Physical Human-Robot Interaction Control of an Upper Limb Exoskeleton with a Decentralized Neuro-Adaptive Control Scheme

Within the concept of physical human-robot interaction (pHRI), the most important criterion is the safety of the human operator interacting with a high degree of freedom (DoF) robot. Therefore, a robust control scheme is in high demand to establish safe pHRI and stabilize nonlinear, high DoF systems. In this paper, an adaptive decentralized control strategy is designed to accomplish the abovementioned objectives. To do so, a human upper limb model and an exoskeleton model are decentralized and augmented at the subsystem level to enable a decentralized control action design. Moreover, human exogenous force (HEF) that can resist exoskeleton motion is estimated using radial basis function neural networks (RBFNNs). Estimating both human upper limb and robot rigid body parameters, along with HEF estimation, makes the controller adaptable to different operators, ensuring their physical safety. The barrier Lyapunov function (BLF) is employed to guarantee that the robot can operate in a safe workspace while ensuring stability by adjusting the control law. Unknown actuator uncertainty and constraints are also considered in this study to ensure a smooth and safe pHRI. Then, the asymptotic stability of the whole system is established by means of the virtual stability concept and virtual power flows (VPFs) under the proposed robust controller. The experimental results are presented and compared to proportional-derivative (PD) and proportional-integral-derivative (PID) controllers. To show the robustness of the designed controller and its good performance, experiments are performed at different velocities, with different human users, and in the presence of unknown disturbances. The proposed controller showed perfect performance in controlling the robot, whereas PD and PID controllers could not even ensure stable motion in the wrist joints of the robot

Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot

This paper presents a teleoperation control for an exoskeleton robotic system based on the brain-machine interface and vision feedback. Vision compressive sensing, brain-machine reference commands, and adaptive fuzzy controllers in joint-space have been effectively integrated to enable the robot performing manipulation tasks guided by human operator's mind. First, a visual-feedback link is implemented by a video captured by a camera, allowing him/her to visualize the manipulator's workspace and movements being executed. Then, the compressed images are used as feedback errors in a nonvector space for producing steady-state visual evoked potentials electroencephalography (EEG) signals, and it requires no prior information on features in contrast to the traditional visual servoing. The proposed EEG decoding algorithm generates control signals for the exoskeleton robot using features extracted from neural activity. Considering coupled dynamics and actuator input constraints during the robot manipulation, a local adaptive fuzzy controller has been designed to drive the exoskeleton tracking the intended trajectories in human operator's mind and to provide a convenient way of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiment studies employing three subjects have been performed to verify the validity of the proposed method

Modelling of extended de-weight fuzzy control for an upper-limb exoskeleton

Performing heavy physical tasks, overhead work and long working hours are some examples of activities that can lead to musculoskeletal problems in humans. To overcome this issue, automated robots such as the upper-limb exoskeleton is used to assist humans while performing tasks. However, several concerns in developing the exoskeleton have been raised such as the control strategies used. In this study, a control strategy known as the extended de-weight fuzz was proposed to ensure that the exoskeleton could be maneuvered to the desired position with the least number of errors and minimum torque requirement. The extended de-weight fuzzy is a combination of the fuzzy-based PD and fuzzy-based de-weight controller systems. The extended de-weight fuzzy was then compared with the fuzzy-based PD and PID controllers, and the performances of these controllers were compared in terms of their deviations and required torques to perform tasks. The findings show that the proposed control strategy performs better than the fuzzy-based PD and PID controller systems

Design of a 4-DOF grounded exoskeletal robot for shoulder and elbow rehabilitation

The number of cerebrovascular and neuromuscular diseases is increasing in parallel with the rising average age of the world’s population. Since the shoulder anatomy is complex, the number of rehabilitation robots for shoulder movements is limited. This paper presents the mechanical design, control, and testing of 4 degrees of freedom (DOF) grounded upper limb exoskeletal robot. It is capable of four different therapeutic exercises (passive, active assistive, isotonic, and isometric). During the mechanical design, the forces to be exposed to the robot were determined and after the design, the system was tested with strength analysis. Also, a low-cost electromyograph device was developed and integrated into the system to measure muscular activation for feedback and instantaneously muscle activation control for the physiotherapist during the therapy. The system can be used for rehabilitation on the shoulder and elbow.  A PID controller for position-controlled exercises was developed. The test results were presented in terms of simulation and the real system for passive exercise. According to the test results, the developed system can perform the passive exercise and can be used for other therapeutic exercises as well

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

A flexible sensor technology for the distributed measurement of interaction pressure

We present a sensor technology for the measure of the physical human-robot interaction pressure developed in the last years at Scuola Superiore Sant'Anna. The system is composed of flexible matrices of opto-electronic sensors covered by a soft silicone cover. This sensory system is completely modular and scalable, allowing one to cover areas of any sizes and shapes, and to measure different pressure ranges. In this work we present the main application areas for this technology. A first generation of the system was used to monitor human-robot interaction in upper- (NEUROExos; Scuola Superiore Sant'Anna) and lower-limb (LOPES; University of Twente) exoskeletons for rehabilitation. A second generation, with increased resolution and wireless connection, was used to develop a pressure-sensitive foot insole and an improved human-robot interaction measurement systems. The experimental characterization of the latter system along with its validation on three healthy subjects is presented here for the first time. A perspective on future uses and development of the technology is finally drafted

Robotic exoskeletons: A perspective for the rehabilitation of arm coordination in stroke patients

Upper-limb impairment after stroke is caused by weakness, loss of individual joint control, spasticity, and abnormal synergies. Upper-limb movement frequently involves abnormal, stereotyped, and fixed synergies, likely related to the increased use of sub-cortical networks following the stroke. The flexible coordination of the shoulder and elbow joints is also disrupted. New methods for motor learning, based on the stimulation of activity- dependent neural plasticity have been developed. These include robots that can adaptively assist active movements and generate many movement repetitions. However, most of these robots only control the movement of the hand in space. The aim of the present text is to analyze the potential of robotic exoskeletons to specifically rehabilitate joint motion and particularly inter-joint coordination. First, a review of studies on upper-limb coordination in stroke patients is presented and the potential for recovery of coordination is examined. Second, issues relating to the mechanical design of exoskeletons and the transmission of constraints between the robotic and human limbs are discussed. The third section considers the development of different methods to control exoskeletons: existing rehabilitation devices and approaches to the control and rehabilitation of joint coordinations are then reviewed, along with preliminary clinical results available. Finally, perspectives and future strategies for the design of control mechanisms for rehabilitation exoskeletons are discussed

Development of a hybrid assist-as-need hand exoskeleton for stroke rehabilitation.

Stroke is one of the leading causes of disability globally and can significantly impair a patient’s ability to function on a daily basis. Through physical rehabilitative measures a patient may regain a level of functional independence. However, required therapy dosages are often not met. Rehabilitation is typically implemented through manual one-to-one assistance with a physiotherapist, which quickly becomes labour intensive and costly. Hybrid application of functional electrical stimulation (FES) and robotic support can access the physiological benefits of direct muscle activation while providing controlled and repeatable motion assistance. Furthermore, patient engagement can be heightened through the integration of a volitional intent measure, such as electromyography (EMG). Current hybrid hand-exoskeletons have demonstrated that a balanced hybrid support profile can alleviate FES intensity and motor torque requirements, whilst improving reference tracking errors. However, these support profiles remain fixed and patient fatigue is not addressed. The aim of this thesis was to develop a proof-of-concept assist-as-need hybrid exoskeleton for post-stroke hand rehabilitation, with fatigue monitoring to guide the balance of support modalities. The device required the development and integration of a constant current (CC) stimulator, stimulus-resistant EMG device, and hand-exoskeleton. The hand exoskeleton in this work was formed from a parametric Watt I linkage model that adapts to different finger sizes. Each linkage was optimised with respect to angular precision and compactness using Differential Evolution (DE). The exoskeleton’s output trajectory was shown to be sensitive to parameter variation, potentially caused by finger measurement error and shifts in coupler placement. However, in a set of cylindrical grasping trials it was observed that a range of movement strategies could be employed towards a successful grasp. As there are many possible trajectories that result in a successful grasp, it was deduced that the exoskeleton can still provide functional assistance despite its sensitivity to parameter variation. The CC stimulator developed in this work has a part cost of USD $145 and allows flexible adjustment of waveform parameters through an on-board micro-controller. The device is designed to output current up to ±30mA given a voltage compliance of ±50V. When applied across a 2kΩ load, the device exhibited a linear output transfer function, with a maximum ramp tracking error of 5 The stimulus-resistant EMG device builds on current designs by using a novel Schmitt trigger based artefact detection channel to adaptively blank stimulation artefacts without stimulator synchronisation. The design has a part cost of USD$150 and has been made open-source. The device demonstrated its ability to record EMG over its predominant energy spectrum during stimulation, through the stimulation electrodes or through separate electrodes. Pearson’s correlation coefficients greater than 0.84 were identified be- tween the normalised spectra of volitional EMG (vEMG) estimates during stimulation and of stimulation-free EMG recordings. This spectral similarity permits future research into applications such as spectral-based monitoring of fatigue and muscle coherence, posing an advantage over current same-electrode stimulation and recording systems, which can- not sample the lower end of the EMG spectrum due to elevated high-pass filter cut-off frequencies. The stimulus-resistant EMG device was used to investigate elicited EMG (eEMG)-based fatigue metrics during vEMG-controlled stimulation and hybrid support profiles. During intermittent vEMG-controlled stimulation, the eEMG peak-to-peak amplitude (PTP) index was the median frequency (MDF) had a negative correlation for all subjects with R > 0:62 during stimulation-induced wrist flexion and R > 0:55 during stimulation-induced finger flexion. During hybrid FES-robotic support trials, a 40% reduction in stimulus intensity resulted in an average 21% reduction in MDF gradient magnitudes. This reflects lower levels of fatigue during the hybrid support profile and indicates that the MDF gradient can provide useful information on the progression of muscle fatigue. A hybrid exoskeleton system was formed through the integration of the CC stimulator, stimulus-resistant EMG device, and the hand exoskeleton developed in this work. The system provided assist-as-need functional grasp assistance through stimulation and robotic components, governed by the user’s vEMG. The hybrid support profile demonstrated consistent motion assistance with lowered stimulation intensities, which in-turn lowered the subjects’ perceived levels of fatigue