW tasks. Movement fluency was assessed using published algorithms to obtain hesitation and smoothness in STS and STW. Main Results: In STW, hesitation, and smoothness showed statistically significant differences among the three groups. Community ambulators were significantly less hesitant (p = 0.009) and smoother (p = 0.007) than the limited community ambulators, but significantly more hesitant (p < 0.001) and less smooth (p < 0.001) than typical individuals. In STS, the community ambulators were significantly smoother than the limited community ambulators (p < 0.001), but not significantly different from typical individuals (p = 0.68). Community ambulators walked significantly faster than limited community ambulators (p < 0.001) but significantly slower compared to typical individuals (p < 0.001). Significance: Assessment of movement after amputation is not just about walking speed. Other important functional tasks can differentiate amputees and therefore should be considered. An amputee must learn to master both the STS and STW tasks before they can independently walk. Quantifying movement fluency in functional tasks is important to understanding the restoration of function following limb loss, tracking rehabilitation, and classifying amputees. While the study's small sample size reflects its feasibility design, findings support future research with larger cohorts. Subsequent studies should incorporate power calculations to improve generalisability. .