Novel evaluation of upper-limb motor performance after stroke based on normal reaching movement model.

BACKGROUND: Upper-limb rehabilitation robots provide repetitive reaching movement training to post-stroke patients. Beyond a pre-determined set of movements, a robot-aided training protocol requires optimization to account for the individuals' unique motor characteristics. Therefore, an objective evaluation method should consider the pre-stroke motor performance of the affected arm to compare one's performance relative to normalcy. However, no study has attempted to evaluate performance based on an individual's normal performance. Herein, we present a novel method for evaluating upper limb motor performance after a stroke based on a normal reaching movement model. METHODS: To represent the normal reaching performance of individuals, we opted for three candidate models: (1) Fitts' law for the speed-accuracy relationship, (2) the Almanji model for the mouse-pointing task of cerebral palsy, and (3) our proposed model. We first obtained the kinematic data of healthy (n = 12) and post-stroke (n = 7) subjects with a robot to validate the model and evaluation method and conducted a pilot study with a group of post-stroke patients (n = 12) in a clinical setting. Using the models obtained from the reaching performance of the less-affected arm, we predicted the patients' normal reaching performance to set the standard for evaluating the affected arm. RESULTS: We verified that the proposed normal reaching model identifies the reaching of all healthy (n = 12) and less-affected arm (n = 19; 16 of them showed an R2 > 0.7) but did not identify erroneous reaching of the affected arm. Furthermore, our evaluation method intuitively and visually demonstrated the unique motor characteristics of the affected arms. CONCLUSIONS: The proposed method can be used to evaluate an individual's reaching characteristics based on an individuals normal reaching model. It has the potential to provide individualized training by prioritizing a set of reaching movements.

A robotic treadmill system to mimic overground walking training with body weight support.

Introduction: Body weight support overground walking training (BWSOWT) is widely used in gait rehabilitation. However, existing systems require large workspace, complex structure, and substantial installation cost for the actuator, which make those systems inappropriate for the clinical environment. For wide clinical use, the proposed system is based on a self-paced treadmill, and uses an optimized body weight support with frame-based two-wire mechanism. Method: The Interactive treadmill was used to mimic overground walking. We opted the conventional DC motors to partially unload the body weight and modified pelvic type harness to allow natural pelvic motion. The performance of the proposed system on the measurement of anterior/posterior position, force control, and pelvic motion was evaluated with 8 healthy subjects during walking training. Results: We verified that the proposed system was the cost/space-effective and showed the more accurate anterior/posterior position than motion sensor, comparable force control performance, and natural pelvic motion. Discussion: The proposed system is cost/space effective, and able to mimic overground walking training with body weight support. In future work, we will improve the force control performance and optimize the training protocol for wide clinical use.

Impedance Control of Human Ankle Joint With Electrically Stimulated Antagonistic Muscle Co-Contraction.

Functional electrical stimulation (FES) is often used, typically in an open-loop manner, to restore paralyzed motor function for daily living activities. Several feedback control strategies have been developed to improve the performance and usability of FES-evoked movement. However, most of them have been position controllers, while the control strategy for human movement has been known as impedance modulation. Moreover, few studies have attempted to use antagonistic co-contraction for FES feedback control despite its expected benefits, such as enhanced stability and performance and better rehabilitation outcome. In this paper, we propose a robust impedance controller for FES that can adjust the intrinsic joint stiffness using co-contraction. It consists of an impedance control law based on time-delay estimation to compensate for the nonlinear uncertain joint dynamics and an antagonistic muscle co-contraction allocator to address the intrinsic joint stiffness caused by the co-contraction. The proposed controller was implemented on the ankle joints of five healthy subjects to simulate a standing balance situation. The results verified that the proposed controller can achieve desired impedance accurately by adjusting the intrinsic stiffness that stems from the change in the amount of co-contraction (up to 48.4% better impedance achievement with high desired stiffness).