Robotic Rehabilitation and Multimodal Instrumented Assessment of Post-stroke Elbow Motor Functions—A Randomized Controlled Trial Protocol

Abstract

Background: The reliable assessment, attribution, and alleviation of upper-limb joint stiffness are essential clinical objectives in the early rehabilitation from stroke and other neurological disorders, to prevent the progression of neuromuscular pathology and enable proactive physiotherapy toward functional recovery. However, the current clinical evaluation and treatment of this stiffness (and underlying muscle spasticity) are severely limited by their dependence on subjective evaluation and manual limb mobilization, thus rendering the evaluation imprecise and the treatment insufficiently tailored to the specific pathologies and residual capabilities of individual patients. Methods: To address these needs, the proposed clinical trial will employ the NEUROExos Elbow Module (NEEM), an active robotic exoskeleton, for the passive mobilization and active training of elbow flexion and extension in 60 sub-acute and chronic stroke patients with motor impairments (hemiparesis and/or spasticity) of the right arm. The study protocol is a randomized controlled trial consisting of a 4-week functional rehabilitation program, with both clinical and robotically instrumented assessments to be conducted at baseline and post-treatment. The primary outcome measures will be a set of standard clinical scales for upper limb spasticity and motor function assessment, including the Modified Ashworth Scale and Fugl-Meyer Index, to confirm the safety and evaluate the efficacy of robotic rehabilitation in reducing elbow stiffness and improving function. Secondary outcomes will include biomechanical, muscular activity, and motor performance parameters extracted from instrumented assessments using the NEEM along with synchronous EMG recordings. Conclusions: This randomized controlled trial aims to validate an innovative instrumented methodology for clinical spasticity assessment and functional rehabilitation, relying on the precision and accuracy of an elbow exoskeleton combined with EMG recordings and the expertise of a physiotherapist, thus complementing and maximizing the benefits of both practices. Pilla et al. Robotic Assessment and Treatment of Elbow Spasticity Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT04484571.

Leap Motion Controller Video Game-Based Therapy for Upper Extremity Motor Recovery in Patients with Central Nervous System Diseases. A Systematic Review with Meta-Analysis

“…Severe motor impairments in the UE, mainly muscle weakness and spasticity [ 81 ], are among the most disabling consequences that appear in patients diagnosed with CNSD and can reduce their and their families’ quality of life [ 82 , 83 ]. The recovery of the UE motor function of these patients is essential to improve the functional capacity and increase personal autonomy to perform ADLs.…”

Clinical Interpretation of Working Volume and Weight Support in Upper Limb Robotic Neurorehabilitation after Stroke

“…At T1 and T2, the data related to joint ROMs and the total working volume obtained with passive mobilization of the paretic upper limb performed by a therapist up to the articular limit in the three planes of the space were collected, together with the weight support selected for performing the proposed exergames and analyzed with Armeo ® Control Software (Figure 1). According to previous studies [11][12][13]21,22], the assessment procedure was performed with the measure of the patient's passive range of motion with the robotic device in a kinematic transparent mode, and the therapist moving the affected limb linked to the exoskeleton for exploring the entire reachable space to interact with the video game during therapy without experiencing resistance to the movement or elicit pain.…”

Reducing Waste in 3D Printing Using a Neural Network Based on an Own Elbow Exoskeleton

“…Spasticity assessment and functional rehabilitation based on the precision and accuracy of the elbow exoskeleton combined with electromyography (EMG) recording and the physiotherapist’s experience are complementary, and through synergy, maximize the benefits of both practices [ 5 , 6 ]. The aforementioned accuracy of the upper limb activation model, according to a study by Jiang et al, for: Convolutional neural network (CNN) in recognizing movement patterns from surface EMG was 97.57% for normal-speed movements and 97.07% for fast-speed movements [ 7 ]; Cross-subject CNN in motion pattern recognition was 79.64% [ 7 ]; and Cross-device CNN in motion pattern recognition was 88.93% for normal-speed movements and 80.87% for mixed-speed movements [ 7 ].…”

“…Spasticity assessment and functional rehabilitation based on the precision and accuracy of the elbow exoskeleton combined with electromyography (EMG) recording and the physiotherapist's experience are complementary, and through synergy, maximize the benefits of both practices [5,6]. The aforementioned accuracy of the upper limb activation model, according to a study by Jiang et al, for:…”