ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis for Stroke.

Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection. Additionally, EMG signals exhibit significant variations across different conditions, sessions, and subjects, making it hard for classifiers to generalize. Traditional approaches require a large labeled dataset from the new condition, session, or subject to train intent classifiers; however, this data collection process is burdensome and time-consuming.

Volitional Control of the Paretic Hand Post-Stroke Increases Finger Stiffness and Resistance to Robot-Assisted Movement.

Increased effort during use of the paretic arm and hand can provoke involuntary abnormal synergy patterns and amplify stiffness effects of muscle tone for individuals after stroke, which can add difficulty for user-controlled devices to assist hand movement during functional tasks. We study how volitional effort, exerted in an attempt to open or close the hand, affects resistance to robot-assisted movement at the finger level. We perform experiments with three chronic stroke survivors to measure changes in stiffness when the user is actively exerting effort to activate ipsilateral EMG-controlled robot-assisted hand movements, compared with when the fingers are passively stretched, as well as overall effects from sustained active engagement and use.

Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis.

Purpose: Wearable robotic devices are currently being developed to improve upper limb function for individuals with hemiparesis after stroke. Incorporating the views of clinicians during the development of new technologies can help ensure that end products meet clinical needs and can be adopted for patient care.

Methods: In this cross-sectional mixed-methods study, an anonymous online survey was used to gather clinicians' perceptions of a wearable robotic hand orthosis for post-stroke hemiparesis.

Motor Learning Following Stroke: Mechanisms of Learning and Techniques to Augment Neuroplasticity.

Sensorimotor impairments are common after stroke requiring stroke survivors to relearn lost motor skills or acquire new ones in order to engage in daily activities. Thus, motor skill learning is a cornerstone of stroke rehabilitation. This article provides an overview of motor control and learning theories that inform stroke rehabilitation interventions, discusses principles of neuroplasticity, and provides a summary of practice conditions and techniques that can be used to augment motor learning and neuroplasticity in stroke rehabilitation.

Overground Robotic Gait Trainer mTPAD Improves Gait Symmetry and Weight Bearing in Stroke Survivors.

Stroke is a leading cause of disability, impairing the ability to generate propulsive forces and causing significant lateral gait asymmetry. We aim to improve stroke survivors' gaits by promoting weight-bearing during affected limb stance. External forces can encourage this; e.