Feedforward model based arm weight compensation with the rehabilitation robot ARMin

Abstract

Highly impaired stroke patients at early stages of recovery are unable to generate enough muscle force to lift the weight of their own arm. Accordingly, task-related training is strongly limited or even impossible. However, as soon as partial or full arm weight support is provided, patients are enabled to perform arm rehabilitation training again throughout an increased workspace. In the literature, the current solutions for providing arm weight support are mostly mechanical. These systems have components that restrict the freedom of movement or entail additional disturbances. A scalable weight compensation for upper and lower arm that is online adjustable as well as generalizable to any robotic system is necessary. In this paper, a model-based feedforward weight compensation of upper and lower arm fulfilling these requirements is introduced. The proposed method is tested with the upper extremity rehabilitation robot ARMin V, but can be applied in any other actuated exoskeleton system. Experimental results were verified using EMG measurements. These results revealed that the proposed weight compensation reduces the effort of the subjects to 26% on average and more importantly throughout the entire workspace of the robot.

Human arm weight compensation in rehabilitation robotics: efficacy of three distinct methods

“…1). For a more detailed explanation, mathematical derivation and implementation, interested readers are referred to the Additional file 1 and our previous work [13].…”

“…Therefore, many rehabilitation robots with arm weight compensation functions have been developed. The common robot types that provide system-dependent arm weight compensation can be divided in gantry-based robots [8][9][10], passive exoskeletons [11,12], actuated exoskeletons [4,11,13,14], and actuated end-effector robots [5,6,[15][16][17][18].…”

“…However, since arm weight is constant over time, we aim to estimate the arm weight parameters once in the beginning of the therapy session and not to adapt them during the therapy. From a literature review [8,22] and our own experience, the following four requirements of ideal, generalizable weight compensation for robot-assisted training of activities of daily living were deduced: Freedom of movement, no additional disturbances, scalability, and applicability to other systems [13].…”

“…Furthermore, previous EMG signal analyses have mainly focused on individual muscle evaluations [29][30][31] instead of a combined evaluation of all relevant muscles with respect to their passive reference measurements as a score. Additional active EMG reference measurements to adapt to a subject's specific behavior and physiology have not been a main focus [13]. Finally, the arm weight relief efficacies of different arm weight compensation methods have never been compared, even for use of the same device.…”

“…2. Recordings were acquired from the pectoralis major (PM, shoulder horizontal flexor), the upper trapezius (UT, shoulder elevator), the posterior deltoid (PD, shoulder extensor), the anterior deltoid (AD, shoulder flexor), the biceps brachii (BB, elbow flexor) and the lateral triceps (LT, elbow extensor) as reported in [13].…”