EMG and Video Dataset for sensor fusion based hand gestures recognition

This dataset contains data for hand gestures recognition recorded with 3 different sensors.

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
DVS: Dynamic Video Sensor which is a very low power event based camera with 128x128 resolution
DAVIS: Dynamic Video Sensor which is a very low power event based camera with 240x180 resolution that also acquires APS frames.

The dataset<strong> </strong>contains recordings of 10 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.

Note: We did not upload the raw data (*.aedat) for the DAVIS being those files very heavy. All the information for the sensor has been extracted and can be found in the two files *.npz and *.mat. In case the raw data was needed please contact

[e-mail]
[e-mail]

==== README ====

DATASET STRUCTURE:<br>
EMG and DVS recordings<br>
10 subjects<br>
3 sessions for each subject<br>
5 gestures in each session ('pinky', 'elle', 'yo', 'index', 'thumb')

Data name:<br>
subjectXX_sessionYY_ZZZ<br>
XX : [01, 02, 03, 04, 05, 06, 07, 08, 09, 10] <br>
YY : [01, 02, 03]<br>
ZZZ : [emg, ann, dvs, davis]

Data format:<br>
emg: .npy<br>
ann: .npy<br>
dvs: .aedat,.npy<br>
davis: .mat,.npz

DVS<br>
DVS recordings only contain DVS events<br>
- .aedat (raw data): can be imported in Matlab using (https://github.com/inivation/AedatTools/tree/master/Matlab) or in Python with function aedat2numpy in converter.py (https://github.com/Enny1991/hand_gestures_cc19/tree/master/jAER_utils)<br>
- .npy (exported data): numpy.ndarray (xpos, ypos, ts, pol), 2D numpy array containing data of all events, timestamps ts reset to the trigger event (synchronized with the myo), timestamps ts in seconds

<br>
DAVIS<br>
DAVIS recordings contain DVS events and APS frames.<br>
- .mat (exported data): mat structure, name 'aedat', events are inside aedat.data.polarity (aedat.data.polarity.x,aedat.data.polarity.y,aedat.data.polarity.timeStamp,aedat.data.polarity.polarity), aps frames are inside aedat.data.frame.samples, timestamps are in aedat.data.frame.timeStampStart (start of frame collection) or aedat.data.frame.timeStampEnd (end of frame collection)<br>
- .npz (exported data): npz files: ['frames_time', 'dvs_events', 'frames'], 'dvs_events' is a numpy.ndarray (xpos, ypos, ts, pol), 2D numpy array containing data of all events, timestamps ts reset to the trigger event (synchronized with the myo), timestamps ts in seconds; 'frames' and 'frames_time' are aps data, 'frames' is a list of all the frames, reset at the triggered time, 'frames_time' is the time for each frame, we considered the start timeStamps for each frame.

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