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192 results for “emg”
PsPM-TC: SCR, ECG, EMG and respiration measurements in a discriminant trace fear conditioning task with visual CS and electrical US.
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information, keypress responses and keypress response times from 18 healthy unmedicated participants (8 males and 10 females aged 23.89+/-2.52 years) participating in a classical (Pavlovian) discriminant trace fear conditioning task. CS were a red and a blue rectangle presented for 3 seconds. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 10 Hz frequency. SOA between the CS onset and US was 4 s. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p>
PsPM-SC4B: SCR, ECG, EMG, PSR and respiration measurements in a delay fear conditioning task with auditory CS and electrical US
<p>This dataset includes pupil size response (PSR), skin conductance response (SCR), electrocardiogram (ECG), electromyogram (EMG) and respiration measurements. Also included are CS and US information, keypress responses, keypress response times, key correctness and shock ratings for each of 21 healthy unmedicated participants (10 males and 11 females aged 22.9+/-3.0 years; discrepancies to published studies are due to misprints and exclusion of a subject with incomplete data, which was excluded in all conducted studies as well as here) participating in a classical (Pavlovian) discriminant delay fear conditioning task. Two pairs of CS+ and CS-, either complex or simple, were delivered with headphones (HD518, Sennheiser, Wedemark-Wennebostel, Germany) at about 68 dB. Complex stimuli were a sequence of four rising (400 to 800 Hz) or falling (800 to 400 Hz) sounds lasting 1 s each. Simple stimuli were tones with constant frequency (400 or 800 Hz) presented for 4 s. US consisted of square electric pulses with 0.2-ms duration and 10 Hz frequency, resulting in a total US duration of 0.5 s. SOA betwen the CS and US was 3.5 s. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p>
PsPM-DoxMem2: Pupil, SCR, ECG, EMG and respiration measurement in a classical pavlovian discriminant delay fear conditioning task, reminder under doxycycline/placebo, retention and re-learning
<p>This dataset includes eyetracker, skin conductance response (SCR), electrocardiogram (ECG), respiration and electromyogram (EMG, only relevant for retention phase) measurements. Also included are CS and US information, keypress responses and keypress response times for 79 healthy participants (40 males and 39 females aged 24.8+/-4.9 years). Participants underwent a classical (Pavlovian) discriminant delay fear conditioning task with 1 CS- and 2 CS+ (50% reinforcement), were reminded of one CS+ one week later under either doxycycline or placebo, and were tested in a retention/extinction and re-learning task another week later. CS were isoluminant coloured triangles. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 500 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. Before the fear conditioning task, participants completed several questionnaires. During the retention/extinction phase, an auditory startle probe (ST) and no US was delivered 3.5 s after CS onset via headphones (102 dB, 40 ms duration with 2 ms on- and offset ramp). In an immediately following re-learning phase, the ST was omitted and the CS reinforced with the same schedule as during acquisition. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p> <p> </p>
Emotion-Antecedent Appraisal Checks: EEG and EMG datasets for Novelty and Pleasantness
<p>The Electroencaphalography (EEG) and facial Electromyography (EMG) signals included in this data set were collected in the context of a previous study conducted by van Peer, Grandjean and Scherer (2014). That study addressed three fundamental questions regarding the mechanisms underlying the appraisal process: Whether appraisal criteria are processed (a) in a fixed sequence, (b) independent of each other, and (c) by different neural structures or circuits. In that study, an oddball paradigm with affective pictures was used to experimentally manipulate novelty and intrinsic pleasantness appraisals. EEG was recorded during task performance, together with facial EMG, to measure, respectively, cognitive processing and efferent responses stemming from the appraisal manipulations. The data set made here publicly available contains the exact same data used by Coutinho, Gentsch, van Peer, Scherer and Schuller (to appear). The only difference in relation to the original data is that the some of the pre-processing steps (i.e., the processing of the raw data) were changed in order to improve the detection of artifacts. The full details of the original study, data collected, pre-processing steps and final data set are included in the paper distributed with the data (study1_dataset.pdf).</p> <p><strong>References</strong></p> <p>Coutinho E, Gentsch k, van Peer JM, Scherer KR & Schuller BW (to appear). Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography. <em>PloS One</em>.</p> <p>van Peer JM, Grandjean D, Scherer KR (2014). Sequential unfolding of appraisals: EEG evidence for the interaction of novelty and pleasantness. <em>Emotion, </em>14(1), 51-63.</p>
Emotion-Antecedent Appraisal Checks: EEG and EMG datasets for Goal Conduciveness, Control and Power
<p>The Electroencaphalography (EEG) and facial Electromyography (EMG) signals included in this dataset was collected in the context of a previous study (Gentsch, Grandjean & Scherer, 2013). This dataset contains the exact data used in Coutinho, Gentsch, van Peer, Scherer & Schuller (to appear). The only difference in relation to the original data is that the some of the pre-processing steps (i.e., the processing of the the raw data) were changed. The full details of the data collected and pre-processing are included in a file distributed with the data (dataset-details.pdf).</p> <p>References</p> <p>Coutinho, Gentsch, van Peer, Scherer & Schuller (to appear). Evidence of Emotion-Antecedent Appraisal Checks in Electroencephalography and Facial Electromyography. PloS One.</p> <p>Gentsch K, Grandjean D, Scherer KR. Temporal dynamics of event-related potentials related to goal conduciveness and power appraisals. Psychophysiology. 2013;50(10):1010–1022. </p>
PsPM-PCF2: PSR, SCR, ECG, respiration and startle-eyeblink EMG measurements in a delay fear conditioning task with 4 CS and different reinforcement rates
<p>This dataset includes eyetracker, skin conductance response (SCR), electrocardiogram (ECG), respiration, electromyogram (EMG), and auditory startle output (snd, as delivered by sound card) measurements. Also included are CS and US information, keypress responses and keypress response times for 19 healthy unmedicated participants (5 males and 14 females aged 24.68 +/- 3.65 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS were 4 coloured rectangles. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 500 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. In the last learning block, an auditory startle probe (ST) and no US was delivered 3.5 s after CS onset via headphones (100 dB, 50 ms duration with 2ms on- and offset ramp). The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p>
EMG elbow dataset
<p>This dataset contains the elbow's surface electromyography (sEMG) and kinematic (joint angle) data of ten subjects (6 males and 4 females) when performing different exercises of Flexion - Extension and Pronation - Supination. For the Flexion - Extension movements, the data was collected with the subject standing performing the movement with his arm next to the body; for the Pronation - Supination movements, the subject was seated in a chair with his forearm supported on a table. The EMG data was acquired using a Shimmer3 EMG device and the joint angle was estimated using the integrated IMUs in the Shimmer3 device. The following excercises were performed by each subject: Flexion - Extension without load, Flexion - Extension with 3lb dumbbel, Flexion - Extension with 5lb dumbbell, Pronation - Supination without load, Pronation - Supination with 3lb dumbbell and Pronation - Supination with 5lb dumbbell.</p> <p>The data is stored in a folder for each one of the subjects, each folder contains the following 13 files: one file named subject_info includes the subject ID, age, height, weight, arm length, hand width and gender. The remaining 12 files (2 for each exercise) are named with the following convention <id_movement_set_load[g]> (id: subject id, movement: excercised performed, set: training or test set, load: external load in grams).</p> <p>The data in each file is structured as follows:</p> <p>raw sEMG Channel 1 | raw sEMG Channel 2 | filtered sEMG Channel 1 | filtered sEMG Channel 2 | joint angle</p> <p>For Flexion - Extension: Channel 1 - Biceps Brachii and Channel 2 - Triceps Brachii.</p> <p>For Pronation - Supination: Channel 1 - Pronator Teres and Channel 2 - Biceps Brachii.</p> <p>The sEMG signal was filtered using a fourth order Chebyshev filter with cutoff frequencies of 10Hz and 500Hz, using a band ripple of 5%.</p>
EMG-EPN-612 Dataset
<p>EMG-EPN-612 Dataset</p> <p>This dataset, called EMG-EPN-612, contains EMG signals of 612 people for benchmarking of hand gesture recognition systems. This dataset has been created by the Artificial Intelligence and Computer Vision Research Lab from the Escuela Politécnica Nacional, Quito-Ecuador. The data was obtained by recording, with the Myo armband, EMG signals on the forearm while users were performing five hand gestures: wave-in, wave-out, pinch, open and fist. EMGs of the hand relaxed are also included. The dataset is divided into two groups of 306 people each. One group is for training or designing hand gesture recognition models and the other is intended for testing the classification and recognition accuracy of hand gesture recognition models. In each of these two groups, each person has 50 EMGs for each of the 5 gesture recorded and also 50 EMGs for the hand relaxed.</p> <p>More information about this dataset can be found at:</p> <p><a href="https://laboratorio-ia.epn.edu.ec/en/resources/dataset/2020_emg_dataset_612">https://laboratorio-ia.epn.edu.ec/en/resources/dataset/2020_emg_dataset_612</a></p> <p> </p> <p> </p>
EMG from Combination Gestures following Visual Prompts
<p>Dataset of surface EMG recordings from 10 subjects performing single and combination gestures, from Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder.</p> <p>Each subject contributes 1224 gesture examples (584 single gestures and 640 combination gestures).</p> <p>For more details and example usage, see the following:</p> <ul> <li>Paper pdf - <a href="https://arxiv.org/pdf/2311.14675.pdf">https://arxiv.org/pdf/2311.14675.pdf</a></li> <li>Experiment code - <a href="https://github.com/nik-sm/com-hom-emg">https://github.com/nik-sm/com-hom-emg</a></li> </ul> <h2>Contents</h2> <p>Dataset of single and combination gestures from 10 subjects. Each subject contributes 1224 gesture examples (584 single gestures and 640 combination gestures).</p> <h2>Data</h2> <p>Each gesture trial consists of 500 ms of activity from 8 recording electrodes around the mid forearm.</p> <p>Recording was performed at 1926 Hz with built-in 20–450 Hz bandpass filtering applied.</p> <h3>Data Collection Details</h3> <p>In each gesture trial, subjects began from a neutral position, with the arm on an armrest. Subjects followed visual cues to perform either a direction gesture, a modifier gesture, or both. The timing structure and number of gestures obtained from each visual prompt varied across experimental blocks.</p> <p>Data for a single gesture trial come from the central 500ms when that gesture is being performed (the window is centered to be as far from gesture onset and gesture end as possible).</p> <p>See the paper for more details.</p> <h2>Labels</h2> <p>Each gesture trial in the dataset has a two-part label, describing which gesture was performed.</p> <p>The first label component describes the direction gesture, and takes values in {0, 1, 2, 3, 4}, with the following meaning:</p> <ul> <li>0 - "Up" (wrist extension)</li> <li>1 - "Down" (wrist flexion)</li> <li>2 - "Left" (wrist abduction; movement towards thumb side)</li> <li>3 - "Right" (wrist adduction; movement towards pinky side)</li> <li>4 - "NoDirection" (absence of a direction gesture; none of the above)</li> </ul> <p>The second label component describes the modifier gesture, and takes values in {0, 1, 2, 3, 4}, with the following meaning:</p> <ul> <li>0 - "Pinch" (connecting thumb and index finger)</li> <li>1 - "Thumb" (touching thumb to first knuckle of index finger)</li> <li>2 - "Fist" (all fingers curled)</li> <li>3 - "Open" (all fingers extended)</li> <li>4 - "NoModifier" (absence of a modifier gesture; none of the above)</li> </ul> <p>Each subject provided 1224 total examples, consisting of:</p> <ul> <li>- 584 single gesture examples. (584 = 73 examples, for each of the 4+4 possible single gesture classes)</li> <li>- 640 combination gesture examples. (640 = 40 examples, for each of the 4x4 possible combination gesture classes)<br> </li> </ul> <h3>Examples of Label Structure</h3> <p>Single gestures have labels like (0, 4) indicating ("Up", "NoModifier") or (4, 3) indicating ("NoDirection", "Open").</p> <p>Combination gesture have labels like (0, 0) indicating ("Up", "Pinch") or (2, 3) indicating ("Left", "Open").</p> <h2>Loading data</h2> <p>After downloading and unzipping, the examples below can be used to load data in Python or MATLAB.</p> <p>Data have shape `(items, channels, timesteps)`.</p> <p>Labels have shape `(items, 2)`, where the first coordinate is the direction and the second coordinate is the modifier.<br> </p> <h3>Python example</h3> <blockquote> <p><em># Load data</em></p> <p>import numpy as np</p> <p>from pathlib import Path</p> <p> </p> <p>folder = Path("path/to/combination-gesture-dataset/python")<br> </p> <p>all_data, all_labels = [], []</p> <p>for i in range(10):</p> <p> data = np.load(f"{folder}/subj{i}/data.npy")</p> <p> labels = np.load(f"{folder}/subj{i}/labels.npy")</p> <p> all_data.append(data)</p> <p> all_labels.append(labels)</p> <p>all_data = np.concatenate(all_data, axis=0)</p> <p>all_labels = np.concatenate(all_labels, axis=0)</p> <p> </p> <p>print(all_data.shape) <em># (12240, 8, 962)</em></p> <p>print(all_labels.shape) <em># (12240, 2)</em></p> <p> </p> <p><em># Convert numeric labels to text</em></p> <p>def convert_label(label):</p> <p> directions = ["Up", "Down", "Left", "Right", "NoDirection"]</p> <p> modifiers = ["Pinch", "Thumb", "Fist", "Open", "NoModifier"]</p> <p> return directions[label[0]], modifiers[label[1]]</p> <p> </p> <p>print(convert_label(all_labels[0])) <em># ('NoDirection', 'Fist')</em></p> </blockquote> <p> </p> <h3>MATLAB example</h3> <blockquote> <p>% Load data</p> <p>folder = "path/to/combination-gesture-dataset/matlab";<br> </p> <p>all_data = [];</p> <p>all_labels = [];</p> <p>for i = 0:9</p> <p> data = load(sprintf("%s/subj%d/data.mat", folder, i));</p> <p> labels = load(sprintf("%s/subj%d/labels.mat", folder, i));</p> <p> all_data = [all_data; data.contents];</p> <p> all_labels = [all_labels; labels.contents];</p> <p>end</p> <p> </p> <p>% (Optional) convert labels from 0-indexed to 1-indexed, for easier conversion to text</p> <p>all_labels = all_labels + 1;</p> <p> </p> <p>size(all_data) % (12240, 8, 962)</p> <p>size(all_labels) % (12240, 2)<br> </p> <p>% Convert numeric labels to text</p> <p>directions = ["Up", "Down", "Left", "Right", "NoDirection"];</p> <p>modifiers = ["Pinch", "Thumb", "Fist", "Open", "NoModifier"];</p> <p>convert_label = @(label) [directions(label(1) + 1), modifiers(label(2) + 1)];</p> <p> </p> <p>convert_label(all_labels(1, :)) % "NoDirection" "Fist"</p> </blockquote>
EMG from Combination Gestures with Ground-truth Joystick Labels
<p>Dataset of surface EMG recordings from 11 subjects performing single and combination gestures, from "**A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition**" by Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, Stephanie Naufel, Tales Imbiriba, Eugene Tunik, Deniz Erdogmus, and Mathew Yarossi.</p> <p>For more details and example usage, see the following:</p> <ul> <li>Paper pdf - <a href="https://arxiv.org/pdf/2309.12217.pdf">https://arxiv.org/pdf/2309.12217.pdf</a></li> <li>Experiment code - <a href="https://github.com/neu-spiral/multi-label-emg">https://github.com/neu-spiral/multi-label-emg</a></li> </ul> <h1>Contents</h1> <p>Dataset of single and combination gestures from 11 subjects. <br>Subjects participated in 13 experimental blocks.<br>During each block, they followed visual prompts to perform gestures while also manipulating a joystick.<br>Surface EMG was recorded from 8 electrodes on the forearm; labels were recorded according to the current visual prompt and the current state of the joystick.</p> <p>Experiments included the following blocks:</p> <ul> <li>1 Calibration block</li> <li>6 Simultaneous-Pulse Combination blocks (3 without feedback, 3 with feedback)</li> <li>6 Hold-Pulse Combination blocks (3 without feedback, 3 with feedback)</li> </ul> <p>The contents of each block type were as follows:</p> <ul> <li>In the Calibration block, subjects performed 8 repetitions of each of the 4 direction gestures, 2 modifier gestures, and a resting pose.<br>Each Calibration trial provided 160 overlapping examples, for a total of: 8 repetitions x 7 gestures x 160 examples = 8960 examples.</li> <li>In Simultaneous-Pulse Combination blocks, subjects performed 8 trials of combination gestures, where both components were performed simultaneously.<br>Each Simultaneous-Pulse trial provided 240 overlapping examples, for a total of: 8 trials x 240 examples = 1920 examples.</li> <li>In Hold-Pulse Combination blocks, subjects performed 28 trials of combination gestures, where 1 gesture component was held while the other was pulsed.<br>Each Hold-Pulse trial provided 240 overlapping examples, for a total of: 28 trials x 240 examples = 6720 examples.</li> </ul> <p>A single data example (from any block) corresponds a window 250ms of EMG recorded at 1926Hz (built-in 20–450 Hz bandpass filtering applied).<br>A 50ms step size was used between each window; note that neighboring data examples are therefore overlapping.</p> <p>Feedback was provided as follows:</p> <ul> <li>In blocks with feedback, a model pre-trained on the Calibration data was used to give realtime visual feedback during the trial.</li> <li>In blocks without feedback, no model was used, and the visual prompt was the only source of information about the current gesture.</li> </ul> <p>For more details, see the paper.</p> <h1>Labels</h1> <p>Two types of labels are provided: </p> <ul> <li>joystick labels were recorded based on the position of the joystick, and are treated as ground-truth.</li> <li>visual labels were also recorded based on what prompt was currently being shown to the subject.</li> </ul> <p>For both joystick and visual labels, the following structure applies. Each gesture trial has a two-part label.</p> <p>The first label component describes the direction gesture, and takes values in {0, 1, 2, 3, 4}, with the following meaning:</p> <ul> <li>0 - "Up" (joystick pull)</li> <li>1 - "Down" (joystick push)</li> <li>2 - "Left" (joystick left)</li> <li>3 - "Right" (joystick right)</li> <li>4 - "NoDirection" (absence of a direction gesture; none of the above)</li> </ul> <p>The second label component describes the modifier gesture, and takes values in {0, 1, 2}, with the following meaning:</p> <ul> <li>0 - "Pinch" (joystick trigger button)</li> <li>1 - "Thumb" (joystick thumb button)</li> <li>2 - "NoModifier" (absence of a modifier gesture; none of the above)</li> </ul> <h2>Examples of Label Structure</h2> <p>Single gestures have labels like (0, 2) indicating ("Up", "NoModifier") or (4, 1) indicating ("NoDirection", "Thumb").</p> <p>Combination gesture have labels like (0, 0) indicating ("Up", "Pinch") or (2, 1) indicating ("Left", "Thumb").</p> <h1>File layout</h1> <p>Data are provided in Numpy and MATLAB format. Descriptions below apply for both.</p> <p>Each experimental block is provided in a separate folder.<br>Within one experimental block, the following files are provided:</p> <ul> <li>`data.npy` - Raw EMG data, with shape (items, channels, timesteps).</li> <li>`joystick_direction_labels.npy` - one-hot joystick direction labels, with shape (items, 5).</li> <li>`joystick_modifier_labels.npy` - one-hot joystick modifier labels, with shape (items, 3).</li> <li>`visual_direction_labels.npy` - one-hot visual direction labels, with shape (items, 5).</li> <li>`visual_modifier_labels.npy` - one-hot visual modifier labels, with shape (items, 3).</li> </ul> <h1>Loading data</h1> <p>For example code snippets for loading data, see the associated code repository.</p>
Surface EMG recordings of the biceps brachii muscle during voluntary isometric contractions - pilot study on fatigue
<p>This publication contains the data recorded during a study on muscle fatigue and its evaluation using surface electromyography (EMG). There were a total of 16 participants. Each directory, with an alphanumeric value contains data from a single participant (.mat and .json files). The README.pdf file in the root directory provides details on the experimental set-up and protocol, and data in each file. </p> <p>The EMG data are provided in matlab files and in addition to those, there are recordings of the <a href="https://doi.org/10.1016/j.jelekin.2019.05.012">perceived fatigue of the participants using a modified Borg CR-10 scale</a> in the json file, in which further participant and experimental information are provided.<br>For each participant information on gender, age (in aggregate form) and height (in aggregate form) are provided.</p> <p> </p>
A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living
<p>KIN-MUS UJI Dataset contains 572 recordings with anatomical angles and forearm muscle activity of 22 subjects while performing 26 representative activities of daily living. This dataset is, to our knowledge, the biggest currently available hand kinematics and muscle activity dataset to focus on goal-oriented actions. Data were recorded using a CyberGlove instrumented glove and surface EMG electrodes, both properly synchronised. Eighteen hand anatomical angles were obtained from the glove sensors by a validated calibration procedure. Surface EMG activity was recorded from seven representative forearm areas. The statistics verified that data were not affected by the experimental procedures and were similar to the data acquired under real-life conditions.</p> <p> </p> <p><strong>Data Sets</strong>:</p> <p>Data are presented as a Matlab data structure (<em>.mat</em> file). This structure contains all the recorded kinematic and muscle activity data classified as: ADL, phase (reaching, manipulation or release) and subject. The fields contained in the structure are those detailed in the following scheme:</p> <ul> <li>Subject: subject ID;</li> <li>ADL: ADL ID;</li> <li>Phase: phase of movement. 1 corresponds to reaching; 2 corresponds to manipulation; 3 corresponds to releasing.</li> <li>Time: Time stamp</li> <li>Angles (18 columns): Calibrated anatomical angles</li> <li>Muscle activity (7 columns): Normalised signal for the seven representative spot areas, according to [1].</li> </ul> <p>RAW_EMG struct provides the raw sEMG data, without any filter and not resampled, so that researchers may choose to condition the signals as they please<strong>.</strong> The fields contained in this structure are those detailed in the following scheme:</p> <ul> <li>Subject: subject ID;</li> <li>ADL: ADL ID, according to Table 1;</li> <li>Time: Time stamp; this field corresponds with the time stamp of the previous structure.</li> <li>Raw EMG data (7 columns): Raw sEMG data without any filter and not resampled, for the seven representative spot areas according to [1].</li> </ul> <p>[1] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. <em>J. NeuroEngineering Rehabil. </em>(2018).</p>
PsPM-SMD: SCR, EMG, ECG, and respiration measurement in response to auditory startle probes
<p>This dataset includes skin conductance response (SCR), orbicularis oculi electromyogram (EMG), electrocardiogram (ECG) and bellows-based respiration measurements as well as sound channel recordings for each of 19 healthy unmedicated participants (6 males and 13 females aged 24.9 +/- 4.1 years, age stated in Khemka et al. 2017 was based on incomplete information) in response to 25 startle probes, as described in Khemka et al. (2017). ITI was determined randomly on each trial between 7-11 s. For 25% of the participants, SCR/ECG/respiration was not recorded. One data file contains only responses to 24 startle probes.</p>
Multi-channel Surface EMG Dataset for Fatigue analysis
<p>This is the data used in paper "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG" DOI: 10.1109/NILES50944.2020.9257909</p> <p>Data can be found as a txt files for each subject separately or can be found as .mat file with all subjects included.</p> <p>Data details:</p> <ul> <li>Sampling Frequency= 200 Hz </li> <li>8-Bit resolution</li> <li>15 Healthy Subjects </li> <li>6 Kg Load with elbow flexed to a 90 angle</li> <li>120 Seconds Duration</li> <li>8 channels sEMG </li> <li>50 Hz Notch Filtered</li> </ul> <p>For more details and citation:</p> <p>A. Ebied, A. M. Awadallah, M. A. Abbass and Y. El-Sharkawy, "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG," 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 423-427, doi: 10.1109/NILES50944.2020.9257909.</p>
Arthrogenic Muscle Inhibition - decomposed EMG
<p>ACL-injured and healthy controls performed isometric contractions (10-50% maximal voluntary contractions) while EMG was being measured of the quadriceps and hamstrings. The EMG was decomposed to individual motor unit characteristics. </p>
EMG dataset for gesture recognition with arm translation
Open the record for dataset details and reuse information.
EMG and Video Dataset for sensor fusion based hand gestures recognition
<p>This dataset contains data for hand gesture recognition recorded with 3 different sensors. </p> <p>sEMG: recorded via the Myo armband that is composed of 8 equally spaced non-invasive sEMG sensors that can be placed approximately around the middle of the forearm. The sampling frequency of Myo is 200 Hz. The output of the Myo is a.u </p> <p>DVS: Dynamic Video Sensor which is a very low power event-based camera with 128x128 resolution</p> <p>DAVIS: Dynamic Video Sensor which is a very low power event-based camera with 240x180 resolution that also acquires APS frames.</p> <p>The dataset contains recordings of 21 subjects. Each subject performed 3 sessions, where each of the 5 hand gesture was recorded 5 times, each lasting for 2s. Between the gestures a relaxing phase of 1s is present where the muscles could go to the rest position, removing any residual muscular activation.</p> <p> </p> <p>Note: All the information for the DVS sensor has been extracted and can be found in the *.npy files. In case the raw data (.aedat) was needed please contact</p> <p> </p> <p>enea.ceolini@ini.uzh.ch</p> <p>elisa@ini.uzh.ch</p> <p>==== README ====</p> <p> </p> <p>DATASET STRUCTURE:</p> <p>EMG, DVS and APS recordings</p> <p>21 subjects</p> <p>3 sessions for each subject</p> <p>5 gestures in each session ('pinky', 'elle', 'yo', 'index', 'thumb')</p> <p> </p> <p>SINGLE DATASETS:</p> <p>- relax21_raw_emg.zip: contains raw sEMG and annotations (ground truth of gestures) in the format `subjectXX_sessionYY_ZZZ` with `XX` subject ID (01 to 21), `YY` session ID (01-03) and `ZZZ` that can be ‘emg’ or ‘ann’.</p> <p> </p> <p>- relax21_raw_dvs.zip: contains the full-frame dvs events in an array with dimensions 0 -> addr_x, 1 -> addr_y, 2 -> timestamp, 3 -> polarity. The timestamps are in seconds and synchronized with the Myo. Each file is in the format `subjectXX_sessionYY_dvs` with `XX` subject ID (01 to 21), `YY` session ID (01-03).</p> <p> </p> <p>- relax21_cropped_aps.zip: contains the 40x40 pixel aps frames for all subjects and trials in the format `subjectXX_sessionYY_Z_W_K` with `XX` subject ID (01 to 21), `YY` session ID (01-03), Z gesture ('pinky', 'elle', 'yo', 'index', 'thumb’), W trial ID (1-5), `K` frame index.</p> <p> </p> <p>- relax21_cropped_dvs_emg_spikes.pkl: spiking dataset that can be used to reproduce the results in the paper. The dataset is a dictionary with the following keys:</p> <ul> <li><strong>- </strong><strong>y</strong>: array of size 1xN with the class (0->4).</li> <li><strong>- </strong><strong>sub</strong>: array of size 1xN with the subject id (1->10).</li> <li><strong>- </strong><strong>sess</strong>: array of size 1xN with the session id (1->3).</li> <li><strong>- </strong><strong>dvs</strong>: list of length N, each object in the list is a 2d array of size 4xT_n where T_n is the number of events in the trial and the 4 dimensions rappresent: 0 -> addr_x, 1 -> addr_y, 2 -> timestamp, 3 -> polarity .</li> <li><strong>- </strong><strong>emg</strong>: list of length N, each object in the list is a 2d array of size 3xT_n where T_n is the number of events in the trial and the 3 dimensions rappresent: 0 -> addr, 1 -> timestamp, 3 -> polarity.</li> </ul> <p> </p> <p> </p>
Data from: Enhancing the security of pattern unlock with surface EMG-based biometrics
Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the F 1 score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock.
Silent Speech EMG
<p>Facial electromyography recordings during both silent and vocalized speech.</p> <p>This data is described in the publication "Digital Voicing of Silent Speech" at EMNLP 2020 (https://arxiv.org/abs/2010.02960).</p> <p>Code for processing this data can be found at https://github.com/dgaddy/silent_speech.</p>
EMG and joint angle data
<p>This dataset consists of lower limb EMG data and joint angle data of different healthy subjects for different walking speeds in the treadmill.</p> <p><em>Note: The EMG data is raw and needs to be high pass filtered, rectification and low pass filtered. Then normalization of the data can be done using <strong>MVC.mat </strong>file (maximum voluntary contraction) provided along with the other data. </em></p> <p><strong>Recorded Instruments:</strong></p> <p>EMG Data: Delsys and the placement of sensors are placed according to <a href="http://seniam.org/sensor_location.htm">SENIAM.</a></p> <p>Joint kinematics: Xsens.</p> <p><strong>Speeds:</strong></p> <p><strong>Constant: </strong>0.9 km/hr, 1.8 km/hr, 2.7 km/hr, 3.6 km/hr</p> <p><strong>Varying: </strong>0.9 km/hr (20 seconds) - 1.8 km/hr (20 seconds) - 2.7 km/hr (20 seconds) - 3.6 km/hr (20 seconds) - 2.7 km/hr (20 seconds) -1.8 km/hr (20 seconds) -0.9 km/hr (20 seconds)<strong> </strong></p>
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