Biophysiological Signals
Each voice sample is stored as a .WAV file, which is then pre-processed for acoustic analysis using the specan function from the WarbleR R package. Specan measures 22 acoustic parameters on acoustic signals for which the start and end times are provided.
The output from the pre-processed WAV files were saved into a CSV file, containing 3168 rows and 21 columns (20 columns for each feature and one label column for the classification of male or female).
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Synergistic prostheses enable the coordinated movement of the human-prosthetic arm, as required by activities of daily living. This is achieved by coupling the motion of the prosthesis to the human command, such as residual limb movement in motion-based interfaces. Previous studies demonstrated that developing human-prosthetic synergies in joint-space must consider individual motor behaviour and the intended task to be performed, requiring personalisation and task calibration.
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Ear-EEG recording collects brain signals from electrodes placed in the ear canal. Compared with existing scalp-EEG, ear-EEG is more wearable and user-comfortable compared with existing scalp-EEG.
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One subject, five different movements, four levels of motor imagery data.The sampling rate is 25Hz, a total of 33,000 lines.
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This dataset provides the ECG signals recorded in ambulatory (moving) conditions of subjects. The ambulatory ECG (A-ECG) data acquired with two different recorders viz. Biopac MP36 Acquisition system and a self-developed wearable ECG recorder are made available. Total 10 subjects' (with avg. age of 27 years, 1 female and 9 males) ECG signals with four body movements- Left & Right arm up/down, Sitting down & standing up and Waist twist are uploaded.
An EEG signals dataset is also provided here.
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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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