EMG Data Set for Machine Learning and Deep Learning Problems
Description
The Data consists of 3 parts. The dataset includes raw EMG data for 2 users. The users performed 5 hand gestures. i.e. Point, Middle Finger Extension, Closed Fist, Pinch, and Rest. The data is acquired using a Thalmic Labs Myo Armband, which has a sampling frequency of 200Hz. The participants were made to hold a gesture for 5s and relax for 3s. 6 gesture samples were acquired in one session. 12 such sessions were conducted for each gesture for each user. This gives us 72 samples for each gesture. Another data set is prepared that represents the Time Domain features extracted for each processing window The gesture windows were identified and Time domain features were extracted for each window. The feature vector for each window is arranged row-wise. Each row represents a processing window and each column in that row represents an extracted Time Domain feature. The Time Domain Features extracted are Integrated EMG (IEMG), Mean Absolute Value (MAV, MAV1 and MAV2), Simple Squared Integral (SSI), Variance (VAR), Root Mean Square (RMS), Waveform Length (WL), Average Amplitude Change (AAC), Absolute Standard Deviation Value Difference (DASDV), Myo Pulse Percentage (MYOP), Log Detector (LOG), Willison Amplitude (WAmp), Slope Sign Change (SSC) and Number of Zero Crossings (ZC) and Amplitude of First Burst (AFB). The last column represents the Gesture Class point(0), middle finger extension (1), closed-grip (2), pinch (3), rest (4). This data set can be used for gesture recognition problems.
Files
Steps to reproduce
1. Wear a Thalmic Labs Myo armband on the forearm. 2. Interface the Myo Armband with a laptop using a Bluetooth dongle 3. Record EMG Sessions - hold gesture for 5s and relax for 3 seconds. 4. In one session collect 6 samples and not more to avoid the effect of fatigue. 5. Repeat 12 sessions for one gesture. 6. Repeat all steps for any gesture.