Myoelectric Arm Orthosis in Motor Learning-Based Therapy for Chronic Deficits After Stroke and Traumatic Brain Injury

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worldwide, affecting 2 % of the European population. This prevalence increases with age, imposing significant financial, economic, and human burdens. In Europe, stroke is the second leading cause of death and the primary cause of disability, with numbers expected to rise due to ageing and improved survival rates. Functional recovery from AF-related stroke is often unsatisfactory, leading to prolonged hospital stays, severe disability, and high mortality. Despite advances in AF and stroke research, the full pathophysiological and management issues between AF and stroke increasingly need innovative approaches such as artificial intelligence (AI) and machine learning (ML). Current risk assessment tools focus on static risk factors, neglecting the dynamic nature of risk influenced by acute illness, ageing, and comorbidities. Incorporating biomarkers and automated ECG analysis could enhance pathophysiological understanding. This paper highlights the need for personalised, integrative approaches in AF and stroke management, emphasising the potential of AI and ML to improve risk prediction, treatment personalisation, and rehabilitation outcomes. Further research is essential to optimise care and reduce the burden of AF and stroke on patients and healthcare systems.

Determining Predictors of Self-reported Adherence to Rehabilitation Home Programs for Persons with Acquired Brain Injury: A Prospective Observational Study

BackgroundPersons with acquired brain injury are often prescribed home programs to extend rehabilitation services and support recovery, however adherence to these recommendations is often low.ObjectiveThe purpose of this study was to determine factors that predict differing levels of self-reported adherence.MethodsThis was a prospective observational study that was completed from June 2022 to December 2023. We recruited persons with acquired brain injury who met specific inclusion and exclusion criteria. Before leaving the inpatient rehabilitation hospital, they completed a medical history form, home program description interview, and a modified version of the Self-Efficacy for Exercise Scale. Then, participants completed a weekly survey regarding self-reported home program adherence. At the end of six months, the participants did a closing interview.ResultsSeventy-seven participants completed the intake procedures and at least one weekly survey. Several models were developed to determine predictive factors. Higher self-efficacy, fewer reported barriers, and the perception of social support were all predictors of self-reported home program adherence.ConclusionThe predictors identified here can be leveraged by therapists to determine the need for additional support or intervention around home program adherence.

Proportional myoelectric control of a virtual bionic arm in participants with hemiparesis, muscle spasticity, and impaired range of motion

BACKGROUND

This research aims to improve the control of assistive devices for individuals with hemiparesis after stroke by providing intuitive and proportional motor control. Stroke is the leading cause of disability in the United States, with 80% of stroke-related disability coming in the form of hemiparesis, presented as weakness or paresis on half of the body. Current assistive exoskeletonscontrolled via electromyography do not allow for fine force regulation. Current control strategies provide only binary, all-or-nothing control based on a linear threshold of muscle activity.

METHODS

In this study, we demonstrate the ability of participants with hemiparesis to finely regulate their muscle activity to proportionally control the position of a virtual bionic arm. Ten stroke survivors and ten healthy, aged-matched controls completed a target-touching task with the virtual bionic arm. We compared the signal-to-noise ratio (SNR) of the recorded electromyography (EMG) signals used to train the control algorithms and the task performance using root mean square error, percent time in target, and maximum hold time within the target window. Additionally, we looked at the correlation between EMG SNR, task performance, and clinical spasticity scores.

RESULTS

All stroke survivors were able to achieve proportional EMG control despite limited or no physical movement (i.e., modified Ashworth scale of 3). EMG SNR was significantly lower for the paretic arm than the contralateral nonparetic arm and healthy control arms, but proportional EMG control was similar across conditions for hand grasp. In contrast, proportional EMG control for hand extension was significantly worse for paretic arms than healthy control arms. The participants' age, time since their stroke, clinical spasticity rate, and history of botulinum toxin injections had no impact on proportional EMG control.

CONCLUSIONS

It is possible to provide proportional EMG control of assistive devices from a stroke survivor's paretic arm. Importantly, information regulating fine force output is still present in muscle activity, even in extreme cases of spasticity where there is no visible movement. Future work should incorporate proportional EMG control into upper-limb exoskeletons to enhance the dexterity of stroke survivors.

Improved Disabilities of the Arm, Shoulder and Hand scores after myoelectric arm orthosis use at home in chronic stroke: A retrospective study

BACKGROUND

Most stroke survivors have persistent upper limb impairments after completing standard clinical care. The resulting impairments can adversely affect their quality of life and ability to complete self-care tasks and remain employed, leading to increased healthcare and societal costs. A myoelectric arm orthosis can be used effectively to support the affected weak arm and increase an individual's use of that arm.

OBJECTIVE

The study objective was to retrospectively evaluate the outcomes and clinical benefits provided by the MyoPro® orthosis in individuals 65 years and older with upper limb impairment secondary to a stroke.

METHODS

The Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire was administered to individuals who have chronic stroke both before and after receiving their myoelectric orthosis. A Generalized Estimating Equation model was analyzed.

RESULTS

After using the MyoPro, 19 individuals with chronic stroke had a mean improvement (decrease) in DASH score of 18.07, 95% CI = (-25.41, -10.72), adjusted for 8 covariates. This large change in DASH score was statistically significant and clinically meaningful as participants self-reported an improvement with engagement in functional tasks.

CONCLUSIONS

Use of the MyoPro increases independence in functional tasks as reported by the validated DASH outcome measure for older participants with chronic stroke.

Myoelectric Arm Orthosis Assists Functional Activities: A 3-Month Home Use Outcome Report

Objective

The objective was to compare task performance in individuals with upper limb impairments with and without a myoelectric arm orthosis.

Design

Three-month observational study. Participants met at 4 time points after receiving their myoelectric orthosis (2-Weeks, Month-1, Month-2, Month-3) to complete 4 standardized common daily tasks.

Setting

Nationwide sessions completed remotely over videoconference calls at home. There were no specific clinic affiliations.

Participants

Adults with upper limb impairment due to stroke who were in the process of being fit with a myoelectric arm orthosis as a first-time user.

Interventions

The orthosis was a custom-fabricated myoelectric arm orthosis called the MyoPro®.

Main Outcome Measures

Functional tasks were completed at each session with and without the MyoPro. Participants were evaluated on their success and the time required to complete each functional task. Longitudinal mixed and longitudinal mixed logistic regression models were analyzed.

Results

Eighteen individuals with chronic arm weakness due to stroke were included in the analysis. Statistically significant and clinically meaningful improvements were observed on the functional tasks in the participants' homes. By 3 months, participants successfully used the MyoPro to accomplish the tasks, reduced the amount of time spent to complete the tasks, and had a higher probability of success as compared with at 2 weeks. With the MyoPro, participants showed significant improvement in overall task completion and completed the tasks in a significantly decreased time as compared with without the MyoPro.

Conclusions

The MyoPro provides a stabilizing support to the weak arm of individuals after stroke and enables individuals to use their impaired arm to complete functional tasks independently in the home environment.