Welcome

Welcome to Ninaweb, the web interface of Ninapro, of ProHand and of PaWFE.
ProHand is an innovative robotic prosthetic hand controlled with machine learning and produced with additive manufacturing.
PaWFE is a signal feature extraction code that can extract widely used signal features in parallel, strongly reducing computational times.
Ninapro is a a publicly available multimodal database to foster research on robotic & prosthetic hands controlled with artificial intelligence.
Ninapro includes electromyography, kinematic, inertial, eye tracking, visual, clinical and neurocognitive data. Ninapro data are used worldwide by scientific researchers in machine learning, robotics, medical and neurocognitive sciences.
Please, create a user profile and feel free to download one of the 10 multi-modal datasets currently available, including for instance surface electromyography (sEMG), intertial, hand kinematics, hand dyanamics, eye tracking, visual, behavioral and clinical data.
Dataset 1 includes data from 27 intact subjects.
Dataset 2 includes data from 40 intact subjects.
Dataset 3 includes data from 11 transradial amputees (with amputation levels as represented in the figure at the end of the page).
Dataset 4 includes 10 intact subjects recorded with "Cometa" electrodes.
Dataset 5 includes 10 intact subjects recorded with two Thalmic Myo armbands, putting them on the same forearm simultaneously.
Dataset 6 contains repeatability data from 10 intact subjects repeating data acquisitions 2 times per day for 5 days.
Dataset 7 contains data from 20 intact subjects and 2 transradial amputees.
Dataset 8 is intended to be used for estimation/reconstruction of finger movement rather than motion/grip classification. Data are from 10 intact subjects and 2 amptuees.
Dataset 9 includes kinematic data from 77 intact subjects acquired with a Cyberglove-II.
Dataset 10, MeganePro, is the first multimodal database from intact and hand amputated subjects including sEMG, intertial, gaze tracking, visual, behavioral and clinical data for prosthetics and the analysis of phantom limb sensation.
Despite intact subjects results can be used as a proxy measure for hand movement classification of hand amputees (Atzori et al., EMBC 2014), we recommend to include data from hand amputees whenever possible.
Considering the data from amputees, several clinical parameters related to the amputation can significantly influence results, as described in Atzori et al., Journal of Rehabilitation Research and Development, 2016. This fact should be properly considered while analyzing the data and presenting the results.

We really hope that you will enjoy Ninaweb.
Please, contact us if you would like to get involved into one of our projects.

The Ninaweb team