An EMG and IMU Dataset for the Italian Sign Language Alphabet
Description
This data repository contains surface electromyography (EMG) and Inertial Measurement Unit (IMU) data collected with the Myo Gesture Control Armband about the gestures of the 26 letters of the Italian Sign Language Alphabet. The dataset contains 780 gesture samples (30 for each letter of the alphabet) and is organized into 26 directories, one for each letter of the alphabet. Each directory includes 30 json files, one for each sample of the gesture representing a letter. Each json file is named using a Global Unique Identifier (GUID), and include the following field: - timestamp, a string representing the date and time of the gesture acquisition. For example, the string “09/07/20/10:03:19” suggests that the gesture and its acquisition were performed the 9th of July 2020, at 10:03:19 a.m. - duration, an integer describing how long was the data acquisition of the gesture in milliseconds. The value is 2000 in all the json files, since the time window for the data acquisition was 2 seconds; - emg, an object representing the EMG data of the gesture. It has two fields - frequency, i.e. the sampling frequency (in Hz) of the values from the EMG sensors. This value is 200 in all the json files; - data, a 400 x 8 integer matrix. Each row is then an 8-dimensional array including the values from the 8 EMG sensors of the Myo Armband. Therefore, data is the time series of the values from the EMG sensors during the acquisition of the gesture; - imu, an object representing the IMU data of the gesture acquisition. It has two fields - frequency, i.e. the sampling frequency (in Hz) of the values from the IMU. This value is 200 in all the json files; - data, a 400 elements length object array. Each object has three fields, namely gyroscope (an array composed by 3 floating point values), acceleration (an array composed by 3 floating point values), and rotation (an array composed by 4 floating point values). The dataset can be used during the training and the testing of algorithms and techniques for automatic gesture recognition and, specifically, for automatic sign language interpretation.