Changes in Dynamic Mean Ankle Moment Arm in Unimpaired Walking Across Speeds, Ramps, and Stairs

Understanding the natural biomechanics of walking at different speeds and activities is crucial to develop effective assistive devices for persons with lower-limb impairments. While continuous measures such as joint angle and moment are well-suited for biomimetic control of robotic systems, whole-stride summary metrics are useful for describing changes across behaviors and for designing and controlling passive and semi-active devices. Dynamic mean ankle moment arm (DMAMA) is a whole-stride measure representing the moment arm of the ground reaction impulse about the ankle joint-effectively, how "forefoot-dominated" or "hindfoot-dominated" a movement is. DMAMA was developed as a target and performance metric for semi-active devices that adjust once per stride. However, for implementation in this application, DMAMA must be characterized across various activities in unimpaired individuals. In our study, unimpaired participants walked at "slow," "normal," and "fast" self-selected speeds on level ground and at a normal self-selected speed while ascending and descending stairs and a 5-degree incline ramp. DMAMA measured from these activities displayed a borderline-significant negative sensitivity to walking speed, a significant positive sensitivity to ground incline, and a significant decrease when ascending stairs compared to descending. The data suggested a nonlinear relationship between DMAMA and walking speed; half of the participants had the highest average DMAMA at their "normal" speed. Our findings suggest that DMAMA varies substantially across activities, and thus, matching DMAMA could be a valuable metric to consider when designing biomimetic assistive lower-limb devices.

Adapting Semi-Active Prostheses to Real-World Movements: Sensing and Controlling the Dynamic Mean Ankle Moment Arm with a Variable-Stiffness Foot on Ramps and Stairs

(1) Background: Semi-active prosthetic feet can provide adaptation in different circumstances, enabling greater function with less weight and complexity than fully powered prostheses. However, determining how to control semi-active devices is still a challenge. The dynamic mean ankle moment arm (DMAMA) provides a suitable biomechanical metric, as its simplicity matches that of a semi-active device. However, it is unknown how stiffness and locomotion modes affect DMAMA, which is necessary to create closed-loop controllers for semi-active devices. In this work, we develop a method to use only a prosthesis-embedded load sensor to measure DMAMA and classify locomotion modes, with the goal of achieving mode-dependent, closed-loop control of DMAMA using a variable-stiffness prosthesis. We study how stiffness and ground incline affect the DMAMA, and we establish the feasibility of classifying locomotion modes based exclusively on the load sensor. (2) Methods: Human subjects walked on level ground, ramps, and stairs while wearing a variable-stiffness prosthesis in low-, medium-, and high-stiffness settings. We computed DMAMA from sagittal load sensor data and prosthesis geometric measurements. We used linear mixed-effects models to determine subject-independent and subject-dependent sensitivity of DMAMA to incline and stiffness. We also used a machine learning model to classify locomotion modes using only the load sensor. (3) Results: We found a positive linear sensitivity of DMAMA to stiffness on ramps and level ground. Additionally, we found a positive linear sensitivity of DMAMA to ground slope in the low- and medium-stiffness conditions and a negative interaction effect between slope and stiffness. Considerable variability suggests that applications of DMAMA as a control input should look at the running average over several strides. To examine the efficacy of real-time DMAMA-based control systems, we used a machine learning model to classify locomotion modes using only the load sensor. The classifier achieved over 95% accuracy. (4) Conclusions: Based on these findings, DMAMA has potential for use as a closed-loop control input to adapt semi-active prostheses to different locomotion modes.