ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments

Abstract

Mobile robotic platforms have made inroads in the rehabilitation area as gait assistance devices. They have rarely been used for human gait monitoring and analysis. The integration of mobile robots in this field offers the potential to develop multiple medical applications and achieve new discoveries. This study proposes the use of a mobile robotic platform based on depth cameras to perform the analysis of human gait in practical scenarios. The aim is to prove the validity of this robot and its applicability in clinical settings. The mechanical and software design of the system is presented, as well as the design of the controllers of the lane-keeping, person-following, and servoing systems. The accuracy of the system for the evaluation of joint kinematics and the main gait descriptors was validated by comparison with a Vicon-certified system. Some tests were performed in practical scenarios, where the effectiveness of the lane-keeping algorithm was evaluated. Clinical tests with patients with multiple sclerosis gave an initial impression of the applicability of the instrument in patients with abnormal walking patterns. The results demonstrate that the system can perform gait analysis with high accuracy. In the curved sections of the paths, the knee joint is affected by occlusion and the deviation of the person in the camera reference system. This issue was greatly improved by adjusting the servoing system and the following distance. The control strategy of this robot was specifically designed for the analysis of human gait from the frontal part of the participant, which allows one to capture the gait properly and represents one of the major contributions of this study in clinical practice.

Keywords: clinical environments; human gait analysis; markerless system; mobile robotic platforms; motion capture; multiple sclerosis.

Figure 1

Robot–person–environment interactions in a typical setup in the development of this study.

Figure 2

An overview of the mechanical design of ROBOGait. The figure shows: (1) the mobile robotic base, (2) on-board computer (Intel NUC), (3) Slamtec LIDAR A2 sensor, (4) adjustable aluminum brackets, (5) servoing system mechanism, (6) servomotor (Hitec HS-755HB), (7) damping device, and (8) Orbbec Astra RGBD camera.

Figure 3

Data flow in the mobile robotic platform following the ROS publisher/subscriber communication protocol.

Figure 4

Schematic diagram of the operation of the lane-keeping controller.

Figure 5

Person-tracking and lane-keeping controller.

Figure 6

Points of interest for the calculation of the desired angle during the lane-keeping task.

Figure 7

Robot’s behavior with the lane-keeping controller. The figure shows seven set points emulated sequentially: ±0.52, ±0.35, ±0.17, and 0 rad.

Figure 8

An overview of the experimental environment during the validation stage.

Figure 9

Comparison of kinematic gait cycles retrieved by the robot system (black line) and the Vicon system (blue line). The gait cycles are normalized from 0–100% and averaged for all of the iterations.

Figure 10

Experiment 1. Servoing system gain of $$Kp=1.5$$ and following distance of $$2.5$$ m. Experiment 2. Servoing system gain of $$Kp=3.0$$ and following distance of $$2.0$$ m. The maps show the planned path and the real path followed by the robot and the person. The kinematic joint signals (knee and hip flex./ext. in the entire experiment) in addition to the angular position of the person in the camera coordinate system.

Figure 11

Walking experiments with MS patients in clinical environments. The upper part shows the trajectories, and the lower part shows the kinematic signals of knee and hip flexion/extension.