The assist-as-needed (AAN) robotic strategy is a popular strategy for encouraging patients’ active participation in robot-assisted rehabilitation therapy. Numerous clinical outcomes have suggested the effectiveness of the AAN scheme to induce neuroplasticity in patients with neurological impairment.1 The AAN strategy focuses on providing the minimal amount of robotic assistance necessary for a patient to complete a movement,2 thus a significant effort is required from the patient. If the patient can perform the task flawlessly, robotic assistance is withdrawn. However, if the patient cannot complete the given task, assistance is offered only as much as it is needed.3
Deploying robotic assistance in accordance with the AAN strategy still come with many shortcomings.3,4 One major issue is how to appropriately estimate patients’ functional ability to set the correct level of robotic assistance. Another issue is the consistency of the estimated subject’s functional ability with clinical data and the repeatability across a wide range of subjects. An appropriate estimation of subject’s functional ability consistent with clinical data can give a realistic basis for deploying robotic assistance, since it gives a measure of subject’s actual disability level or recovery progress.5,6
A few strategies of AAN have been proposed recently which have attempted to address the challenges in the scheme. Wolbrecht et al.7 proposed a model-based robotic assistance strategy which can enable a robot to learn the patients’ ability in real time based on a radial-basis function (RBF). The RBF is applied under an adaptive control framework.
Another AAN strategy was proposed by Pehlivan et al.3 The authors introduced a minimal assist-as-needed (mAAN) strategy which uses a Kalman filter to estimate subjects’ functional inputs instead of the RBF technique that is a sensor-less force estimation strategy. Under the scheme, the ANN strategy is achieved in the following two ways: (1) by updating the derivative feedback gain to modify the bounds of allowable error on the desired trajectory and (2) by decaying a feed-forward disturbance rejection term which reduces the constraint on allowable quick movements. The combined effect could vary the robotic assistance according to the subjects’ capability.8 The potential limitation of this approach is the reliance on the robot model for the estimation of subject’s capability. It is well known that model errors always exist and can significantly excite the disturbance term making it difficult to accurately estimate the subject’s input. There is the implication that different robot models would produce different functional ability estimates which will hinder an appropriate standardization or deployment of robotic assistance for clinical purpose.9,10
Pérez-Rodríguez et al.11 also introduced an AAN strategy called anticipatory assistance-as-needed control algorithm capable of ensuring that the deviation from a patients’ desired trajectory is restored by giving an anticipated force assistance. This way, robotic assistance is always given as a restoring force to maintain the subject on the reference (desired) trajectory. With regards to the validity of this strategy, there are however no experimental studies till date.
Other noteworthy AAN strategies include the rule-based assistive strategy proposed by Wang et al.,12 which is applied in Physiotherabot; the hybrid impedance control for wrist and forearm rehabilitation proposed by Akdoğan and Adli,13 which is applied on a 3-degree-of-freedom (3-DOF) upper-limb rehabilitation robot; and the visual error augmentation-based AAN proposed by Akdoğan et al.,14 which can provide robotic assistance as needed by amplifying tracking error to heighten the participant’s motivation.
Efforts in developing an appropriate estimation strategy for AAN robotic assistance are still on course;15 however, there has been less focus on developing appropriate estimation techniques of subject’s functional ability that are consistent with the clinical procedure and that can be integrated in the control loop to provide robotic assistance.15,16
In this paper, we propose an ANN strategy to direct robotic assistance based on a novel functional ability index (FAI). The main originality of this work is the derivation of the new FAI estimation algorithm in accordance with the clinical procedure for the estimation of subject’s motor ability in movement task. As a preliminary investigation, we derive our FAI following the Wolf Motor Function Test (WMFT), a popular motor function test with consistency over a wide range of neurologically impaired patients. The FAI serves as input to a decay algorithm under the adaptive control law which consequently varies the robotic assistance according to the subject’s functional ability. The FAI is independent on the robot model or controller adaptation law and thus it is unaffected by modelling uncertainties.
The rest of the paper is organized as follows: section ‘System dynamic and control’ presents the dynamics for the robotic rehabilitation system, the proposed FAI, and the proposed control algorithm. Section ‘Experimental study’ presents the data collection and simulation study; section ‘Results’ describes the results; section ‘Discussion’ presents the discussion; and section ‘Conclusion’ concludes the paper.
System dynamic and control
The mechanical system
The proposed prototype of the exoskeleton system is shown in Figure 1. The system is an upper-limb rehabilitation robotic device with two active degrees of freedom (DOFs) at the shoulder and elbow joint, respectively. If actively controlled, the exoskeleton can permit abduction/adduction (AA) movement of the shoulder joint and flexion/extension (FE) movement of the elbow, thus allowing the possibility of performing the table to mouth reaching task.