When stroke survivors perform rehabilitation exercises in clinical settings, experienced therapists can evaluate the associated quality of movements by observing only the initial part of the movement execution so that they can discourage therapeutically undesirable movements effectively and reinforce desirable ones as much as possible in the limited therapy time. This paper introduces a novel monitoring platform based on wearable technologies that can replicate the capability of skilled therapists. Specifically, we propose to deploy five wearable sensors on the trunk, and upper and forearm of the two upper limbs, analyze partial to complete observation data of reaching exercise movements, and employ supervised machine learning to estimate therapists’ evaluation of movement quality. Estimation performance was evaluated using F-Measure, Receiver Operating Characteristic Area, and Root Mean Square Error, showing that the proposed system can be trained to evaluate the movement quality of the entire exercise movement using as little as the initial 5s of the exercise performance. The proposed platform may help ensure high quality exercise performance and provide virtual feedback of experienced therapists during at-home rehabilitation.
Stroke is a leading cause of death and disabilities in adults, and the majority of its survivors suffer from upper extremity paresis . There is scientific evidence that repetitive rehabilitation exercises and training could improve motor abilities as a result of motor learning processes . Among many, a reaching movement is a fundamental component of daily movement that requires the coordination of multiple upper extremity segments . It is shown that repetitive reaching exercises improve the smoothness, precision, and speed of arm movements . To continue to improve and to sustain motor function, it is clinically important that patients continue to engage in rehabilitation exercises even outside the clinical settings , which emphasizes the importance of the home-based therapy.
via A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study – IEEE Conference Publication
Recent technological developments regarding wearable soft-robotic devices extend beyond the current application of rehabilitation robotics and enable unobtrusive support of the arms and hands during daily activities. In this light, the HandinMind (HiM) system was developed, comprising a soft-robotic, grip supporting glove with an added computer gaming environment. The present study aims to gain first insight into the feasibility of clinical application of the HiM system and its potential impact. In order to do so, both the direct influence of the HiM system on hand function as assistive device and its therapeutic potential, of either assistive or therapeutic use, were explored. A pilot randomized clinical trial was combined with a cross-sectional measurement (comparing performance with and without glove) at baseline in 5 chronic stroke patients, to investigate both the direct assistive and potential therapeutic effects of the HiM system. Extended use of the soft-robotic glove as assistive device at home or with dedicated gaming exercises in a clinical setting was applicable and feasible. A positive assistive effect of the soft-robotic glove was proposed for pinch strength and functional task performance `lifting full cans’ in most of the five participants. A potential therapeutic impact was suggested with predominantly improved hand strength in both participants with assistive use, and faster functional task performance in both participants with therapeutic application.
Neurorehabilitation research has shown that training programs for patients after stroke should ideally consist of high intensity, task-specific and functional exercises with active contribution of the patient, to have the best chance for improving arm/hand function , . Conventional rehabilitation involves predominantly one-to-one attention of a therapist for each patient, which is a challenge when aiming to provide high intensity training and involves high costs , . This is impeded further by an increased ageing of the population, associated with a higher prevalence of stroke patients and less healthcare professionals available to provide such intensive training.
via Applying a soft-robotic glove as assistive device and training tool with games to support hand function after stroke: Preliminary results on feasibility and potential clinical impact – IEEE Conference Publication
Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user’s movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.
Stroke is a leading cause of adult disability around the world. A large number of stroke survivors are left with a unilateral arm or leg paralysis. After completing conventional rehabilitation therapy, a significant number of stroke survivors are left with limited reaching and grasping capabilities .
Source: Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient – IEEE Xplore Document
Treatment options for stroke survivors with severe hand impairment are limited. Active task practice can be restricted by difficulty in voluntarily activating finger muscles and interference from involuntary muscle excitation.
We developed a portable, actuated glove-orthosis, which could be employed to address both issues. We hypothesized that combining passive cyclical stretching (reducing motoneuronal hyperexcitability) imposed by the device with active-assisted, task-oriented training (rehabilitating muscle activation) would improve upper extremity motor control and task performance post-stroke.
Thirteen participants who experienced a stroke 2-6 months prior to enrollment completed 15 treatment sessions over five weeks. Each session involved cyclically stretching the long finger flexors (30 min) followed by active-assisted task-oriented movement practice (60 min). Outcome measures were completed at six intervals: three before and three after treatment initiation.
Overall improvement in post-training scores was observed across all outcome measures, including the Graded Wolf Motor Function Test, Action Research Arm Test, and grip and pinch strength ( p ≤ 0.02), except finger extension force. No significant change in spasticity was observed. Improvement in upper extremity capabilities is achievable for stroke survivors even with severe hand impairment through a novel intervention combining passive cyclical stretching and active-assisted task practice, a paradigm which could be readily incorporated into the clinic.
Source: IEEE Xplore Abstract – Use of a Portable Assistive Glove to Facilitate Rehabilitation in Stroke Survivors With Severe Hand …