A Novel User Control for Lower Extremity Rehabilitation Exoskeletons

Abstract

Lower extremity exoskeletons offer the potential to restore ambulation to individuals with paraplegia due to spinal cord injury. However, they often rely on preprogrammed gait, initiated by switches, sensors, and/or EEG triggers. Users can exercise only limited independent control over the trajectory of the feet, the speed of walking, and the placement of feet to avoid obstacles. In this paper, we introduce and evaluate a novel approach that naturally decodes a neuromuscular surrogate for a user's neutrally planned foot control, uses the exoskeleton's motors to move the user's legs in real-time, and provides sensory feedback to the user allowing real-time sensation and path correction resulting in gait similar to biological ambulation. Users express their desired gait by applying Cartesian forces via their hands to rigid trekking poles that are connected to the exoskeleton feet through multi-axis force sensors. Using admittance control, the forces applied by the hands are converted into desired foot positions, every 10 milliseconds (ms), to which the exoskeleton is moved by its motors. As the trekking poles reflect the resulting foot movement, users receive sensory feedback of foot kinematics and ground contact that allows on-the-fly force corrections to maintain the desired foot behavior. We present preliminary results showing that our novel control can allow users to produce biologically similar exoskeleton gait.

Keywords: gait; lower extremity exoskeletons; rehabilitation robotics; robot control systems; spinal cord injury.

Figure 1

(A) Mount to attach the foot to ankle motors. (B) Foot of the biped with extrusion to mount (1) Optoforce. (C) Front view of 10 DOF biped robot designed based on anthropometric data. L1 is the link length between hip and knee, L2 is link length between knee and foot. (C) The coordinate system X, Y, and Z used for robot's movement on treadmill.

Figure 2

The Admittance Control algorithm for control of biped gait in the sagittal plane. θ1 is hip angle, θ2 is knee angle, and θ3 is the ankle angle computed based on Equations (1-3), respectively. Velocities in X and Y are represented by vx and vy, respectively. Acceleration in X and Y are represented by ax, and ay, respectively. Position in X and Y are represented by px and py, respectively.

Figure 3

The Admittance Control algorithm for trekking pole control of coronal plane movement of the robot leg. θ4 is hip angle, and θ5 is the ankle angle and is computed using Equations (6) and (7). Angular velocity and angular acceleration is denoted by vz and az, respectively.

Figure 4

User controlling the biped by holding the trekking poles on the ipsilateral side using hands. Constant force springs connect the robot to the overhead frame to maintain balance.

Figure 5

Cartesian values of the desired and actual foot position of (A) X, (B) Y, and (C) Z positions vs. time for the left foot. Desired angles generated by the inverse kinematics and actual angles achieved by the motors of (D) hip in sagittal, (E) knee in sagittal, and (F) hip in coronal plane vs. time for left leg. The flat regions in the knee plot indicate the stance phase of gait. The red lines denote the actual position/angle reached by the robot and the blue lines denote the desired position/angle computed by the algorithm.

Figure 6

Mean ± std. error of (A) spatial symmetry, (B) temporal symmetry, and (C) step height symmetry, for trials 1 through 8 for biped walking by 7 participants and for 1 trial by the reference participant. (D) X and Y positions of one biped foot for one naïve subject walking the biped on the treadmill at medium speed.

Figure 7

Mean ± std. error of duty cycle in (A) right leg and (B) left leg of 7 participants for trials 1 through 8, and of 1 reference participant (C) during 1 trial. Stance phase is red and swing phase is blue.

Figure 8

Right knee angle of multiple strides of the (A) user-controlled biped, and (B) reference participant, walking on the treadmill. Right hip angle of multiple strides of the (C) user-controlled biped, and (D) reference participant, walking on the treadmill.