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 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

• enea.ceolini@ini.uzh.ch
• elisa@ini.uzh.ch

==== README ====

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

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

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

DVS
DVS recordings only contain DVS events
- .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)
- .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

 
DAVIS
DAVIS recordings contain DVS events and APS frames.
- .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)
- .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.