Towards NIRS-based hand movement recognition.

This work reports on preliminary results about on hand movement recognition with Near InfraRed Spectroscopy (NIRS) and surface ElectroMyoGraphy (sEMG). Either basing on physical contact (touchscreens, data-gloves, etc.), vision techniques (Microsoft Kinect, Sony PlayStation Move, etc.

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.