Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Locomotor Training Following Spinal Cord Injury

Robotic gait training is an emerging technique for retraining walking ability following spinal cord injury (SCI). A key challenge in this training is determining an appropriate stepping trajectory and level of assistance for each patient, since patients have a wide range of sizes and impairment levels. Here, we demonstrate how a lightweight yet powerful robot can record subject-specific, trainer-induced leg trajectories during manually assisted stepping, then immediately replay those trajectories. Replay of the subject-specific trajectories reduced the effort required by the trainer during manual assistance, yet still generated similar patterns of muscle activation for six subjects with a chronic SCI. We also demonstrate how the impedance of the robot can be adjusted on a step-by-step basis with an error-based, learning law. This impedance-shaping algorithm adapted the robot's impedance so that the robot assisted only in the regions of the step trajectory where the subject consistently exhibited errors. The result was that the subjects stepped with greater variability, while still maintaining a physiologic gait pattern. These results are further steps toward tailoring robotic gait training to the needs of individual patients

Review of control strategies for robotic movement training after neurologic injury

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies

Hierarchical Compliance Control of a Soft Ankle Rehabilitation Robot Actuated by Pneumatic Muscles

Traditional compliance control of a rehabilitation robot is implemented in task space by using impedance or admittance control algorithms. The soft robot actuated by pneumatic muscle actuators (PMAs) is becoming prominent for patients as it enables the compliance being adjusted in each active link, which, however, has not been reported in the literature. This paper proposes a new compliance control method of a soft ankle rehabilitation robot that is driven by four PMAs configured in parallel to enable three degrees of freedom movement of the ankle joint. A new hierarchical compliance control structure, including a low-level compliance adjustment controller in joint space and a high-level admittance controller in task space, is designed. An adaptive compliance control paradigm is further developed by taking into account patient’s active contribution and movement ability during a previous period of time, in order to provide robot assistance only when it is necessarily required. Experiments on healthy and impaired human subjects were conducted to verify the adaptive hierarchical compliance control scheme. The results show that the robot hierarchical compliance can be online adjusted according to the participant’s assessment. The robot reduces its assistance output when participants contribute more and vice versa, thus providing a potentially feasible solution to the patient-in-loop cooperative training strateg

Compliance adaptation of an intrinsically soft ankle rehabilitation robot driven by pneumatic muscles

Pneumatic muscles (PMs)-driven robots become more and more popular in medical and rehabilitation field as the actuators are intrinsically complaint and thus are safer for patients than traditional rigid robots. This paper proposes a new compliance adaptation method of a soft ankle rehabilitation robot that is driven by four pneumatic muscles enabling three rotational movement degrees of freedom (DoFs). The stiffness of a PM is dominated by the nominal pressure. It is possible to control the robot joint compliance independently of the robot movement in task space. The controller is designed in joint space to regulate the compliance property of the soft robot by tuning the stiffness of each active link. Experiments in actual environment were conducted to verify the control scheme and results show that the robot compliance can be adjusted when provided changing nominal pressures and the robot assistance output can be regulated, which provides a feasible solution to implement the patient-cooperative training strategy

Towards human-knee orthosis interaction based on adaptive impedance control through stiffness adjustment

Rehabilitation interventions involving powered, wearable lower limb orthoses that can provide high-challenging locomotor tasks for repetitive training sessions, mainly when assist-as-needed strategies, such as adaptive impedance control, are designed. In this study, the adaptive behavior was ensured by software control of the robotic stiffness involved in the human-knee orthosis interaction in function of the gait cycle and speed. To estimate the stiffness, we analyzed the interaction torque-angle characteristics with experimental data. The speed-stiffness dependency was more evident when high stiffness values are demanded by the user's effort. Experimental evidence from five healthy subjects highlight that the adaptive control strategy provides a more comfortable, natural motion, and kinematic freedom as compared to the trajectory tracking control, allowing the user to contribute to the gait training. Future insights cover the implementation of gravitational compensation and real-time estimation and control of all inner dynamic properties of the impedance control law.This work has been supported by the FCT - Fundacao para a Ciencia e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, with the reference project UID/EEA/04436/2013, and by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalizacao (POCI) - with the reference project POCI-01-0145-FEDER-006941, and partially supported with grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Active interaction control applied to a lower limb rehabilitation robot by using EMG recognition and impedance model

Purpose – The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training. Design/methodology/approach – An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions. Findings – Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode. Originality/value – Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles

DEVELOPMENT OF A ROBOTIC EXOSKELETON SYSTEM FOR GAIT REHABILITATION

Ph.DDOCTOR OF PHILOSOPH

Human-robot cooperative movement training: Learning a novel sensory motor transformation during walking with robotic assistance-as-needed

BACKGROUND: A prevailing paradigm of physical rehabilitation following neurologic injury is to "assist-as-needed" in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning. METHODS: Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation, which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically, we use a robotic force field paradigm to impose a virtual impairment on the left leg of unimpaired subjects walking on a treadmill. We then derive an "assist-as-needed" robotic training algorithm to help subjects overcome the virtual impairment and walk normally. The problem is posed as an optimization of performance error and robotic assistance. The optimal robotic movement trainer becomes an error-based controller with a forgetting factor that bounds kinematic errors while systematically reducing its assistance when those errors are small. As humans have a natural range of movement variability, we introduce an error weighting function that causes the robotic trainer to disregard this variability. RESULTS: We experimentally validated the controller with ten unimpaired subjects by demonstrating how it helped the subjects learn the novel sensory motor transformation necessary to counteract the virtual impairment, while also preventing them from experiencing large kinematic errors. The addition of the error weighting function allowed the robot assistance to fade to zero even though the subjects' movements were variable. We also show that in order to assist-as-needed, the robot must relax its assistance at a rate faster than that of the learning human. CONCLUSION: The assist-as-needed algorithm proposed here can limit error during the learning of a dynamic motor task. The algorithm encourages learning by decreasing its assistance as a function of the ongoing progression of movement error. This type of algorithm is well suited for helping people learn dynamic tasks for which large kinematic errors are dangerous or discouraging, and thus may prove useful for robot-assisted movement training of walking or reaching following neurologic injury

An Industrial Robot-Based Rehabilitation System for Bilateral Exercises

Robot-assisted rehabilitation devices can provide intensive and precise task-based training that differs from clinician-facilitated manual therapy. However, industrial robots are still rarely used in rehabilitation, especially in bilateral exercises. The main purpose of this research is to develop and evaluate the functionality of a bilateral upper-limb rehabilitation system based on two modern industrial robots. A `patient-cooperative' control strategy is developed based on an adaptive admittance controller, which can take into account patients' voluntary efforts. Three bilateral training protocols (passive, active, and self) are also proposed based on the system and the control strategy. Experimental results from 10 healthy subjects show that the proposed system can provide reliable bilateral exercises: the mean RMS values for the master error and the master-slave error are all less than 1.00 mm and 1.15 mm respectively, and the mean max absolute values for the master error and the master-slave error are no greater than 6.11 mm and 6.73 mm respectively. Meanwhile, the experimental results also confirm that the recalculated desired trajectory can present the voluntary efforts of subjects. These experimental findings suggest that industrial robots can be used in bilateral rehabilitation training, and also highlight the potential applications of the proposed system in further clinical practices