This study presented a novel control approach for rehabilitation robotic system using the hybrid system theory and the subject’s bio-damping and bio-stiffness parameters. Resistance training was selected as a paradigm. The proposed control architecture incorporated the physical therapist’s behavior intervention, the stroke survivor’s muscle strength changes, and the robotic device’s motor control into a unified framework. The main focuses of this research were to (i) automatically monitor the subject’s muscle strength changes using the online identified bio-damping/stiffness parameters; (ii) make decisions on the modification of the desired resistive force so as to coincide with the subject’s muscle strength changes; and (iii) generate accommodating plans when the safety-related issues such as spasticity and the abnormal robotic working states happen during the execution of training tasks. A Barrett WAM compliant manipulator-based resistance training system and two experiments including four scenarios were developed to verify the proposed approach. Experimental results with healthy subjects showed that the hybrid system–based control architecture could administrate the subject’s muscle strength changes and the robotic device’s interventions in an automated and safe manner.
Patients suffering from strokes need to receive intensive rehabilitation training to improve range of motion, movement coordination, and muscle strength. These therapy programs are traditionally conducted by physical therapists. However, the efficacy of therapist-centered rehabilitation therapy often relies on the therapist’s subjective clinical experience. In recent years, there is an increasing interest in using robotic devices to help providing rehabilitation training because these devices can provide a variety of highly repetitive movements and training protocols for stroke patients.1
Human–robot interactive control approaches, that specify how rehabilitation robotic devices adaptively interact with stroke survivors in an automated and safe manner, are one of the major challenges in developing robot-assisted training system. The control strategies explored in some early rehabilitation robotic devices, such as the proportional integral derivative controller for MIME,2 the impedance controller for MIT-Manus,3 and the admittance controller for GENTLE/s,4are mainly concentrated on providing constant assistance while not integrating stroke patients’ feedback into the control loop. It is a common hypothesis in the field of robot-assisted rehabilitation that the control approaches, that can close the loop via the patients and further adapt robotic devices’ assistance to the stroke patients’ progress, will be more efficient.5 This issue is typically addressed using assist-as-needed6,7 or user-cooperative control strategies.8,9 In the study by Hussain et al.,6 an adaptive seamless assist-as-needed control scheme is developed for the robotic gait training, which learns in real time the disability level of human subjects based on the trajectory tracking errors and adapts the robotic assistance accordingly. Riener et al.8 presents a patient-cooperative strategy for robotic gait training, and results with healthy subjects show that subjects train more actively and only get support as much as needed. Nonetheless, the potential issue of these methods is that they focus on low-level robotic motor controllers. The training tasks update, the safety-related issues (e.g. spasticity and twitch) monitoring, the robotic working states (e.g. joint torque, voltage, workload, and end-effector velocity) detection, the therapeutic progress assessment, and the decision-making behaviors are all administered by physical therapists.
In the last few years, hierarchical supervisory control strategies have been developed. These approaches incorporate the training tasks update and the physical therapist’s behavior decision into the low-level robotic motor controller. Denève et al.10 merge a high-level sequential controller into a robotic-assisted upper limb rehabilitation system, by which three different low-level control laws for passive, active, and resistance modes can be switched. In the study by Varol et al.,11 a three-level hierarchical supervisory control architecture is proposed, which consists of the lowest level robotic joint torque controller, the middle-level torque references generator, and the high-level intent recognizer. Fuzzy-based hierarchical supervisory control strategies are also presented in our previous studies.12,13 Especially in the study,13 a high-level safety supervisory controller is designed to monitor spasticity-related issues. Great improvements have been made in the current hierarchical supervisory control architecture for incorporating therapists’ behavior-decision experiences. Unfortunately, these supervisory control methods are statically designed concerning some predefined situations, and the absence of dynamic mechanism makes them incapable of dealing with the extended/unexpected events and the complex training tasks coordination. Besides, few rehabilitation robotic control system design takes the safety-related issues into consideration.
In this article, we present a hybrid system–based control architecture using resistance training as a paradigm, which can incorporate the physical therapist’s behavior intervention, the stroke survivor’s muscle strength progress, and the robotic device’s motor control into a unified framework. In fact, over the years, the hybrid systems framework has been effectively used in many fields to model and analyze their performances, such as the power systems,14 the communication networks,15 and the coordinated control of assistive robotic devices for complex tasks.16
The primary focus of this article is to (i) automatically monitor the impaired limb’s muscle strength progress using the online identified bio-impedance changes; (ii) make decisions on the modification of the desired resistive force so as to coincide with the impaired limb’s muscle strength changes; and (iii) be aware of robotic working state/safety-related issues during the execution of training task and to generate accommodating plans when such events happen. The remainder of this article is organized as follows: “Methods” section presents the experimental setup and protocols, bio-impedance parameters identification, and controller development. “Results” section details the results of the proposed control strategy. Some discussions and conclusions are given in sections “Discussions” and “Conclusions”.
The rehabilitation robotic system for upper limb muscle strength training used in the trials, shown in Figure 1(a), consisted of a Barrett WAM™ manipulator, a three dimensional (3-D) force sensor, and an external PC offered by Barrett Technology.17 The standard WAM™ is a four degree of freedom highly dexterous, back-drivable manipulator. Its human-like kinematics and high back drivability enable inherent force-control, haptic interaction, and rehabilitation application. In order to record the force interaction between the impaired limb and the WAM end-effector, a 3-D force sensor was designed and attached to the end-effector. Figure 1(b) and (c) shows the mechanical structure, appearance, and strain gauges distribution of the 3-D force sensor. Force data measured from the sensor must be transformed from the WAM tool frame into its world frame. The graphical user interface developed using Linux/GDK technology, shown in Figure 1(d), was used to display the actual training trajectory when the patient moved his arm in the XOZ vertical plane (O-XYZ coordinates shown in Figure 1(a)), where no reference trajectories were predefined except for several via points. The external PC, running with the Ubuntu Linux system and the Xenomai real-time module, was responsible for executing the control loop and sending high-level commands to the WAM-aided rehabilitation training system. Real-time communication between the external PC and motor Pucks™ was conducted via a high-speed controller area network bus.