Biophysiological Signals

Accurate proportional myo-electric control of the hand is important in replicating dexterous manipulation in robot prostheses. Many studies in this field have focused on recording discrete hand gestures, while few have focused on the proportional and multiple-DOF control of the human hand using EMG signals. To aid researchers on advanced myoelectric hand control and estimation, we present this data from our work "Extraction of nonlinear muscle synergies for proportional and simultaneous estimation of finger kinematics".  

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The databases include arrays of alphabets and numbers, ["Ka Gyi" "Ka Kway" "Ga Nge" "Ga Gyi" "Nga" "Sa Lone" "Sa Lane" "0" '0' "Nya" "Ta Talin Jade" "Hta Won Bell" "Dain Yin Gouk" "Dain Yin Hmote" "Na Gyi" "Ta Won Bu" "Hta Sin Htoo" "Da Dway" "Da Out Chike" "Na Nge" "Pa Sout" "Pha Oo Htote" "Ba Htet Chike" "Ba Gone" "Ma" "Ya Pa Lat" "Ya Gout" "La" "Wa" "Tha" "Ha" "La Gyi" "Ah" '0' '1' '2' '3' '4' '5' '6' '7' '8' '9' "10"]. The symbols are recorded as gestures of palm by the MIIT research team and recorded audio file also for each number and alphabet.

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We provide a large benchmark dataset consisting of about: 3.5 million keystroke events; 57.1 million data-points for accelerometer and gyroscope each; and 1.7 million data-points for swipes. Data was collected between April 2017 and June 2017 after the required IRB approval. Data from 117 participants, in a session lasting between 2 to 2.5 hours each, performing multiple activities such as: typing (free and fixed text), gait (walking, upstairs and downstairs) and swiping activities while using desktop, phone and tablet is shared.

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The MyoUP (Myo University of Patras) database contains recordings from 8 intact subjects (3 females, 5 males; 1 left handed, 7 right handed; age 22.38 ± 1.06 years). The acquisition process was divided into three parts: 5 basic finger movements (E1), 12 isotonic and isometric hand configurations (E2), and 5 grasping hand-gestures (E3). The recording device used was the Myo Armband by Thalmic labs (8 dry sEMG channels and sampling frequency of 200Hz). The dataset was created for use in gesture recognition tasks.

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This database contains the results of an experiment were healthy subjects played 5 trials of a rehabilitation-based VR game, to experience either difficulty variations or presence variations.

Colected results are demogrpahic information, emotional emotions after each trial and electrophysiological signals during all 5 trials.

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This database contains the 166 Galvanic Skin Response (GSR) signal registers collected from the subjects participating in the first experiment (EXP 1) presented in:

R. Martinez, A. Salazar-Ramirez, A. Arruti, E. Irigoyen, J. I. Martin and J. Muguerza, "A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis," in IEEE Access, vol. 7, pp. 43730-43741, 2019. doi: 10.1109/ACCESS.2019.2908445

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Motor point identification is pivotal to elicit comfortable and sustained muscle contraction through functional electrical stimulation. To this purpose, anatomical charts and manual search techniques are used to extract subject-specific stimulation profile. Such information being heterogenous they lack standardization and reproducibility. To address these limitations; we aim to identify, localize, and characterize the motor points of forearm muscles across nine healthy subjects.

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FRAP curve modeling using transient-sensitive analog computer unit with oscilloscopic CRT (Practicum, 2014)

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The data is obtained from electrocardiography, using flexible electrode, Ag/AgCl electrode and Metal Clamp electrode of a femal subject, age 22 years old.

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This Free CAD files are for the manuscript "A temperature-controlled patch-clamp platform demonstrated on Jurkat T lymphocytes and human stem cell derived neurons". The files allow for easily 3D-printing a housing box for the electronics. 

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