Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments

Abstract

Background: Walking patterns can provide important indications of a person's health status and be beneficial in the early diagnosis of individuals with a potential walking disorder. For appropriate gait analysis, it is critical that natural functional walking characteristics are captured, rather than those experienced in artificial or observed settings. To better understand the extent to which setting influences gait patterns, and particularly whether observation plays a varying role on subjects of different ages, the current study investigates to what extent people walk differently in lab versus real-world environments and whether age dependencies exist.

Methods: The walking patterns of 20 young and 20 elderly healthy subjects were recorded with five wearable inertial measurement units (ZurichMOVE sensors) attached to both ankles, both wrists and the chest. An automated detection process based on dynamic time warping was developed to efficiently identify the relevant sequences. From the ZurichMOVE recordings, 15 spatio-temporal gait parameters were extracted, analyzed and compared between motion patterns captured in a controlled lab environment (10 m walking test) and the non-controlled ecologically valid real-world environment (72 h recording) in both groups.

Results: Several parameters (Cluster A) showed significant differences between the two environments for both groups, including an increased outward foot rotation, step width, number of steps per 180° turn, stance to swing ratio, and cycle time deviation in the real-world. A number of parameters (Cluster B) showed only significant differences between the two environments for elderly subjects, including a decreased gait velocity (p = 0.0072), decreased cadence (p = 0.0051) and increased cycle time (p = 0.0051) in real-world settings. Importantly, the real-world environment increased the differences in several parameters between the young and elderly groups.

Conclusion: Elderly test subjects walked differently in controlled lab settings compared to their real-world environments, which indicates the need to better understand natural walking patterns under ecologically valid conditions before clinically relevant conclusions can be drawn on a subject's functional status. Moreover, the greater inter-group differences in real-world environments seem promising regarding the sensitive identification of subjects with indications of a walking disorder.

Keywords: IMU sensors; ZurichMOVE; gait analysis; hydrocephalus; natural walking patterns; non-controlled settings; real-world environment; walking disorder.

Copyright © 2020 Renggli, Graf, Tachatos, Singh, Meboldt, Taylor, Stieglitz and Schmid Daners.

Figures

FIGURE 1

(A) ZurichMOVE sensor axis orientation for accelerometer and gyroscope as well as the three Euler Angles, together with positive turning direction, Θ around the x-axis, Φ around the y-axis and Ψ around the z-axis. (B) ZurichMOVE sensor attachment on both ankles, both wrists and the chest including the axis orientation. (C) Sensor attachment to the body using kinesiology tapes.

FIGURE 2

(A) Workflow of step detection based on a minima and maxima angular velocity search in the z-direction of the foot sensor followed by a dynamic time warping (DTW) based template matching procedure. (B) For every reported step, the presence or not of arm swinging was checked using DTW template matching. (C) The process of merging turning sequences Θ(j), Θ(j + 1) and Θ(j + 2) belonging to the same turning event to get the full turning angle is illustrated. (D) Estimation workflow of global acceleration ɑ(t), velocity v(t), and position p(t) during one gait cycle [between two foot flat (FF) events] via double integration of IMU acceleration data ɑs(t). Rotation matrix RWS(t) then rotated the sensor position into global coordinates. Drift was linearly estimated and removed, including compensation for the effect of gravity. Zero acceleration and velocity at FF events and ground-level walking were assumed. This Integration process was performed for each gait cycle individually. BC, boundary conditions.

FIGURE 3

Principle of step width (SW) calibration procedure. The subject walked on two parallel lines, spaced by dtight or dbroad, for which the tilting angles Φtight and Φbroad were evaluated. These four values were used to define a linear reference line for the SW estimation where the Φ values were matched to d values between the feet.

FIGURE 4

Comparison between gait parameters collected in lab (10-m walking test) versus real-world (72 h investigation) environments for young and elderly subjects (n = 20 each). The boxplots indicate the absolute differences between the two environments (ParameterReal–world - ParameterLab) for both groups. The median value is illustrated as a line, the mean value as a cross and outliers as dots. The line indicating zero difference between the two settings is depicted in bold. Abbreviations are listed in Table 2.