Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait

Abstract

Background: The step-by-step determination of the spatio-temporal parameters of gait is clinically relevant since it provides an estimation of the variability of specific gait patterns associated with frequent geriatric syndromes. In recent years, several methods, based on the use of magneto-inertial units (MIMUs), have been developed for the step-by-step estimation of the gait temporal parameters. However, most of them were applied to the gait of healthy subjects and/or of a single pathologic population. Moreover, spatial parameters in pathologic populations have been rarely estimated step-by-step using MIMUs. The validity of clinically suitable MIMU-based methods for the estimation of spatio-temporal parameters is therefore still an open issue. The aim of this study was to propose and validate a method for the determination of both temporal and spatial parameters that could be applied to normal and heavily compromised gait patterns.

Methods: Two MIMUs were attached above each subject's ankles. An instrumented gait mat was used as gold standard. Gait data were acquired from ten hemiparetic subjects, ten choreic subjects, ten subjects with Parkinson's disease and ten healthy older adults walking at two different gait speeds. The method detects gait events (GEs) taking advantage of the cyclic nature of gait and exploiting some lower limb invariant kinematic characteristics. A combination of a MIMU axes realignment along the direction of progression and of an optimally filtered direct and reverse integration is used to determine the stride length.

Results: Over the 4,514 gait cycles analyzed, neither missed nor extra GEs were generated. The errors in identifying both initial and final contact at comfortable speed ranged between 0 and 11 ms for the different groups analyzed. The stride length was estimated for all subjects with less than 3% error.

Conclusions: The proposed method is apparently extremely robust since gait speed did not substantially affect its performance and both missed and extra GEs were avoided. The spatio-temporal parameters estimates showed smaller errors than those reported in previous studies and a similar level of precision and accuracy for both healthy and pathologic gait patterns. The combination of robustness, precision and accuracy suggests that the proposed method is suitable for routine clinical use.

Figure 1

Subject wearing two MIMUs attached above the ankles and walking on the instrumented mat used as gold standard for MIMU based estimates of gait spatio-temporal parameters.

Figure 2

Gait events detection from MIMU signals. (a) Angular velocities in the sagittal plane (ω_z) for a hemiparetic subject are reported (black line: affected side). Rectangular frames represent trusted swing (dotted line) and trusted stance (solid line) intervals for the affected limb. (b) ML angular velocity (black line) and AP acceleration (gray line) for the affected side of a hemiparetic subject. Colored boxes represent time intervals for the IC (light gray) and FC (intense gray) search; dotted vertical lines represent the GEs timings.

Figure 3

Minimum, first quartile (q1), median, third quartile (q3) and maximum values of mean absolute errors ( mae ) relative to: (a) IC and (b) stride time for all groups (healthy elderly – E, hemiparetic – H, Parkinson’s disease – P and choreic – C) and for both comfortable (V1) and higher (V2) speed. Errors larger than q₁ + 1.5(q₃ + q₁) or smaller than q₁–1.5(q₃–q₁) are considered outliers and represented with red marks (+).

Figure 4

Minimum, first quartile (q1), median, third quartile (q3) and maximum values of mean absolute errors (mae) relative to: (a) IC, (b) step time and (c) stride length for all groups (healthy elderly – E, hemiparetic – H, Parkinson’s disease – P and choreic – C). Errors larger than q₁ + 1.5(q₃ + q₁) or smaller than q₁–1.5(q₃–q₁) are considered outliers and represented with red marks (+).