Using Person-Specific Muscle Fatigue Characteristics to Optimally Allocate Control in a Hybrid Exoskeleton - Preliminary Results

Automatic Assist Level Adjustment Function of a Gait Exercise Rehabilitation Robot with Functional Electrical Stimulation for Spinal Cord Injury: Insights from Clinical Trials

This study aimed to identify whether the combined use of functional electrical stimulation (FES) reduces the motor torque of a gait exercise rehabilitation robot in spinal cord injury (SCI) and to verify the effectiveness of the developed automatic assist level adjustment in people with paraplegia. Acute and chronic SCI patients (1 case each) performed 10 min of gait exercises with and without FES using a rehabilitation robot. Reinforcement learning was used to adjust the assist level automatically. The maximum torque values and assist levels for each of the ten walking cycles when walking became steady were averaged and compared with and without FES. The motor's output torque and the assist level were measured as outcomes. The assist level adjustment allowed both the motor torque and assist level to decrease gradually to a steady state. The motor torque and the assist levels were significantly lower with the FES than without the FES under steady conditions in both cases. No adverse events were reported. The combined use of FES attenuated the motor torque of a gait exercise rehabilitation robot for SCI. Automatic assistive level adjustment is also useful for spinal cord injuries.

An Enhanced Model Free Adaptive Control Approach for Functional Electrical Stimulation Assisted Knee Joint Regulation and Control

Functional electrical stimulation has been widely used in the neurologically disabled population as a rehabilitation method because of its intrinsic and higher ability to activate paralyzed muscles. However, the nonlinear and time-varying nature of the muscle against exogenous electrical stimulus makes it very challenging to achieve optimal control solutions in real-time, that results in difficulty in achieving functional electrical stimulus-assisted limb movement control in the real-time rehabilitation process. Model-based control methods have been suggested in many functional electrical stimulations elicited limb movement applications. However, in the presence of uncertainties and dynamic variations during the process the model-based control methods are unable to give a robust performance. In this work, a model-free adaptable control approach is designed to regulate knee joint movement with electrical stimulus assistance without prior knowledge of the dynamics of the subjects. The model free adaptive control with a data-driven approach is provided with recursive feasibility, compliance with input constraints, and exponential stability. The experimental results obtained from both non-disabled participants and a participant with spinal cord injury validate the ability of the proposed controller to allocate electrical stimulus for regulating seated knee joint movement in the pre-defined trajectory.

Development of a Gait Rehabilitation Robot Using an Exoskeleton and Functional Electrical Stimulation: Validation in a Pseudo-paraplegic Model

Objective:

We have developed a robot for gait rehabilitation of paraplegics for use in combination with functional electrical stimulation (FES). The purpose of this study was to verify whether the robot-derived torque can be reduced by using FES in a healthy-person pseudo-paraplegic model.

Methods:

Nine healthy participants (22–36 years old) participated in this study. The robot exoskeleton was designed based on the hip–knee–ankle–foot orthosis for paraplegia. Participants walked on a treadmill using a rehabilitation lift to support their weight. The bilateral quadriceps femoris and hamstrings were stimulated using FES. The participants walked both with and without FES, and two walking speeds, 0.8 and 1.2 km/h, were used. Participants walked for 1 min in each of the four conditions: (a) 0.8 km/h without FES, (b) 0.8 km/h with FES, (c) 1.2 km/h without FES, and (d) 1.2 km/h with FES. The required robot torques in these conditions were compared for each hip and knee joint. The maximum torque was compared using one-way analysis of variance to determine whether there was a difference in the amount of assist torque for each gait cycle.

Results:

Walking with the exoskeleton robot in combination with FES significantly reduced the torque in hip and knee joints, except for the right hip during extension.

Conclusions:

In the healthy-participant pseudo-paraplegic model, walking with FES showed a reduction in the robot-derived torque at both the hip and knee joints. Our rehabilitation robot combined with FES has the potential to assist paraplegics with various degrees of muscle weakness and thereby provide effective rehabilitation.

Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning

A hybrid exoskeleton comprising a powered exoskeleton and functional electrical stimulation (FES) is a promising technology for restoration of standing and walking functions after a neurological injury. Its shared control remains challenging due to the need to optimally distribute joint torques among FES and the powered exoskeleton while compensating for the FES-induced muscle fatigue and ensuring performance despite highly nonlinear and uncertain skeletal muscle behavior. This study develops a bi-level hierarchical control design for shared control of a powered exoskeleton and FES to overcome these challenges. A higher-level neural network–based iterative learning controller (NNILC) is derived to generate torques needed to drive the hybrid system. Then, a low-level model predictive control (MPC)-based allocation strategy optimally distributes the torque contributions between FES and the exoskeleton’s knee motors based on the muscle fatigue and recovery characteristics of a participant’s quadriceps muscles. A Lyapunov-like stability analysis proves global asymptotic tracking of state-dependent desired joint trajectories. The experimental results on four non-disabled participants validate the effectiveness of the proposed NNILC-MPC framework. The root mean square error (RMSE) of the knee joint and the hip joint was reduced by 71.96 and 74.57%, respectively, in the fourth iteration compared to the RMSE in the 1st sit-to-stand iteration.