Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects

Abstract

The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis. The gait parameters are evaluated to objectively assess the amputees' prosthesis-wearing status. The Madgwick algorithm was used in the study to correct the angular velocity deviation using acceleration data and convert it to quaternion. Further, the zero-velocity update method was applied to reconstruct patients' walking trajectories. The combination of computed walking trajectory with pelvic and lower limb joint motion enables sketching the details of motion via a stickman that helps visualize and animate the walk and gait of a test subject. Five participants with above-knee (n = 2) and below-knee (n = 3) amputations were recruited for gait analysis. Kinematic parameters were evaluated during a walking test to assess joint alignment and overall gait characteristics. Our findings support the feasibility of employing simple algorithms to achieve accurate and precise joint angle estimation and gait parameters based on wireless inertial sensor data.

Keywords: Madgwick filtering; ZUPT; dynamic alignment; prosthetic gait analysis; wireless inertial measuring system.

Figure 1

Wireless IMUs and their use: (a) composition of a single IMU, (b) illustration of the coordinate system and mounting locations of IMUs.

Figure 2

Operation interface to acquire inertial data, where section (A): status monitoring, (B): function keys, (C): IMU connection and data acquiring, (D): illustration of acquired inertial data (from left to right column for angular velocity, acceleration, and magnetic field, respectively).

Figure 3

Flow diagram to reconstruct subjects’ walking trajectories using Madgwick filtering and ZUPT.

Figure 4

Setting of markers (dash line) for swing and stance phases through using acceleration data.

Figure 5

Adoption of the ZUPT scheme to correct drift errors.

Figure 6

Definition of geometric relationship among the joints and trunk.

Figure 7

Definition of coordinate systems on two consecutive joints of robot links. (Redrawn based on [37] and the content in the article).

Figure 8

Reconstruction of walking trajectory. (The colors in the figure from top to down represent the pelvis, thigh, calf, and foot).

Figure 9

(a,b) illustrate the simulation of the right and left lower limbs, respectively, and show the setup of IMUs to validate the measuring system through using ABB^® IRB 120 robot, and illustration of the definition of local coordinate systems. The numbering of IMUs is the same as in Figure 1, which is used to simulate the mounting positions of IMUs on the lower limbs.

Figure 10

Robot motion trajectories (designated and IMU reconstructed) for (a) 2D and (b) 3D motion tasks.

Figure 11

Validation of the developed inertial measuring system: (a) subject wearing IMUs and optical markers, (b) setup of optical motion capture system (Qualisys^®), where the force plates are the 1.8 m marked region. The yellow box represents the positions of the force plates.

Figure 12

Comparison of evaluated extension/flexion of (a,b) hip, (c,d) knee, and (e,f) ankle joint between the results from inertial data and Qualisys^®. The depicted figures demonstrate the effectiveness of the proposed algorithms in aligning the measurements of the inertial sensors with those of the high-speed camera system.

Figure 13

Route of 10 m walking test, (a) schematic and (b) photo.

Figure 14

IMUs mounted on prosthesis-wearing amputee participants, (a) transfemoral and (b) transtibial.

Figure 15

Pelvis rotation on three planes for (a) healthy and (b) amputee subject (#1), where the ROMs on the individual planes are characterized at the up-left corner. (Each two consecutive vertical solid lines ‘―’ characterize a stride. The rotation on the sagittal plane is able to show two steps in a stride, where dash vertical lines ‘- - -’ divide the steps, and circles ‘o’ indicate the instants of the left- and right-thigh farthest rotating position in the step.)

Figure 16

Comparison of ROMs for hip, knee, and ankle joints (from top to bottom) along the sagittal plane between sound and prosthetic legs.