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https://github.com/michidk/myo-dataset

This repository contains sEMG Data of 13 subjects recorded with the Myo Armband.
https://github.com/michidk/myo-dataset

data-set dataset gesture-detection gesture-recognition myo myo-armband rock-paper-scissors semg

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This repository contains sEMG Data of 13 subjects recorded with the Myo Armband.

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README 

        
# Myo Dataset



Myo dataset made for the reaseach presented in my paper about ["Person-Independent sEMG Gesture Recognition Using LSTM Networks for Human-Computer Interaction"](https://wiki.tum.de/display/infar/%5B19WS+-+GR%5D+Person-Independent+sEMG+Gesture+Recognition+Using+LSTM+Networks+for+Human-Computer+Interaction).



This repository contains sEMG Data of 13 subjects recorded with the Myo Armband. Subjects were instructed to wear the armband on their dominant arm, like this:



![image](.github/images/myo-position.png)



The gestures that were recorded, are the ones from the Rock, Paper, Scissors game:



![Rock](.github/images/rock.png)

![Paper](.github/images/paper.png)

![Scissors](.github/images/scissors.png)



## File Naming Convention



Each recording was given it's own folder.

The folder are named like this: `__` (also called the label)

Where arm `r` stands for the right arm.



## Recorded Data



Each kind of data is stored in it's own file per recording.

| File Ending | Description |

| - | - |

|`---emg.csv`|Raw EMG Data from the Myo armband of the eight sensors|

|`---pose.csv`|Pose data (detected poses by the built-in Myo detecion)|`s6_r_1-paper-4-pose.csv`|

|`---orientation.csv`|IMU Orientation data|

|`---meta.csv`|Meta Data (see below)|



## Meta Data



Each gesture recording comes with a meta data description file.



Example with explainatory comments:

```yaml

label: s6_r_1 # same as the folder

gesture: scissors # which gesture the user was told to record

index: 8 # 8th gesture of type scissors from this session



date: 21/08/2020

time: 05:49:30



arm: 0 # arm 0 = right, arm 1 = left

arm_direction: 1 # -1 if the armband was worn in reverse

rssi: -53 # signal strength

battery_level: 97 # 100 means full battery

mirror_left_arm: True # wether the recording applicatin automatically mirror the data if the user was left handed



files: # which data was recorded; e.g. pose data might miss, if no pose was detected

  - s6_r_1-scissors-8-pose.csv

  - s6_r_1-scissors-8-orientation.csv

  - s6_r_1-scissors-8-emg.csv



```



## Data Recording Procedure



In order to generate labeled data, a Python application

interfacing with the Myo was implemented. The data is recorded while

showing an icon and text label describing the gesture. Each gesture is

recorded for two seconds following a 0.5 second break where subjects

are supposed to relax their arm. The two-second time-span was chosen

because several tests showed that it is the amount of time required for

most persons to perform the transition from rest pose to the wanted

gesture and back. In one recording iteration, each gesture is recorded

ten times, which makes a total of 30 gestures per iteration. Therefore,

one iteration takes 75 seconds. Those values were chosen after initial

testing, which showed that subjects quickly get exhausted performing the

gestures. After a short break, without taking the armband off, a second

iteration was performed in the same session. This is then repeated once

more. Some participants were recorded a second time: After a long break,

the armband was put on differently, and the process described above was

repeated. In total, each participant contributed three (one session) or

six iterations (two sessions). One additional participant recorded 30

datasets in 10 sessions on different days to create a highly specialized

dataset.



Thirteen healthy subjects aged between 18 and 58 years participated in

the data acquisition process. Four of them were female, and nine

candidates male. One participant was left-handed. All subjects agreed to

the data recording and analysis by writing. Before starting the

recording session, participants filled out the consent form, which

states what is being recorded and how the data is used. The Myo armband

was then put onto the thickest part of the forearm, just below the

elbow on the dominant arm. Then the subject waited until the

connection icon on the armband stopped blinking, which indicated that

the armband warmed up. After performing the Myo sync gesture, which is

needed to detect the arm wearing the armband, the Myo is ready to use.

Subjects were told to start from a rest pose, performing the gesture and

reaching the highest intensity when the progress bar indicates that half

the time passed.



There are multiple reasons for always using the dominant arm during the

recording. First, a possible user interface using one Myo armband would

be controlled with the dominant arm because it feels more natural to the

user. But also not using the dominant arm could introduce irregularities

into the dataset because users are not used to performing similar

gestures with their non-dominant arm. Also, differences in the data

resulting from recording the left and right arm are non-existent since

the current arm is detected, and the data mirrored accordingly.



All data provided by the Myo armband is stored in multiple files labeled

with a unique but randomized identifier. Each file contains roughly

400 ± 5 entries since the recording took two seconds, and the data was

sampled with. Each entry contains an index, a timestamp, and one value

for each of the eight sensors. and other data along with

meta-information (label, recording time, left or right arm, battery

level, and connection quality) is stored in other files.