weixinl/tl_time_sequence

Transfer learning on time series

Datasets

UCIHAR

Human Activity Recognition Using Smartphones Data Set
https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
30 subjects, 6 activities
The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.

For each record in the dataset it is provided:

• Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
• Triaxial Angular velocity from the gyroscope.
• A 561-feature vector with time and frequency domain variables.
• Its activity label.
• An identifier of the subject who carried out the experiment.

data: (10299, 1152) each record: 1152=91128 8 features, window size 128

EMG data for gestures Data Set

https://archive.ics.uci.edu/ml/datasets/EMG+data+for+gestures

EMG Pattern Database

For recording patterns, we used a MYO Thalmic bracelet worn on a user’s forearm, and a PC with a Bluetooth receiver. The bracelet is equipped with eight sensors equally spaced around the forearm that simultaneously acquire myographic signals. The signals are sent through a Bluetooth interface to a PC. We present raw EMG data for 36 subjects while they performed series of static hand gestures.The subject performs two series, each of which consists of six (seven) basic gestures. Each gesture was performed for 3 seconds with a pause of 3 seconds between gestures.

Description of raw_data _*** file Each file consist of 10 columns:

1. Time - time in ms; 2-9) Channel - eightEMG channels of MYO Thalmic bracelet;
2. Class –thelabel of gestures: 0 - unmarked data, 1 - hand at rest, 2 - hand clenched in a fist, 3 - wrist flexion, 4 – wrist extension, 5 – radial deviations, 6 - ulnar deviations, 7 - extended palm (the gesture was not performed by all subjects).

Relevant Paper: Lobov S., Krilova N., Kastalskiy I., Kazantsev V., Makarov V.A. Latent Factors Limiting the Performance of sEMG-Interfaces. Sensors. 2018;18(4):1122. doi: 10.3390/s18041122

Supported by the Ministry of Education and Science of the Russian Federation in the framework of megagrant allocation in accordance with the decree of the government of the Russian Federation №220, project № 14.Y26.31.0022

emg_normal

label num: 10 subject num: 4 each subject 200 items