Clinical Features to Predict the Use of a sEMG Wearable Device (REMO) for Hand Motor Training of Stroke Patients: A Cross-Sectional Cohort Study.

After stroke, upper limb motor impairment is one of the most common consequences that compromises the level of the autonomy of patients. In a neurorehabilitation setting, the implementation of wearable sensors provides new possibilities for enhancing hand motor recovery. In our study, we tested an innovative wearable (REMO) that detected the residual surface-electromyography of forearm muscles to control a rehabilitative PC interface.

Proportional estimation of finger movements from high-density surface electromyography.

Background: The importance to restore the hand function following an injury/disease of the nervous system led to the development of novel rehabilitation interventions. Surface electromyography can be used to create a user-driven control of a rehabilitation robot, in which the subject needs to engage actively, by using spared voluntary activation to trigger the assistance of the robot.

Methods: The study investigated methods for the selective estimation of individual finger movements from high-density surface electromyographic signals (HD-sEMG) with minimal interference between movements of other fingers.

Individual finger classification from surface EMG: Influence of electrode set.

The aim of this work was to minimize the number of channels, determining acceptable electrode locations and optimizing electrode-recording configurations to decode isometric flexion and extension of individual fingers. Nine healthy subjects performed cyclical isometric contractions activating individual fingers. During the experiment they tracked a moving visual marker indicating the contraction type (flexion/extension), desired activation level and the finger that should be employed.

On optimal electrode configuration to estimate hand movements from forearm surface electromyography.

Understanding the movement of the hand from sEMG signals acquired on the forearm is key in the development of future prosthetics of the upper limb. Despite the technical advancement on this technique, state of the art of sEMG still relies strongly on optimal electrode placement which is typically performed by a specialist by mean of a heuristic search. Involving a specialist has few major disadvantages including high costs and relatively long schedules.

Quantifying forearm muscle activity during wrist and finger movements by means of multi-channel electromyography.

The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements.