Choosing appropriate prosthetic ankle work to reduce the metabolic cost of individuals with transtibial amputation

Abstract

Powered ankle prostheses have been designed to reduce the energetic burden that individuals with transtibial amputation experience during ambulation. There is an open question regarding how much power the prosthesis should provide, and whether approximating biological ankle kinetics is optimal to reduce the metabolic cost of users. We tested 10 individuals with transtibial amputation walking on a treadmill wearing the BiOM powered ankle prosthesis programmed with 6 different power settings (0-100%), including a prosthetist-chosen setting, chosen to approximate biological ankle kinetics. We measured subjects' metabolic cost of transport (COT) and the BiOM's net ankle work during each condition. Across participants, power settings greater than 50% resulted in lower COT than 0% or 25%. The relationship between power setting, COT, and net ankle work varied considerably between subjects, possibly due to individual adaptation and exploitation of the BiOM's reflexive controller. For all subjects, the best tested power setting was higher than the prosthetist-chosen setting, resulting in a statistically significant and meaningful difference in COT between the best tested and prosthetist-chosen power settings. The results of this study demonstrate that individuals with transtibial amputation may benefit from prescribed prosthetic ankle push-off work that exceeds biological norms.

Figure 1

On average, subjects exhibited a lower cost of transport (COT) as power setting increased. Data points represent the group mean COT (dark blue: n = 9, light blue: n = 8, error bars: ±1 standard deviation). Stars (★) indicate a statistically significant difference between conditions (p < 0.05). For example, 0% had significantly higher COT than 50%, 75%, and 100% power conditions.

Figure 2

We observed large inter-subject variability in the relationship between power setting and the cost of transport (COT). Yet, for all subjects, the best tested power setting ($⁎$) was higher than the prosthetist-chosen power setting (•). Each subject’s best tested power setting was defined as the tested power setting closest to the minimum of the best fit cubic polynomial. This method was chosen to accommodate the noisy breath-by-breath measurements of metabolic cost and the sparse sampling of the parameter space. The third-order polynomials (dashed gray line) are presented for reference.

Figure 3

(A) On average, subjects walking with their best tested power setting had significantly lower cost of transport (COT) compared to walking with the prosthetist-chosen power setting (p < 0.001). The mean prosthetist-chosen power setting was 41.6% ± 8.7%; the mean best tested setting was 86.1% ± 13.2%. The star (★) indicates a significant difference in the magnitude of COT between the prosthetist chosen and best tested power settings. Error bars represent ±1 standard deviation in COT (vertical) and power setting (horizontal). (B) The corresponding mean net ankle work for the prosthetist-chosen and best tested conditions were 0.11 ± 0.06 J/kg and 0.24 ± 0.08 J/kg, respectively.

Figure 4

We found a linear correlation (Pearson’s r = 0.90 ± 0.07) between power setting and net ankle work for individual subjects. However, not all subjects exhibited a monotonically increasing relationship, and we observed a plateau in ankle work past the 50% power setting for some subjects. Filled circles (•) indicate the prosthetist-chosen power settings and corresponding net ankle work. Asterisks ($⁎$) indicate the subjects’ best tested power settings. Net ankle work for each condition was calculated as the mean of the ankle work from the last 30 steps for all conditions except Subject 4’s 75% condition, which was the average of 5 steps.

Figure 5

With all subjects pooled, there was a moderate linear correlation between cost of transport (COT) and net ankle work (Pearson’s r = −0.55, p < 0.0001). The best fit linear model (dashed line) resulted in R² = 0.30. Individually, subjects exhibited a stronger linear relationship between COT and net ankle work (r = −0.82 ± 0.15, R² = 0.69 ± 0.22). The majority of subjects’ best tested power settings corresponded to their maximum net ankle work. Starting from the left, colored lines connect increasing power settings for individual subjects. Filled circles (•) indicate the prosthetist-chosen power settings. Asterisks ($⁎$) indicate the best tested power settings. Net ankle work for each condition was calculated as the mean of the ankle work from the last 30 steps for all conditions except Subject 4’s 75% condition, which was the average of 5 steps.

Figure 6

An example of the real-time display used to tune the actuation settings of the BiOM prosthesis (adapted from the BiOM user’s manual). As the subject walks, a dot appears on the screen that indicates the work done by the prosthesis at the user’s chosen walking speed. The prosthetist modifies the power setting of the device until the dots fall into the ±95% confidence interval for healthy biological net ankle work (dashed lines) and the patient is satisfied with the device performance.

Figure 7

In this experiment, 10 transtibial amputees walked on a treadmill wearing the BiOM powered ankle prosthesis with 6 different power settings: 0%, 25%, 50%, 75%, 100%, and the parameter setting chosen by the prosthetist during fitting (prosthetist-chosen, PC). We measured metabolic energy expenditure using indirect calorimetry and calculated the cost of transport (COT) from metabolic cost and treadmill speed. Treadmill belt speed was normalized to leg length, and determined using a Froude number of 0.16, which corresponds to the typical preferred walking speed for individuals with transtibial amputation. In our experiment, subjects walked at an average speed of 1.21 ± 0.07 m/s. We also collected net ankle work from the BiOM for each step during all conditions.