Wearing a safety harness during treadmill walking influences lower extremity kinematics mainly through changes in ankle regularity and local stability

Abstract

Background: Wearing a harness during treadmill walking ensures the subject's safety and is common practice in biomedical engineering research. However, the extent to which such practice influences gait is unknown. This study investigated harness-related changes in gait patterns, as evaluated from lower extremity kinematics during treadmill walking.

Findings: Healthy subjects (n = 10) walked on a treadmill at their preferred speed for 3 minutes with and without wearing a harness (LiteGait®, Mobility Research, Inc.). In the former condition, no weight support was provided to the subjects. Lower extremity kinematics was assessed in the sagittal plane from the mean (meanRoM), standard deviation (SDRoM) and coefficient of variation (CoVRoM) of the hip, knee, and ankle ranges of motion (RoM), as well as from the sample entropy (SampEn) and the largest Lyapunov exponent (LyE) of the joints' angles. Wearing the harness increased the meanRoM of the hip, the SDRoM and the CoVRoM of the knee, and the SampEn and the LyE of the ankle. In particular, the harness effect sizes for both the SampEn and the LyE of the ankle were large, likely reflecting a meaningful decline in the neuromuscular stabilizing control of this joint.

Conclusions: Wearing a harness during treadmill walking marginally influences lower extremity kinematics, resulting in more or less subtle changes in certain kinematic variables. However, in cases where differences in gait patterns would be expressed through modifications in these variables, having subjects walk with a harness may mask or reinforce such differences.

Figure 1

Experimental set-up with a subject fitted into the LiteGait^® system (Mobility Research, Inc., Tempe, AZ). This safety system consists of a lightweight waist harness straps linked to a telescoping metallic arm. The metallic arm was adjusted based on the subject's height so that the system did not provide any body weight support (slack straps). The absence of support was also monitored using the BiSym digital microprocessor of the LiteGait^® system. The harness size (small, medium and large) was selected based on the subject's upper body dimensions. It was tightened using locking straps located in the subject's back based on two criteria: (i) the subject had to feel comfortable wearing the harness while walking, and (ii) the harness had to fit well the waist without moving around it during walking. Reflective markers were attached to anatomical landmarks on the lower extremities, including the anterior and posterior superior iliac spine, lumbosacral joint, greater trochanter of the femur, lateral mid-thigh, front lower thigh, lateral and medial epicondyles of the femur, front mid-shank, lateral lower shank, lateral and medial malleoli, lateral border of the fifth metatarsal head, medial border of the first metatarsal head, lateral and medial processes of the calcaneal tuberosity, heel, and between the second and third metatarsophalangeal joints.

Figure 2

Attractor reconstruction and calculation of the largest Lyapunov exponent (LyE) and sample entropy (SampEn). (A) The original $x{\left(i\right)}_{i=1}^N$ angle time series and the time-delayed copies [x(i+ τ),...,x(i + (m-1)τ)] used for attractor reconstruction. (B) The attractors were composed of sets of m-dimensional vectors v(i) = [x(i), x(i+ τ),...,x(i + (m-1)τ)], with i = 1,...,N - (m-1)τ. The delay τ was obtained from the first minimum of the average mutual information function and the dimension m was selected where the percentage of the global false nearest neighbours approached zero. (C) The LyE algorithm tracked the divergence of nearest neighbours over time, focusing on a reference trajectory with a single nearest neighbour being followed and replaced when its separation L'(t_k) from the reference trajectory becomes large. The new neighbour was chosen to minimize the replacement length L(t_k) and the angular separation θ_k. Once the reference trajectory has gone over the data sample, $LyE={\left(t_M-t_0\right)}^{-1}{\sum }_{k=1}^M\mathsf{\text{log}}\left[L^{\prime }\left(t_k\right)/L\left(t_{k-1}\right)\right]$ was estimated, with M the total number of replacement steps [14,15]. (D) For the SampEn, the first step consisted in calculating $C_i^m\left(\tau ,r\right)={\left(N-m\tau \right)}^{-1}\left(\mathsf{\text{number of }}j\mathsf{\text{ such that }}d\left[v\left(i\right),v\left(j\right)\right]\le r\right)$, where j≠i ranges from 1 to N - mτ, and $d\left[v\left(i\right),v\left(j\right)\right]=\underset{0\le k\le m-1}{\mathsf{\text{max}}}\left(\mid x\left(j+k\right)-x\left(i+k\right)\mid \right)$ is the maximum difference between the scalar components of the vectors [v(i), v(j)]. The distance r was chosen as 0.2× standard deviation of x(i). The density ${\Phi }^m\left(\tau ,r\right)={\left(N-m\tau \right)}^{-1}{\sum }_{i=1}^{N-m\tau }{C}_i^m\left(\tau ,r\right)$ was obtained afterwards. (E) The procedure was repeated for an (m+1)-dimensional attractor, by computing Φ^m+1(τ, r). Finally, the negative log likelihood of the conditional probability that two close vectors (within r) in a m-dimensional attractor remain close in a (m+1)-dimensional attractor was obtained as SampEn = -τ^-1 log (Φ^m+1(τ, r)/Φ^m(τ, r)) [16].

Figure 3

Linear measures from the lower extremity joint range of motions. Mean_RoM: central tendency, SD_RoM: standard deviation, and CoV_RoM: coefficient of variation, for the range of motion (RoM) of all three joints of the lower extremities. LA: left ankle. RA: right ankle. LK: left knee. RK: right knee. LH: left hip. RH: right hip. Error bars denote between-subjects standard error of the mean. Statistically significant effects in the two-way (Side: Right/Left; Harness: With/Without) repeated measures ANOVAs are reported, with p_H and p_S/H corresponding to p-values for the harness main effect and the side×harness interaction effect, respectively. Results from the post-hoc Tukey's HSD analyses are reported in the presence of an interaction effect, with the difference between average values indicated with horizontal bars and p-values.

Figure 4

Nonlinear measures from the lower extremity joint angles. SampEn: Sample Entropy. LyE: largest Lyapunov exponent. LA: left ankle. RA: right ankle. LK: left knee. RK: right knee. LH: left hip. RH: right hip. Statistically significant effects in the two-way (Side: Right/Left; Harness: With/Without) repeated measures ANOVAs are reported, with p_H the p-value for the harness main effect.