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In this study, an electromyography (EMG) signal-based learning is integrated with a Sliding-Mode Control (SMC) law for an effective human-exoskeleton synergy. A modified Recursive Newton-Euler Algorithm (RNEA) with SMC was used to determine and control the inverse dynamics of a highly nonlinear 4 degree-of-freedom exoskeleton designed for the automation of upper-limp therapeutic exercises. The exoskeleton position and velocity, along with the raw EMG signal from the bicep Brachii muscle were used as a feedback. The root mean square (RMS) values of targeted muscles EMG are tracked in a predetermined time window to quantify an adaptive threshold value and control the torque at the exoskeleton joints accordingly. Simulations of the proposed robust control law have been done in MATLAB-Simulink. Results have shown that the designed hybrid Control strategy offers the ability to adjust the needed support instantly based on the amount of muscle engagement presented in the combined motion of the human-exoskeleton system while maintaining the state trajectory errors and input torque bounded to ±7 × 10−3 rads and ±5 N.m, respectively.

 

Human-exoskeleton misalignment could lead to permanent damages upon the targeted limb with long-term use in rehabilitation. Hence, achieving proper alignment is necessary to ensure patient safety and an effective rehabilitative journey. In this study, a joint-based and task-based exoskeleton for upper limb rehabilitation were modeled and assessed. The assessment examined and quantified the misalignment present at the elbow joint as well as its effects on the main flexor and extensor muscles’ tendon length during elbow flexion-extension. The effects of the misalignments found for both exoskeletons resulted to be minimal in most muscles observed, except the anconeus and brachialis. The anconeus muscle demonstrated a relatively higher variation in tendon length with the joint-based exoskeleton misalignment, indicating that the task-based exoskeleton is favored for tasks that involve this particular muscle. Moreover, the brachialis demonstrated a significantly higher variation with the task-based exoskeleton misalignment, indicating that the joint-based exoskeleton is favored for tasks that involve the muscle.

 

This paper presents an adaptive Fuzzy Sliding Mode Control approach for an Assist-as-Needed (AAN) strategy to achieve effective human–exoskeleton synergy. The proposed strategy employs an adaptive instance-based learning algorithm to estimate muscle effort, based on surface Electromyography (sEMG) signals. To determine and control the inverse dynamics of a highly nonlinear 4-degrees-of-freedom exoskeleton designed for upper-limb therapeutic exercises, a modified Recursive Newton-Euler Algorithm (RNEA) with Sliding Mode Control (SMC) was used. The exoskeleton position error and raw sEMG signal from the bicep’s brachii muscle were used as inputs for a fuzzy inference system to produce an output to adjust the sliding mode control law parameters. The proposed robust control law was simulated using MATLAB-Simulink, and the results showed that it could instantly adjust the necessary support, based on the combined motion of the human–exoskeleton system’s muscle engagement, while keeping the state trajectory errors and input torque bounded within ±5×10−2 rads and ±5 N.m, respectively.

 

This research presents an Assist-as-Needed (AAN) Algorithm for controlling a bio-inspired exoskeleton, specifically designed to aid in elbow-rehabilitation exercises. The algorithm is based on a Force Sensitive Resistor (FSR) Sensor and utilizes machine-learning algorithms that are personalized to each patient, allowing them to complete the exercise by themselves whenever possible. The system was tested on five participants, including four with Spinal Cord Injury and one with Duchenne Muscular Dystrophy, with an accuracy of 91.22%. In addition to monitoring the elbow range of motion, the system uses Electromyography signals from the biceps to provide patients with real-time feedback on their progress, which can serve as a motivator to complete the therapy sessions. The study has two main contributions: (1) providing patients with real-time, visual feedback on their progress by combining range of motion and FSR data to quantify disability levels, and (2) developing an assist-as-needed algorithm for rehabilitative support of robotic/exoskeleton devices.

 

Exoskeletons and robots have been used as a common practice to assist and automate rehabilitation exercises. Exoskeleton fitting and alignments are important factors and challenges that need to be addressed for smooth and safe operations and better outcomes. Such challenges often dictate the exoskeleton design approaches. Some focus on simplifying and mimicking human joints (joint-based) while others have a focus on a specific task (task-based), which does not need to align with the corresponding limb joint/s to generate the desired anatomical motion. In this study, the two design approaches are assessed in an elbow flexion-extension task. The muscle responses have been collected and compared with and without the exoskeletons. Based on 6 with no disability participants, the normalized Electromyography (EMG) RMS values are plotted. The plot profiles and magnitudes are used as a base to assess the exoskeleton alignment. For this specific task, the task-based exoskeleton has shown a profile closer to the one without exoskeleton with a relatively identical support as the joint-based one; the latter is evidenced through most subjects’ muscle response magnitudes. This preliminary data has shown a good methodology and insight towards the assessment of exoskeletons, but more human subject data is needed with different task combinations to further strengthen the findings. 

Neuromuscular and sensorimotor degeneration caused by stroke or any other disease significantly reduce the physical, cognitive, and social well-being across the life span. Mostly, therapeutic interventions are employed in order to restore the lost degrees-of-freedom (DOF) caused by such impairments and automating these therapeutic tasks through exoskeletons/robots is becoming a common practice. However, aligning these robotic devices with the complex anatomical and geometrical motions of the joints is very challenging. At the same time, a good alignment is required in order to establish a better synergy of human-exoskeleton system for an effective intervention procedure. In this paper, a case study of an exoskeleton and shoulder joint alignment were studied through different size and orientation impairment models through motion capture data and musculoskeletal modeling in OpenSim. A preliminary result indicates that shoulder elevation is very sensitive to misalignment and varies with shoulder joint axes orientation; this is partly due to drastic displacement of the upper arm axes with respect to the shoulder joint origin during elevation. Additional study and analysis is required to learn any possible restraint on shoulder elevation that could potentially help in the exoskeleton development. 

In this study, a methodology for designing a task-based exoskeleton which can recreate the end-effector trajectory of a given limb during a rehabilitation task/movement is presented. The exoskeleton provides an option to replace traditional joint-based exoskeleton joints, which often have alignment issues with the biological joint. The proper fit of the exoskeleton to the user and task are research topics to reduce pain or joint injuries as well as for the execution of the task. The proposed task-based synthesis method was successfully applied to generate the 3D motions of the elbow flexion and extensions using a one degree of freedom (DOF), spatial four-bar mechanism. The elbow joint is analyzed through motion capture system to develop the bio-exoskeleton. The resulted exoskeleton does not need to align with the corresponding limb joint to generate the desired anatomical motion.

 

Abstract This study presents robot-based rehabilitation and its assessment. Robotic devices have significantly been useful to help therapists do the training procedure consistently. However, as robotic devices interface with humans, quantifying the interaction and its intended outcomes is still a research challenge. In this study, human–robot interaction during rehabilitation is assessed based on measurable interaction forces and human physiological response data, and correlations are established to plan the intervention and effective limb trajectories within the intended rehabilitation and interaction forces. In this study, the Universal Robot 5 (UR5) is used to guide and support the arm of a subject over a predefined trajectory while recording muscle activities through surface electromyography (sEMG) signals using the Trigno wireless DELSYS devices. The interaction force is measured through the force sensor mounted on the robot end-effector. The force signals and the human physiological data are analyzed and classified to infer the related progress. Feature reduction and selection techniques are used to identify redundant inputs and outputs.