Published October 1, 2017 | Version v1
Dataset Open

Ninapro dataset 4 (Cometa Wave Plus + Dormo)

  • 1. Information Systems Institute at the University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland
  • 2. Department of Management and Engineering, University of Padova, Padova, Italy

Description

The 4th Ninapro database includes 10 intact subjects recorded with "Cometa" electrodes (http://www.cometasystems.com/).

The database is thoroughly described in the paper: "Pizzolato et al., Comparison of Six Electromyography Acquisition Setups on Hand Movement Classification Tasks, Plos One 2017 (accepted).".
Please, cite this paper for any work related to the 5th Ninapro database.

The dataset is part of the Ninapro database (http://ninapro.hevs.ch/). Please, look at the database for more information.

Acquisition Protocol
The subjects repeat several movements represented by movies that are shown on the screen of a laptop.
The experiment is divided in three exercises:
1. Basic movements of the fingers
2. Isometric, isotonic hand configurations and basic wrist movements
3. Grasping and functional movements
During the acquisition, the subjects were asked to repeat the movements with the right hand. Each movement repetition lasted 5 seconds and was followed by 3 seconds of rest.
The protocol includes 6 repetitions of 52 different movements (plus rest) performed by 10 intact subjects. The movements were selected from the hand taxonomy as well as from hand robotics literature.

Acquisition Setup
The muscular activity is gathered using 12 active single–differential wireless electrodes from Cometa. The electrodes are positioned as shown in the figure: eight electrodes are equally spaced around the forearm in correspondence to the radio humeral joint; two electrodes are placed on the main activity spots of the flexor digitorum and of the extensor digitorum; two electrodes are placed on the main activity spots of the biceps and of the triceps. The described locations have been chosen in order to combine a dense sampling approach with a precise anatomical positioning strategy.

 

The electrodes were fixed on the forearm using their standard adhesive bands.
The sEMG signals are sampled at a rate of 2 kHz.
During the acquisition, the subjects were asked to repeat the movements with the right hand. Each movement repetition lasted 5 seconds and was followed by 3 seconds of rest. The protocol includes 6 repetitions of 52 different movements (plus rest) performed by 10 intact subjects. The movements were selected from the hand taxonomy as well as from hand robotics literature.

Data Sets

For each exercise, for each subject, the database contains one matlab file with synchronized variables.
The variables included in the matlab files are:
• subject: the subject number;
• sensor: the name of the sEMG sensor;
• frequency: the frequency in Hertz of the recorded data
• exercise: exercise number;
• emg: sEMG signal. Columns 1-8 are the electrodes equally spaced around the forearm at the height of the radio humeral joint. Columns 9 and 10 contain signals from the main activity spot of the muscles flexor and extensor digitorum superficialis, while columns 11 and 12 contain signals from the main activity spot of the muscles biceps brachii and triceps brachii.
• stimulus: the original label of the movement repeated by the subject;
• restimulus: the corrected stimulus, processed with movement detection algorithms;
• repetition: stimulus repetition index;
• rerepetition: restimulus repetition index;
• age: subject’s age;
• gender: subject’s gender, ”m” for male ”f” for female;
• weight: subject’s weight in kilograms;
• height: subject’s height in centimeters;
• laterality: subject’s laterality, ”r” for right-handed, ”l” for left-handed;
• circumference: circumference of the subject’s forearm at the radio-humeral joint height, measured in centimeters;

Files

s1.zip

Files (2.3 GB)

Name Size Download all
md5:c5af132a26d8d6c682567e286f618385
224.2 MB Preview Download
md5:5216b9eef6c2f928480502704feda167
217.8 MB Preview Download
md5:838fa31aad40ff7a0c9cb01e9f04ad9c
233.9 MB Preview Download
md5:e2859345e07394adfdc9441422da93bc
233.2 MB Preview Download
md5:f1d504f7fceef84f88657b2c8fcc3a9f
226.7 MB Preview Download
md5:8de31457cc81e668e05a9c51075d5216
235.9 MB Preview Download
md5:40a00a62e87c4dc95fed219df728c072
230.4 MB Preview Download
md5:380de31e07dc8c023b5a6417ea550227
226.9 MB Preview Download
md5:1b6cbf648f79fc522c46b20acc765f3f
228.3 MB Preview Download
md5:0cc33c23286adca0354c5fed430f416e
231.3 MB Preview Download

Additional details

References

  • Stefano Pizzolato, Luca Tagliapietra, Matteo Cognolato, Monica Reggiani, Henning Müller, Manfredo Atzori, Comparison of Six Electromyography Acquisition Setups on Hand Movement Classification Tasks, Plos One, 2017 (accepted)