Inertial Sensor-Based Gait and Attractor Analysis as Clinical Measurement Tool: Functionality and Sensitivity in Healthy Subjects and Patients With Symptomatic Lumbar Spinal Stenosis

Abstract

Objective: To determine if the attractor for acceleration gait data is similar among healthy persons defining a reference attractor; if exercise-induced changes in the attractor in patients with symptomatic lumbar spinal stenosis (sLSS) are greater than in healthy persons; and if the exercise-induced changes in the attractor are affected by surgical treatment.Methods: Twenty-four healthy subjects and 19 patients with sLSS completed a 6-min walk test (6MWT) on a 30-m walkway. Gait data were collected using inertial sensors (RehaGait®;) capturing 3-dimensional foot accelerations. Attractor analysis was used to quantify changes in low-pass filtered acceleration pattern (δM) and variability (δD) and their combination as attractor-based index (δF = δM* δD) between the first and last 30 m of walking. These parameters were compared within healthy persons and patients with sLSS (preoperatively and 10 weeks and 12 months postoperatively) and between healthy persons and patients with sLSS. The variability in the attractor pattern among healthy persons was assessed as the standard deviation of the individual attractors.Results: The attractor pattern differed greatly among healthy persons. The variability in the attractor between subjects was about three times higher than the variability around the attractor within subject. The change in gait pattern and variability during the 6MWT did not differ significantly in patients with sLSS between baseline and follow-up but differed significantly compared to healthy persons.Discussion: The attractor for acceleration data varied largely among healthy subjects, and hence a reference attractor could not be generated. Moreover, the change in the attractor and its variability during the 6MWT differed between patients and elderly healthy persons but not between repeated assessments. Hence, the attractor based on low-pass filtered signals as used in this study may reflect pathology specific differences in gait characteristics but does not appear to be sufficiently sensitive to serve as outcome parameter of decompression surgery in patients with sLSS.

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

“…For example, parameters obtained using the methods reviewed in the present study were sensitive to disability in post-stroke patients [39], were suggested as outcome parameters for surgical procedures in lumbar spinal stenosis [40], and were useful in assessing the evolution of lower limb amputees during rehabilitation [52]. This technology also provided objective markers of gait fatigability in patients with multiple sclerosis [37,62].…”

“…Other measures have been proposed that do not fit into the previous categories, for example Dandu and colleagues propose dynamic time warping (DTW) and warp scores, outcome measures specifically designed and fitted to the 6MWT to quantify progressive changes to walking patterns [56]. Byrnes and colleagues describe outcome measures starting from the definition of an attractor, which represents the mean cycle of all strides and may reflect specific differences in pathologic gait [40].…”

“…Data processing was performed mostly using Matlab (10 papers, 35.7%, [38,41,[44][45][46]51,58,59,62,63]). Eight papers did not report the software used (28.6%, [37,39,40,48,53,56,60,64]), 4 papers used custom software (14.3%, [47,49,50,57]). The remaining 6 papers (21.4%, [42,43,52,54,55,61]) reported the use of other software.…”

“…Ratio between the length of the real path of the foot in 3D space (including both stride length and width) and stride length of one cycle (% stride length) [39] Strike angle Kinematics Angle between the foot and the ground at heel strike in sagittal plane (deg) [39] Lift off angle Kinematics Angle between the foot and the ground at toe off in sagittal plane (deg) [39] Max angular velocity Kinematics Maximum pitch foot angular velocity during swing phase (deg/s) [39] Hip, knee, ankle joint, and pelvis angles Kinematics Joint angles using Euler angle decomposition (deg) [41,45,54,58] Causality index Other Sum of the number of significant relationships remaining after thresholding the pairwise causality matrix [56] Kernel density estimation (KDE) peak Other Peak of the density functions at 100 equally spaced inertial gait data amplitude values [56] Dynamic time warping (DTW) score Other Summarizes the degree of similarity between sequences following alignment [56] Warp score Other Summarizes the number of "warps", or repetitions of samples, needed to achieve an optimal alignment between sequences. [56,62] Change in acceleration pattern between two conditions (δM) Other Difference between two attractors [40] Change in variability around the attractor (δD) Other Change in acceleration variability between conditions [40] Attractor-based index Other Product of δM and δD [40]…”

The Gaitprint: Identifying Individuals by Their Running Style

“…An individual attractor can be regarded as a mean cycle derived from multiple gait or running cycles during a session, lasting from seconds up to several minutes [19]. Based on the latter, current works in sports rehabilitation propose a highly individual foot acceleration print [20] or gaitprint [21] representing a unique characteristic of a person´s gait. They can be understood as an analogue to a fingerprint, representing a singular pattern of a human finger, which will only match with one particular person.…”