Dynamic stability of individuals with transtibial amputation walking in destabilizing environments

Abstract

Lower limb amputation substantially disrupts motor and proprioceptive function. People with lower limb amputation experience considerable impairments in walking ability, including increased fall risk. Understanding the biomechanical aspects of the gait of these patients is crucial in improving their gait function and their quality of life. In the present study, 9 persons with unilateral transtibial amputation and 13 able-bodied controls walked on a large treadmill in a Computer Assisted Rehabilitation Environment (CAREN). While walking, subjects were either not perturbed, or were perturbed either by continuous mediolateral platform movements or by continuous mediolateral movements of the visual scene. Means and standard deviations of both step lengths and step widths increased significantly during both perturbation conditions (all p<0.001) for both groups. Measures of variability, local and orbital dynamic stability of trunk movements likewise exhibited large and highly significant increases during both perturbation conditions (all p<0.001) for both groups. Patients with amputation exhibited greater step width variability (p=0.01) and greater trunk movement variability (p=0.04) during platform perturbations, but did not exhibit greater local or orbital instability than healthy controls for either perturbation conditions. Our findings suggest that, in the absence of other co-morbidities, patients with unilateral transtibial amputation appear to retain sufficient sensory and motor function to maintain overall upper body stability during walking, even when substantially challenged. Additionally, these patients did not appear to rely more heavily on visual feedback to maintain trunk stability during these walking tasks.

Keywords: Amputation; Dynamic stability; Gait; Perturbations; Virtual reality.

Figure 1. Experimental Setup

A: Example photo of a typical person with amputation standing inside the CAREN virtual reality system (Motek, Amsterdam, Netherlands). B: The visual scene used during CAREN trials, depicting a path through a forest with mountains in the background. Both sides the path were lined with 2.4 m tall white posts spaced every 3 m to increase motion parallax (Bardy et al., 1996; McAndrew et al., 2011; 2011).

Figure 2. Stepping Parameters (Step Length and Width) and Stepping Variability

A: Mean step width, B: Mean step length, C: Step width variability, and D: Step length variability. Each graph shows data separately for all healthy controls (AB) and all amputees (TTA) for all three walking conditions. Error bars indicate the appropriate between-subject ± standard error.

Figure 3. Kinematic Variability (MeanSD_pos and MeanSD_vel) of C7

A: Mean standard deviation (MeanSD) of C7 vertebra marker displacements (position). B: MeanSD of C7 vertebra marker velocity. Each graph shows data separately for all healthy controls (AB) and all amputees (TTA) for all three walking conditions. Error bars indicate the appropriate between-subject ± standard error.

Figure 4. Orbital Dynamic Stability (MaxFM) of C7

A: Maximum Floquet multipliers for all Poincaré sections (0–100% of gait cycle) for one typical healthy control subject (AB) and one person with amputation (TTA) for all three different walking conditions (NOP, PLAT< and VIS). B: Mean orbital stability (MaxFM) for all healthy controls (AB) and all amputees (TTA) separately for each walking condition. Error bars indicate the appropriate between-subject ± standard error.

Figure 5. Short-Term Local Dynamic Stability (λ^*

_S) of C7. A: Exemplary mean local divergence curves for one typical healthy control subject (AB) and one person with amputation (TTA) for all three walking conditions (NP, PLAT and VIS). Short-term local divergence exponents (λ^*_S) were computed as the linear slopes of these curves over the region from 0 to 1 stride. B: Mean short-term local stability (λ^*_S) for all healthy controls (AB) and all amputees (TTA) separately for each walking condition. Error bars indicate the appropriate between-subject ± standard error.