Abstract
Robot-assisted rehabilitation has proven to be effective for improving the motor performance of patients with neuromuscular injuries. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy training. This paper presents an end-effector upper-limb rehabilitation robot for the functional recovery training of disabled patients. A force-field-based rehabilitation control strategy is then developed to induce active patient participation during training tasks. The proposed control strategy divides the 3D space around the rehabilitation training path into a human-dominated area and a robot-dominated area. It encodes the space around the training path and endows the corresponding normal and tangential force; the tangential component assists with movement along the target path, and the normal component pushes the patient’s hand towards the target path using a real-time adjustable controller. Compared with a common force-field controller, the human–robot interaction in this strategy is easy and can be quickly adjusted by changing the force field’s range or the variation characteristics of two forces, and the intervention in two directions can change continuously and smoothly despite the patient’s hand crossing the two areas. Visual guidance based on the Unity-3D environment is introduced to provide visual training instructions. Finally, the feasibility of the proposed control scheme is validated via training experiments using five healthy subjects.
How to cite.
Pan, J., Zhang, L., and Sun, Q.: Development of a force-field-based control strategy for an upper-limb rehabilitation robot, Mech. Sci., 13, 949–959, https://doi.org/10.5194/ms-13-949-2022, 2022.
1 Introduction
The number of patients with upper-limb motor dysfunction caused, for example, by stroke and spinal cord injury has increased sharply year over year (Malcolm et al., 2009). About two-thirds of stroke patients survive; however, more than 80 % of them may suffer hemiparesis. These survivors require prolonged physical therapy to recover motor function for the activities of daily living (ADLs) (Cortese et al., 2015). Research on neurological rehabilitation suggests that repetitive motor activity has positive effects with respect to improving movement coordination and avoiding muscle atrophy, and the therapeutic effect is mainly determined by the intention, task-oriented quality and sustainability of rehabilitation training. Robotic systems have a natural advantage in rehabilitation over traditional rehabilitation treatments (Bertani et al., 2017). Robot-assisted therapy can deliver long-endurance, repetitive and sustainable therapeutic training using programmable control strategies (Milot et al., 2013). Furthermore, therapists can obtain a series of quantitative assessments of patient training performance to further optimize the treatment strategies (Mehrholz et al., 2012). In recent decades, the application of robotic systems to the rehabilitation treatment of neuromuscular impairment has received increasing attention from around the world (Krebs et al., 2004; Gassert and Dietz, 2018). To date, many rehabilitation robots have been developed, including end-effector and exoskeleton robots. With respect to the aforementioned robot systems, end-effector rehabilitation robots have attracted plenty of interest from widespread researchers, resulting in the development of systems such as MIT-MANUS (Hogan et al., 1992), GENTLE/S (Loureiro et al., 2003), REHAROB (Andras et al., 2009), ACRE (Schoone et al., 2007) and PASCAL (Keller et al., 2013).
The effectiveness of robot-assisted rehabilitation treatment is largely determined by the control strategy applied in the therapy training (Jiang et al., 2012; Kahn et al., 2004). Various kinds of control strategies have been developed for end-effector upper-limb rehabilitation robots in order to execute predetermined training tasks. The existing control methods can be classified into passive control strategies and cooperative control strategies according to the interaction between humans and rehabilitation robots (Lindberg et al., 2004). In the early stages of hemiplegia recovery, the patient’s affected limb is completely paralyzed, without any muscle contraction or active movement. The passive control strategy is particularly applicable in this situation, as the robot assists the patient to passively perform repetitive flexion and extension training tasks along a predetermined path, helps the patient maintain the normal range of joint movement (Proietti et al., 2016), and lays the foundation for active training. Many controllers have been proposed to ensure the performance of passive training, including fixed-gain PD (proportional and differentiation) controllers (French et al., 2014), neural network-based PI (proportional and integral) controllers (Erol et al., 2005), fuzzy logic PD controllers (Xu et al., 2011), dynamic fuzzy network impedance controllers (Song et al., 2014) and so on. However, if the motion-related central nervous system has been restored but is still weak, a cooperative control strategy can be used, which emphasizes fully mobilizing the patient’s intention to actively exercise during a training task in order to maximize the efficiency of rehabilitation training (Mounis et al., 2019). Therefore, the control strategy applied during this stage should facilitate patient–robot interaction with minimal robot intervention and maximal patient effort (Wu et al., 2018; Frisoli et al., 2009; Akiyama et al., 2015; Lee et al., 2020). Wang et al. (2019) developed an end-effector upper-limb rehabilitation robot based on fuzzy logic rules and impedance control; this system uses recursive least squares to estimate the human impedance parameters and quantify the residual motor capacity. These parameters and the motion deviations are input into the fuzzy logic controller. Zhang et al. (2020a) proposed an impedance-based assist-as-needed controller that enables the patient to move freely in the fault-tolerant region and provides assistance according to the patient’s functional ability when deviating from the fault-tolerant region. A new performance-based assistance method was developed by Leconte and Ronsse (2016) that can assess the movement features of smoothness, velocity and amplitude during training tasks. Krebs et al. (2003) proposed a novel concept of performance-based progressive robot therapy that uses speed, time or electromyography (EMG) thresholds to initiate robot assistance. Cui et al. (2017) developed a wrench-based controller to conduct an exoskeleton with 7 degrees of freedom (7DOF) for dexterous motion training that contains four basic force/torque components which guide and correct the position/pose errors. Shi et al. (2022, 2020) proposed a human-centered control method for assist-as-needed (AAN) robotic rehabilitation, and they created a feedback-stabilized closest-attitude tracking algorithm according to the geometric properties of a special 3D orthogonal group, SO(3), and then realized the tracking of the robot to the desired position/posture by force/velocity field.
Therefore, the main purpose of this paper is to present a new patient-cooperative control framework for an end-effector upper-limb rehabilitation robot that provides robot-assisted training for individuals with neuromuscular disorders. First, a minimal-intervention force-field-based control strategy is proposed. It divides the 3D space around the rehabilitation training path into the patient-dominated area and the robot-dominated area, encodes the space, and provides the corresponding normal and tangential forces that guide the patient’s hand movement towards and along the target path, respectively; moreover, a damping term is added to maintain the stability of the system. The human–robot interaction can be adjusted by changing the force field’s range or variation characteristics to meet the subjects’ requirements during different recovery stages. The patient-dominated area enables greater patient initiative with less robot intervention; however, robot intervention increases significantly as the patient’s hand deviates into the robot-dominated area. Finally, the feasibility of the proposed control strategy is evaluated by several preliminary training experiments using five healthy subjects who are required to accomplish the task in both the health and mock-paralyzed states. The experimental results are presented and discussed in Sects. 3 and 4. […]
TBI Rehabilitation 